Input: Confidence Estimation For Information Extraction Information extraction techniques automatically create structured databases from unstructured data sources, such as the Web or newswire documents. Despite the successes of these systems, accuracy will always be imperfect. For many reasons, it is highly desirable to accurately estimate the confidence the system has in the correctness of each extracted field. The information extraction system we evaluate is based on a linear-chain conditional random field (CRF), a probabilistic model which has performed well on information extraction tasks because of its ability to capture arbitrary, overlapping features of the input in a Markov model. We implement several techniques to estimate the confidence of both extracted fields and entire multi-field records, obtaining an average precision of 98% for retrieving correct fields and 87% for multi-field records.

Confidence

Estimation

Information Extraction

Information extraction

techniques

structured

databases

data

sources

documents

successes

systems

accuracy

reasons

confidence

system

correctness

extracted

field

information extraction system

extraction system

evaluate

based

linear-chain

conditional random field

field

probabilistic model

performed

extraction tasks

ability

features

input

Markov model

implement

techniques

confidence

extracted

fields

multi-field

records

precision

fields

multi-field

records

databases

sources

conditional random field

information extraction system

features

input

techniques

precision


Input: PRC Inc: Description Of The PAKTUS System Used For MUC-3 The PRC Adaptive Knowledge-based Text Understanding System (PAKTUS) has been under development as an Independent Research and Development project at PRC since 1984. The objective is a generic system of tools, including a core English lexicon, grammar, and concept representations, for building natural language processing (NLP) systems for text understanding. Systems built with PAKTUS are intended to generate input to knowledge based systems or data base systems. Input to the NLP system is typically derived from an existing electronic message stream, such as a news wire. PAKTUS supports the adaptation of the generic core to a variety of domains: JINTACCS messages, RAINFORM messages, news reports about a specific type of event, such as financial transfers or terrorist acts, etc., by acquiring sublanguage and domain-specific grammar, words, conceptual mappings, and discourse patterns. The long-term goal is a system that can support the processing of relatively long discourses in domains that are fairly broad with a high rate of success.

Description

System

Knowledge-based

Text

Understanding System

development

Research

Development

project

objective

system

tools

including

core

English

lexicon

concept

representations

building

natural language processing

systems

text

understanding

Systems

generate

input

knowledge based

systems

data

base

systems

Input

NLP system

message

stream

news

supports

adaptation

core

variety

domains

messages

messages

news

reports

type

event

transfers

domain-specific

words

mappings

discourse

patterns

long-term

goal

system

support

processing

discourses

domains

rate

success

Description

System

lexicon

system

Input

stream

systems

understanding

system

discourses


Input: Lexicon Design Using A Paradigmatic Approach The paper describes models for representation and methods to handle lexicographic structures supplied by the

Lexicon

Design

Approach

paper

models

representation

methods

structures

paper

models


Input: Practical Issues In Automatic Documentation Generation PLANDoc, a system under joint development by Columbia and Bellcore, documents the activity of planning engineers as they study telephone routes. It takes as input a trace of the engineer' s interaction with a network planning tool and produces 1-2 page summary. In this paper, we describe the user needs analysis we performed and how it influenced the development of PLANDoc. In particular, we show how it pinpointed the need for a sublanguage specification, allowing us to identify input messages and to characterize the different sentence paraphrases for realizing them. We focus on the systematic use of conjunction in combination with paraphrase that we developed for PLANDoc, which allows for the generation of summaries that are both concise-avoiding repetition of similar information, and fluent-avoiding repetition of similar phrasing.

Automatic

Documentation

Generation

system

development

documents

study

telephone

routes

input

interaction

network

tool

page

summary

paper

user

analysis

performed

influenced

development

specification

input

messages

sentence

focus

conjunction

combination

developed

generation

summaries

repetition

information

repetition

paper

analysis


Input: The Dragon Continuous Speech Recognition System: A Real-Time Implementation We present a 1000-word continuous speech recognition (CSR) system that operates in real time on a personal computer (PC). The system, designed for large vocabulary natural language tasks, makes use of phonetic Hidden Markov models (HMM) and incorporates acoustic, phonetic, and linguistic sources of knowledge to achieve high recognition performance. We describe the various components of this system. We also present our strategy for achieving real time recognition on the PC. Using a 486-based PC with a 29K-based add-on board, the recognizer has been timed at 1. 1 times real time.

Continuous Speech Recognition System

Time

Implementation

word

continuous speech recognition

system

real time

computer

system

designed

vocabulary

natural language

tasks

Hidden Markov models

sources

knowledge

recognition

performance

components

system

strategy

real time

recognition

based

based

timed

times

real time

system

tasks


Input: Preliminary ATIS Development At MIT " DARPA has recently initiated a plan for a common spoken language task, to be developed independently by all members of the DARPA community, with the hope that it will provide a mechanism leading to appropriate formal evaluation procedures at the level of spoken language. The task that was selected for this purpose is the Air Travel Information System (ATIS) task, based on selected tables from the Official Airline Guide (OAG). It was decided that the first evaluation would be limited in scope to deal with text input only, and to cover only sentences that could be understood unambiguously out of context. Data have been recorded over the past several months at Texas Instruments, using an interface that involves a " " wizard " " who fully interprets the meaning of the subject' s sentences, and generates database responses using a menu driven data access system. We have been actively engaged in the last few months in developing the natural language and back end portions of the MIT version of the ATIS domain. This paper describes our progress to date on this effort, including an evaluation of the performance of the system on the recently released designated DARPA test set. The remainder of this paper is organized as follows. First we will give a general description of the system we are developing, emphasizing those aspects that differ from the current general conception of the common task. Next we will describe in greater detail certain aspects of the back end, including knowledge representation, control strategy, the user interface, and our preliminary treatment of discourse history. This is followed by a section describing changes made in the parser, in the areas of semantics, the interface with the back-end, and a preliminary new-word treatment. This section also includes a brief discussion of some interesting phenomena that occurred in the training sentences. An evaluation section follows, discussing our system' s performance on both training and test data, as well as a preliminary assessment of the perplexity of the system. We conclude with a summary of our results and our position on the nature of the common task. "

Development

common

language

task

developed

community

provide

mechanism

evaluation

procedures

level

language

task

purpose

Information System

task

based

tables

evaluation

scope

deal

text

input

sentences

context

Data

recorded

interface

sentences

generates

database

menu

data

access

system

developing

natural language

portions

version

domain

paper

progress

date

effort

including

evaluation

performance

system

test set

remainder

paper

description

system

developing

aspects

current

common

task

detail

aspects

including

knowledge representation

control

strategy

user interface

treatment

discourse

section

parser

areas

semantics

interface

new-word

treatment

section

includes

discussion

phenomena

training

sentences

evaluation

section

system

performance

training

test

assessment

perplexity

system

summary

results

nature

common

task

evaluation

input

sentences

context

description

system

paper

progress

evaluation

performance

section

parser

section

discussion

section

performance

summary

results

phenomena

sentences


Input: Session 3: Machine Translation Semantic analysis to resolve lexical and syntactic ambiguities during parsing, and thus reduce translation errors very significantly. Unification grammars allowing syntactic and semantic constraints to be checked in a unified manner while parsing, and permitting reversible grammars i. e., the same grammars to be used for generation as well as for analysis. Advanced parsing methodologies, including augmented-LR compilation where knowledge sources (syntactic grammars, lexicons, and semantic ontologies) can be defined and maintained separately but are jointly compiled to apply simultaneously at run time, both in parsing and in generation. Natural language generation, focusing on how to structure fluent target-language output, an activity not truly investigated in the pre-ALPAC days. Automated corpus analysis tools, statistical and other means of extracting useful information from large bi-or multi-lingual corpora, including collocations, transfers, and contextual cues for disambiguation. MRDs = > MTDs, use of electronic machine-readable dictionaries (MRDs) to partially automate the creation of machine-tractable dictionaries (MTDs) in processable internal form for parsers and generators, permitting principled scaling up in MT configurations.

Machine Translation

Semantic analysis

analysis

lexical

syntactic

ambiguities

parsing

translation

errors

Unification

syntactic

semantic

constraints

checked

manner

parsing

generation

analysis

parsing

methodologies

including

compilation

knowledge sources

syntactic

lexicons

semantic

ontologies

apply

time

parsing

generation

Natural language generation

focusing

structure

target-language

output

corpus

analysis

tools

statistical

extracting

information

multi-lingual

corpora

including

collocations

transfers

cues

disambiguation

machine-readable

dictionaries

creation

machine-tractable

dictionaries

form

parsers

generators

scaling

configurations

Semantic analysis

ambiguities

dictionaries

dictionaries

compilation

methodologies

information

corpora


Input: Calculating The Probability Of A Partial Parse Of A Sentence A standard problem iu parsiug algorithms is the organization o [branched searches lo deal with ambiguous sentences. We discuss shift-reduce parsiug of stochastic context-free grammars and show how to construct a probabilistic score for ranking competing parse hypotheses. The score we use is the likelihood that the collection of subtrees can be completed into a full parse tree by means of the steps the parser is constrained lo follow.

Probability

Partial

Parse

Sentence

standard

problem

algorithms

organization

searches

deal

sentences

shift-reduce

context-free

construct

parse

hypotheses

collection

parse tree

steps

parser

Probability

Sentence


Input: Discourse Structure In The TRAINS Project In a natural dialog, a considerable proportion of the utterances actually relate to the maintenance of the dialog itself rather than to furthering the task or goals motivating the conversation. For example, many utterances serve to acknowledge, clarify, correct a previous utterance rather than pursue some goal in the domain. In addition, natural dialog is full of false starts, ungrammatical sentences and other complexities not found in in written language. This paper describes our recent efforts to define and construct a model of discourse interaction that handle dialogs that are rich in these natural dialog-related phenomena.

Discourse Structure

Project

natural

dialog

utterances

maintenance

dialog

task

goals

conversation

example

utterances

utterance

goal

domain

addition

natural

dialog

sentences

complexities

language

paper

efforts

construct

model

discourse

interaction

dialogs

natural

dialog-related

phenomena

paper

efforts

model

discourse

phenomena

dialogs


Input: Session 11: Prosody This paper provides a brief introduction to prosody research in the context of human-computer communication and an overview of the contributions of the papers in the session.

Prosody

paper

provides

introduction

prosody

research

context

human-computer

communication

overview

contributions

papers

paper

introduction


Input: Gisting Continuous Speech " The objective of this woik is automatic, real-time " " gisting " " of voice traffic for updating of information in databases, for producing timely reports, and for prompt notification of events of interest. Specifically, the goal is to build a prototype, real-time system capable of processing radio communication between air traffic controllers and pilots, identifying dialogs and extracting their " " gist " " (e. g., identifying flights, determining whether they are landing or taking off), and producing a continuous output stream with that information. The approach is intended to be general and applicable to other domains. The system is built upon state-of-the-art techniques in speech recognition, speaker identification, natural language analysis, and topic statistical classification. These techniques have been extended where necessary to address specific aspects of the gisting problem. Because various sources of information must be combined, the system design features a high degree of interaction between the natural language and domain-knowledge components and the speech processing components. "

Speech

objective

automatic

real-time

updating

information

databases

reports

events

goal

prototype

real-time

system

processing

communication

pilots

dialogs

extracting

output

stream

information

approach

domains

system

techniques

speech recognition

identification

natural language analysis

topic

statistical

classification

techniques

aspects

problem

sources

information

system

design

features

degree

interaction

natural language

domain-knowledge

components

speech processing

components

system

processing

techniques

system


Input: The Automatic Component Of The LINGSTAT Machine-Aided Translation System We present the newest implementation of the LINGSTAT machine-aided translation system. The most significant change from earlier versions is a new set of modules that produce a draft translation of the document for the user to refer to or modify. This paper describes these modules, with special emphasis on an automatically trained lexicalized grammar used in the parsing module. Some preliminary results from the January 1994 ARPA evaluation are reported.

Automatic

Component

Machine-

Translation System

System

implementation

machine-aided

translation system

versions

modules

translation

document

user

paper

modules

trained

parsing

module

results

evaluation

reported

Component

Translation System

modules

translation

paper

modules


Input: Contextual Spelling Correction Using Latent Semantic Analysis Contextual spelling errors are denned as the use of an incorrect, though valid, word in a particular sentence or context. Traditional spelling checkers flag misspelled words, but they do not typically attempt to identify words that are used incorrectly in a sentence. We explore the use of

Correction

Latent Semantic Analysis

Semantic Analysis

spelling

errors

word

sentence

context

spelling

words

words

sentence

Latent Semantic Analysis

Correction

word

sentence

words

sentence


Input: The Text REtrieval Conferences (TRECs)-Summary Results Of TREC-3 And TREC-4 " There have been four Text REtrieval Conferences (TRECs); TREC-1 in November 1992, TREC-2 in August 1993, TREC-3 in November 1994 and TREC-4 in November 1995. The number of participating systems has grown from 25 in TREC-1 to 36 in TREC-4, including most of the major text retrieval software companies and most of the universities doing research in text retrieval (see table for some of the participants). The diversity of the participating groups has ensured that TREC represents many different approaches to text retrieval, while the emphasis on individual experiments evaluated in a common setting has proven to be a major strength of TREC. The test design and test collection used for document detection in TIPSTER was also used in TREC. The participants ran the various tasks, sent results into NIST for evaluation, presented the results at the TREC conferences, and submitted papers for a proceedings. The test collection consists of over 1 million documents from diverse full-text sources, 250 topics, and the set of relevant documents or " " right answers " " to those topics. A Spanish collection has been built and used during TREC-3 and TREC-4, with a total of 50 topics. TREC-1 required significant system rebuilding by most groups due to the huge increase in the size of the document collection (from a traditional test collection of several megabytes in size to the 2 gigabyte TIPSTER collection). The results from TREC-2 showed significant improvements over the TREC-1 results, and should be viewed as the appropriate baseline representing state-of-the-art retrieval techniques as scaled up to handling a 2 gigabyte collection. TREC-3 therefore provided the first opportunity for more complex experimentation. The major experiments in TREC-3 included the development of automatic query expansion techniques, the use of passages or sub-documents to increase the precision of retrieval results, and the use of the training information to select only the best terms for routing queries. Some groups explored hybrid approaches (such as the use of the Rocchio methodology in systems not using a vector space model), and others tried approaches that were radically different from their original approaches. TREC-4 allowed a continuation of many of these complex experiments. The topics were made much shorter and this change triggered extensive investigations in automatic query expansion. There were also five new tasks, called tracks. These were added to help focus research on certain known problem areas, and included such issues as investigating searching as an interactive task by examining the process as well as the outcome, investigating techniques for merging results from the various TREC subcollections, examining the effects of corrupted data, and evaluating routing systems using a specific effectiveness measure. Additionally more groups participated in a track for Spanish retrieval. The TREC conferences have proven to be very successful, allowing broad participation in the overall DARPA TIPSTER effort, and causing widespread use of a very large test collection. All conferences have had very open, honest discussions of technical issues, and there have been large amounts of " " cross-fertilization " " of ideas. This will be a continuing effort, with a TREC-5 conference scheduled in November of 1996. "

Text REtrieval

Summary

Results

Text REtrieval

number

systems

including

text retrieval

software

universities

research

text retrieval

retrieval

table

participants

diversity

approaches

text retrieval

retrieval

individual

experiments

evaluated

common

strength

test

design

test

collection

document

detection

participants

tasks

results

evaluation

results

papers

proceedings

test

collection

documents

full-text

sources

topics

documents

topics

collection

topics

required

system

due

increase

size

document

collection

test

collection

size

collection

results

improvements

results

retrieval

techniques

scaled

collection

provided

complex

experimentation

experiments

included

development

automatic

query expansion

techniques

increase

precision

retrieval

results

training

information

terms

routing

queries

approaches

methodology

systems

vector space model

space

model

approaches

approaches

complex

experiments

topics

investigations

automatic

query expansion

expansion

tasks

called

help

focus

research

problem

areas

included

issues

searching

task

process

outcome

techniques

results

effects

evaluating

routing

systems

effectiveness

retrieval

participation

effort

test

collection

discussions

issues

amounts

cross-fertilization

effort

research

text retrieval

investigations

query expansion

approaches

text retrieval

collection

detection

vector space model

systems

techniques

results

documents

collection

topics

collection

techniques

experiments

size

collection

size

collection

approaches

approaches


Input: Acquiring Predicate-Argument Mapping Information From Multilingual Texts This paper discusses automatic acquisition of predicate-argument mapping information from multilingual texts. The lexicon of our NLP system abstracts the language-dependent portion of predicate-argument mapping information from the core meaning of verb senses (i. e.

Argument

Mapping

Information

Texts

paper

automatic

acquisition

predicate-argument

mapping

information

texts

lexicon

NLP system

system

abstracts

language-dependent

portion

predicate-argument

mapping

information

core

verb

Information

Texts

information

texts

lexicon

NLP system

paper

acquisition


Input: Constraints, Exceptions And Representations This paper shows that default-based phonologies have the potential to capture morphophonological generalisations which cannot be captured by non-default theories. In achieving this result, I offer a characterisation of Underspecification Theory and Optimality Theory in terms of their methods for ordering defaults. The result means that machine learning techniques for building declarative analyses may not provide an adequate basis for morphophonological analysis.

paper

default-based

phonologies

non-default

theories

result

Theory

Theory

terms

methods

ordering

defaults

result

machine

techniques

analyses

provide

basis

analysis

methods

defaults


Input: A Czech Morphological Lexicon In this paper, a treatment of Czech phonological rules in two-level morphology approach is described. First the possible phonological alternations in Czech are listed and then their treatment in a practical application of a Czech morphological lexicon.

Lexicon

paper

treatment

rules

two-level

morphology

approach

alternations

listed

treatment

practical application

lexicon

paper

approach


Input: Semantic Visualization This paper summarizes several initiatives at MITRE that are investigating the visualization of a range of content. We present results of our work in relevancy visualization, news visualization, world events visualization and sensor/battlefield visualization to enhance user interaction in information access and exploitation tasks. We summarize several initiatives we are currently pursuing and enumerate unsolved problems.

Semantic

Visualization

paper

initiatives

visualization

content

results

visualization

news

visualization

events

visualization

visualization

user

interaction

information access

exploitation

tasks

initiatives

problems

paper

initiatives


Input: Nominal Metonymy Processing Abstract. We argue for the necessity of resolution of metonymies for nominals (and other cases) in the context of semantics-based machine translation. By using an ontology as a search space, we are able to identify and resolve m tonymie expressions with significant accuracy, both for a pre-deterrnined inventory of metonymie types and for previously unseen cases. The entity replaced by the metonymy is made explicitly available in our meaning representation, to support translation, anaphora, and other mechanisms.

Processing

Abstract

necessity

resolution

cases

context

semantics-based

machine translation

ontology

search space

expressions

accuracy

inventory

types

cases

entity

representation

support

translation

mechanisms

types

inventory


Input: Towards Multimodal Spoken Language Corpora: TransTool And SyncTool This paper argues for the usefulness of multimodal spoken language corpora and specifies components of a platform for the creation, maintenance and exploitation of such corpora. Two of the components, which have already been implemented as prototypes, are described in more detail: TransTool and SyncTool. TransTool is a transcription editor meant to facilitate and partially automate the task of a human transcriber, while SyncTool is a tool for aligning the resulting transcriptions with a digitized audio and video recording in order to allow synchronized presentation of different representations (e. g., text, audio, video, acoustic analysis). Finally, a brief comparison is made between these tools and other programs developed for similar purposes.

Spoken Language

Corpora

paper

usefulness

language

corpora

components

platform

creation

maintenance

exploitation

corpora

components

implemented

prototypes

detail

transcription

task

tool

resulting

transcriptions

video

recording

order

presentation

representations

text

video

analysis

comparison

tools

programs

developed

purposes

paper

corpora

components

platform

tool

transcriptions

tools

programs


Input: Discovering Lexical Information By Tagging Arabic Newspaper Text In this paper we describe a system for building an Arabic lexicon automatically by tagging Arabic newspaper text. In this system we are using several techniques for tagging the words in the text and figuring out their types and their features. The major techniques that we are using are: finding phrases, analyzing the affixes of the words, and analyzing their patterns. Proper nouns are particularly difficult to identify in the Arabic language; we describe techniques for isolating them.

Lexical Information

Tagging

Newspaper

Text

paper

system

lexicon

tagging

newspaper

text

system

techniques

tagging

words

text

figuring

types

features

techniques

phrases

words

patterns

nouns

language

techniques

Tagging

Text

tagging

text

tagging

words

paper

system

techniques

system


Input: A Recognition-Based Meta-Scheme For Dialogue Acts Annotation The paper describes a new formal framework for comparison, design and standardization of annotation schemes for dialogue acts. The framework takes a recognition-based approach to dialogue tagging and defines four independent taxonomies of tags, one for each orthogonal dimension of linguistic and contextual analysis assumed to have a bearing on identification of illocutionary acts. The advantages and limitations of this proposal over other previous attempts are discussed and concretely exemplified.

Recognition-

Based

Scheme

Dialogue

paper

framework

comparison

design

schemes

dialogue acts

framework

recognition-based

recognition-based approach

approach

dialogue

tagging

taxonomies

tags

dimension

analysis

identification

advantages

limitations

proposal

paper

framework

recognition-based approach

dialogue


Input: Multi-Document Summarization By Visualizing Topical Content Mani, Inderjeet; House, David; Klein, Gary; Hirschman, Lynette; Firmin Hand, Therese; Sundheim, Beth M., The TIPSTER SUMMAC Text Summarization Evaluation, Conference Of The European Association For Computation al Linguistics, 1999 * * * Nagao, Katashi; Hasida, Koiti, Automatic Text Summarization Based on the Global Document Annotation, COLING-ACL, 1998 * * * Power, Richard; Scott, Donia R., Multilingual Authoring using Feedback Texts, COLING-ACL, 1998 * * * Mani, Inderjeet; Gates, Barbara; Bloedorn, Eric, Improving Summaries By Revising Them, Annual Meeting Of The Association For Computation al Linguistics, 1999

Document Summarization

Content

Hand

Text Summarization

Evaluation

Association

Computation

Linguistics

Automatic

Text Summarization

Based

Document

Feedback

Texts

Association

Computation

Linguistics

Document

Automatic


Input: A Common Theory Of Information Fusion From Multiple Text Sources Step One: Cross-Document Structure We introduce CST (cross-document structure theory), a paradigm for multi-document analysis. CST takes into account the rhetorical structure of clusters of related textual documents. We present a taxonomy of cross-document relationships. We argue that CST can be the basis for multi-document summarization guided by user preferences for summary length, information provenance, cross-source agreement, and chronological ordering of facts.

Common

Theory

Information

Fusion

Text

Sources

Step

Cross-

Document

Structure

cross-document

structure

theory

paradigm

multi-document

analysis

structure

clusters

documents

taxonomy

cross-document

relationships

basis

multi-document summarization

summarization

user

preferences

summary

length

information

cross-source

agreement

ordering

paradigm

analysis

preferences

multi-document summarization

clusters

documents

taxonomy

relationships


Input: Error-Driven HMM-Based Chunk Tagger With Context-Dependent Lexicon This paper proposes a new error-driven HMM-based text chunk tagger with context-dependent lexicon. Compared with standard HMM-based tagger, this tagger uses a new Hidden Markov Modelling approach which incorporates more contextual information into a lexical entry. Moreover, an error-driven learning approach is adopted to decrease the memory requirement by keeping only positive lexical entries and makes it possible to further incorporate more context-dependent lexical entries. Experiments show that this technique achieves overall precision and recall rates of 93. 40% and 93. 95% for all chunk types, 93. 60% and 94. 64% for noun phrases, and 94. 64% and 94. 75% for verb phrases when trained on PENN WSJ TreeBank section 00-19 and tested on section 20-24, while 25-fold validation experiments of PENN WSJ TreeBank show overall precision and recall rates of 96. 40% and 96. 47% for all chunk types, 96. 49% and 96. 99% for noun phrases, and 97. 13% and 97. 36% for verb phrases.

Error-

Chunk

Context-

Lexicon

paper

proposes

error-driven

text

chunk

context-dependent

lexicon

standard

approach

contextual information

information

lexical entry

entry

error-driven

learning approach

approach

memory

requirement

lexical entries

context-dependent

lexical entries

Experiments

technique

precision

recall

rates

chunk

types

noun phrases

verb

phrases

trained

section

tested

section

validation

experiments

precision

recall

rates

chunk

types

noun phrases

verb

phrases

contextual information

lexical entry

learning approach

memory

technique

rates

experiments

rates


Input: One Sense Per Collocation And Genre/Topic Variations This paper revisits the one sense per collocation hypothesis using fine-grained sense distinctions and two different corpora. We show that the hypothesis is weaker for fine-grained sense distinctions (70% vs. 99% reported earlier on 2-way ambiguities). We also show that one sense per collocation does hold across corpora, but that collocations vary from one corpus to the other, following genre and topic variations. This explains the low results when performing word sense disambiguation across corpora. In fact, we demonstrate that when two independent corpora share a related genre/topic, the word sense disambiguation results would be better. Future work on word sense disambiguation will have to take into account genre and topic as important parameters on their models.

Sense

Collocation

Genre

Topic

paper

sense

collocation

hypothesis

sense

distinctions

corpora

hypothesis

sense

distinctions

reported

ambiguities

sense

collocation

corpora

collocations

corpus

genre

topic

variations

results

performing

word sense disambiguation

corpora

corpora

genre

topic

word sense disambiguation

results

word sense disambiguation

genre

topic

parameters

models

paper

hypothesis

genre

collocations


Input: Multidimensional Transformation-Based Learning This paper presents a novel method that allows a machine learning algorithm following the transformation-based learning paradigm (Brill, 1995) to be applied to multiple classification tasks by training jointly and simultaneously on all fields. The motivation for constructing such a system stems from the observation that many tasks in natural language processing are naturally composed of multiple subtasks which need to be resolved simultaneously; also tasks usually learned in isolation can possibly benefit from being learned in a joint framework, as the signals for the extra tasks usually constitute inductive bias. The proposed algorithm is evaluated in two experiments: in one, the system is used to jointly predict the part-of-speech and text chunks/baseNP chunks of an English corpus; and in the second it is used to learn the joint prediction of word segment boundaries and part-of-speech tagging for Chinese. The results show that the simultaneous learning of multiple tasks does achieve an improvement in each task upon training the same tasks sequentially. The part-of-speech tagging result of 96. 63% is state-of-the-art for individual systems on the particular train/test split.

Transformation-

Based

paper

method

machine

algorithm

transformation-based

learning

paradigm

applied

classification tasks

training

fields

motivation

constructing

system

stems

observation

tasks

natural language processing

tasks

isolation

benefit

framework

signals

tasks

bias

proposed

algorithm

evaluated

experiments

system

part-of-speech

text

chunks

chunks

English

corpus

prediction

word

segment

boundaries

part-of-speech

tagging

Chinese

results

learning

tasks

improvement

task

training

tasks

part-of-speech

tagging

result

individual

systems

train

test

paper

method

paradigm

algorithm

system

corpus

tagging

Chinese


Input: Contrast And Variability In Gene Names We studied contrast and variability in a corpus of gene names to identify potential heuristics for use in performing entity identification in the molecular biology domain. Based on our findings, we developed heuristics for mapping weakly matching gene names to their official gene names. We then tested these heuristics against a large body of Medline abstracts, and found that using these heuristics can increase recall, with varying levels of precision. Our findings also underscored the importance of good information retrieval and of the ability to disambiguate between genes, proteins, RNA, and a variety of other referents for performing entity identification with high precision.

Contrast

Variability

Gene

studied

contrast

variability

corpus

gene

names

performing

entity

identification

biology

domain

Based

findings

developed

mapping

matching

gene

names

gene

names

tested

abstracts

increase

recall

levels

precision

findings

importance

information retrieval

ability

disambiguate

genes

proteins

variety

referents

performing

entity

identification

precision

names

corpus


Input: XML-Based NLP Tools For Analysing And Annotating Medical Language We describe the use of a suite of highly flexible xml-based nlp tools in a project for processing and interpreting text in the medical domain. The main aim of the paper is to demonstrate the central role that xml mark-up and xml nlp tools have played in the analysis process and to describe the resultant annotated corpus of medline abstracts. In addition to the xml tools, we have succeeded in integrating a variety of non-xml' off the shelf' nlp tools into our pipelines, so that their output is added into the mark-up. We demonstrate the utility of the annotations that result in two ways. First, we investigate how they can be used to improve parse coverage ofa hand-crafted grammar that generates logical forms. And second, we investigate how they contribute to automatic lexical semantic acquisition processes.

Language

suite

tools

project

processing

text

domain

main

paper

role

tools

analysis

process

corpus

abstracts

addition

tools

variety

tools

pipelines

output

utility

result

improve

parse

coverage

hand-crafted

generates

logical forms

automatic

lexical

semantic

acquisition

processes

processing

text

paper

tools

abstracts

corpus

tools

pipelines


Input: Reducing Parameter Space For Word Alignment This paper presents the experimental results of our attemps to reduce the size of the parameter space in word alignment algorithm. We use IBM Model 4 as a baseline. In order to reduce the parameter space, we pre-processed the training corpus using a word lemmatizer and a bilingual term extraction algorithm. Using these additional components, we obtained an improvement in the alignment error rate.

Parameter

Space

Word Alignment

paper

experimental

results

size

parameter

space

word alignment

algorithm

IBM Model

order

parameter

space

training corpus

corpus

word

term

extraction

algorithm

components

improvement

alignment

error rate

rate

paper

results

algorithm

training corpus


Input: Extracting And Evaluating General World Knowledge From The Brown Corpus " We have been developing techniques for extracting general world knowledge from miscellaneous texts by a process of approximate interpretation and abstraction, focusing initially on the Brown corpus. We apply interpretive rules to clausal patterns and patterns of modification, and concurrently abstract general " " possi-bilistic " " propositions from the resulting formulas. Two examples are " " A person may believe a proposition " ", and " " Children may live with relatives " ". Our methods currently yield over 117, 000 such propositions (of variable quality) for the Brown corpus (more than 2 per sentence). We report here on our efforts to evaluate these results with a judging scheme aimed at determining how many ofthese propositions pass muster as " " reasonable general claims " " about the world in the opinion of humanjudges. We find that nearly 60% of the extracted propositions are favorably judged according to our scheme by any given judge. The percentage unanimously judged to be reasonable claims by multiple judges is lower, but still sufficiently high to suggest that our techniques may be of some use in tackling the long-standing " " knowledge acquisition bottleneck " " in AI. "

Extracting

Evaluating

World Knowledge

Knowledge

Brown Corpus

Corpus

developing

techniques

extracting

world knowledge

texts

process

interpretation

abstraction

focusing

Brown corpus

apply

rules

patterns

patterns

modification

abstract

resulting

formulas

examples

relatives

methods

yield

variable

quality

Brown corpus

corpus

sentence

report

efforts

evaluate

results

judging

scheme

claims

opinion

extracted

judged

scheme

judge

percentage

judged

claims

judges

techniques

knowledge

acquisition

World Knowledge

Brown Corpus

extracting

texts

rules

patterns

methods

Brown corpus


Input: How To Get A Chinese Name (Entity): Segmentation And Combination Issues When building a Chinese named entity recognition system, one must deal with certain language-specific issues such as whether the model should be based on characters or words. While there is no unique answer to this question, we discuss in detail advantages and disadvantages of each model, identify problems in segmentation and suggest possible solutions, presenting our observations, analysis, and experimental results. The second topic of this paper is classifier combination. We present and describe four classifiers for Chinese named entity recognition and describe various methods for combining their outputs. The results demonstrate that classifier combination is an effective technique of improving system performance: experiments over a large annotated corpus of fine-grained entity types exhibit a 10% relative reduction in F-measure error.

Chinese

Name

Entity

Combination

Chinese

named

entity

recognition system

deal

language-specific

issues

model

based

words

question

detail

advantages

model

problems

solutions

observations

analysis

experimental

results

topic

paper

classifier

combination

classifiers

Chinese

named

entity

recognition

methods

outputs

results

classifier

combination

technique

improving

system

performance

experiments

corpus

entity types

relative

reduction

error

paper

combination

classifiers

recognition

combination

improving

experiments

reduction


Input: Improving Summarization Performance By Sentence Compression-A Pilot Study In this paper we study the effectiveness of applying sentence compression on an extraction based multi-document summarization system. Our results show that pure syntactic-based compression does not improve system performance. Topic signature-based reranking of compressed sentences does not help much either. However reranking using an oracle showed a significant improvement remains possible. Keywords: Text Summarization, Sentence Extraction, Sentence Compression, Evaluation.

Summarization

Performance

Sentence

Compression

Pilot

Study

paper

study

effectiveness

applying

sentence

compression

extraction

based

summarization system

results

syntactic-based

compression

improve

system

performance

Topic

sentences

help

oracle

improvement

Text Summarization

Sentence

Extraction

Sentence

Compression

Evaluation

paper

compression

compression

performance


Input: Noun Phrase Coreference As Clustering This paper introduces a new, unsupervised algorithm for noun phrase coreference resolution. It differs from existing methods in that it views coreference resolution as a clustering task. In an evaluation on the MUC-6 coreference resolution corpus, the algorithm achieves an F-measure of 53. 6%, placing it firmly between the worst (40%) and best (65%) systems in the MUC-6 evaluation. More importantly, the clustering approach outperforms the only MUC-6 system to treat coreference resolution as a learning problem. The clustering algorithm appears to provide a flexible mechanism for coordinating the application of context-independent and context-dependent constraints and preferences for accurate partitioning of noun phrases into coreference equivalence classes.

Noun Phrase

paper

algorithm

noun phrase

coreference resolution

resolution

methods

coreference resolution

clustering

task

evaluation

coreference resolution

corpus

algorithm

systems

evaluation

clustering

approach

system

coreference resolution

problem

clustering

algorithm

provide

mechanism

application

context-independent

context-dependent

constraints

preferences

partitioning

noun phrases

equivalence classes

algorithm

coreference resolution

evaluation

corpus

preferences

partitioning

approach

system


Input: Psychocomputational Linguistics: A Gateway to the Computational Linguistics Curriculum Computational modeling of human language processes is a small but growing subfield of computational linguistics. This paper describes a course that makes use of recent research in psychocomputational modeling as a framework to introduce a number of mainstream computational linguistics concepts to an audience of linguistics, cognitive science and computer science doctoral students. The emphasis on what I take to be the largely interdisciplinary nature of computational linguistics is particularly germane for the computer science students. Since 2002 the course has been taught three times under the auspices of the MA/PhD program in Linguistics at The City University of New York' s Graduate Center. A brief description of some of the students' experiences after having taken the course is also provided.

Linguistics

Computational Linguistics

Computational

modeling

human language

processes

computational linguistics

linguistics

paper

research

modeling

framework

number

mainstream

computational linguistics

concepts

linguistics

cognitive science

computer science

nature

computational linguistics

computer science

times

program

Linguistics

University

Graduate

Center

description

experiences

provided

modeling

computational linguistics


Input: A Plan-Based Analysis Of Indirect Speech Act We propose an account of indirect forms of speech acts to request and inform based on the hypothesis that language users can recognize actions being performed by others, infer goals being sought, and cooperate in their achievement. This cooperative behaviour is independently motivated and may or may not be intended by speakers. If the hearer believes it is intended, he or she can recognize the speech act as indirect; otherwise it is interpreted directly. Heuristics are suggested to decide among the interpretations.

Based

Analysis

Speech Act

propose

forms

speech acts

based

hypothesis

language

users

actions

performed

goals

behaviour

speech act

interpretations

Analysis

Speech Act


Input: Ambiguous Noun Phrases In Logical Form " Schubert, Lenhart K.; Pelletier, Francis Jeffry, From English To Logic: Context-Free Computation Of " " Conventional " " Logical Translation, American Journal Of Computation al Linguistics, 1982 * * * Hobbs, Jerry R., An Improper Treatment Of Quantification In Ordinary English, Annual Meeting Of The Association For Computation al Linguistics, 1983 * * * Pollack, Martha E.; Pereira, Fernando, An Integrated Framework For Semantic And Pragmatic Interpretation, Annual Meeting Of The Association For Computation al Linguistics, 1988 * * * Alshawi, Hiyan; Van Eijck, Jan, Logical Forms In The Core Language Engine, Annual Meeting Of The Association For Computation al Linguistics, 1989 "

Noun

Logical Form

English

Logic

Context-

Computation

Translation

Journal

Computation

Linguistics

Treatment

Quantification

English

Association

Computation

Linguistics

Framework

Semantic

Interpretation

Association

Computation

Linguistics

Van

Core

Language

Engine

Association

Computation

Linguistics

Framework

Interpretation


Input: Extracting The Lowest-Frequency Words: Pitfalls And Possibilities Church, Kenneth Ward; Hanks, Patrick, Word Association Norms, Mutual Information, And Lexicography, Computation al Linguistics, 1990 * * * Dunning, Ted E., Accurate Methods For The Statistics Of Surprise And Coincidence, Computation al Linguistics, 1993 * * * Smadja, Frank A., Retrieving Collocations From Text: Xtract, Computation al Linguistics, 1993

Extracting

Frequency

Words

Pitfalls

Word

Association

Norms

Mutual Information

Computation

Linguistics

Methods

Statistics

Computation

Linguistics

Text

Computation

Linguistics

Extracting

Words


Input: Word Sense Disambiguation using Optimised Combinations of Knowledge Sources Word sense disambiguation algorithms, with few exceptions, have made use of only one lexical knowledge source. We describe a system which performs word sense disambiguation on all content words in free text by combining different knowledge sources: semantic preferences, dictionary definitions and subject/domain codes along with part-of-speech tags, optimised by means of a learning algorithm. We also describe the creation of a new sense tagged corpus by combining existing resources. Tested accuracy of our approach on this corpus exceeds 92%, demonstrating the viability of all-word disambiguation rather than restricting oneself to a small sample.

Word Sense Disambiguation

Sense Disambiguation

Knowledge Sources

Word sense disambiguation

algorithms

exceptions

knowledge source

source

system

performs

word sense disambiguation

content words

words

text

knowledge sources

semantic

preferences

dictionary definitions

domain

codes

part-of-speech

tags

algorithm

creation

sense

tagged

corpus

resources

accuracy

approach

corpus

all-word

disambiguation

sample

Knowledge Sources

Word Sense Disambiguation

knowledge source

algorithms

system

content words

approach

accuracy


Input: GRAPH: The Costs of Redundancy in Referring Expressions We describe a graph-based generation system that participated in the Tuna attribute selection and realisation task of the reg 2008 Challenge. Using a stochastic cost function (with certain properties for free), and trying attributes from cheapest to more expensive, the system achieves overall. 76 dice and. 54 masi scores for attribute selection on the development set. For realisation, it turns out that in some cases higher attribute selection accuracy leads to larger differences between system-generated and human descriptions.

Costs

Redundancy

generation system

system

selection

realisation

task

Challenge

cost

function

properties

system

selection

development

realisation

cases

selection

accuracy

differences

system-generated

descriptions

generation system

task

selection

differences


Input: Colouring Summaries BLEU In this paper we attempt to apply the IBM algorithm, BLEU, to the output of four different summarizers in order to perform an intrinsic evaluation of their output. The objective of this experiment is to explore whether a metric, originally developed for the evaluation of machine translation output, could be used for assessing another type of output reliably. Changing the type of text to be evaluated by BLEU into automatically generated extracts and setting the conditions and parameters of the evaluation experiment according to the idiosyncrasies of the task, we put the feasibility of porting BLEU in different Natural Language Processing research areas under test. Furthermore, some important conclusions relevant to the resources needed for evaluating summaries have come up as a side-effect of running the whole experiment.

paper

apply

algorithm

output

order

perform

evaluation

output

objective

experiment

metric

developed

evaluation

machine translation

translation

output

type

output

type

text

evaluated

generated

extracts

parameters

evaluation

experiment

task

porting

Natural Language Processing

research

areas

test

conclusions

resources

evaluating

summaries

side-effect

experiment

algorithm

output

evaluation

output

evaluation

machine translation


Input: Automatic Prosodic Labeling with Conditional Random Fields and Rich Acoustic Features Many acoustic approaches to prosodic labeling in English have employed only local classifiers, although text-based classification has employed some sequential models. In this paper we employ linear chain and factorial conditional random fields (CRFs) in conjunction with rich, contextually-based prosodic features, to exploit sequential dependencies and to facilitate integration with lexical features. Integration of lexical and prosodic features improves pitch accent prediction over either feature set alone, and for lower accuracy feature sets, factorial CRF models can improve over linear chain based prediction of pitch accent.

Automatic

Features

approaches

English

classifiers

text-based

classification

models

paper

chain

conditional random fields

fields

conjunction

features

dependencies

integration

lexical features

Integration

lexical

features

improves

pitch

prediction

feature set

accuracy

feature sets

CRF models

models

improve

chain

based

prediction

pitch

models

classification

paper

conditional random fields

Integration

prediction

CRF models

prediction


Input: WSD Based On Mutual Information And Syntactic Patterns This paper describes a hybrid system for WSD, presented to the English all-words and lexical-sample tasks, that relies on two different unsupervised approaches. The first one selects the senses according to mutual information proximity between a context word a variant of the sense. The second heuristic analyzes the examples of use in the glosses of the senses so that simple syntactic patterns are inferred. This patterns are matched against the disambiguation contexts. We show that the first heuristic obtains a precision and recall of. 58 and. 35 respectively in the all words task while the second obtains. 80 and. 25. The high precision obtained recommends deeper research of the techniques. Results for the lexical sample task are also provided.

Based

Mutual Information

Syntactic Patterns

paper

system

English

all-words

lexical-sample

tasks

approaches

mutual information

proximity

context word

variant

sense

examples

simple

syntactic patterns

patterns

matched

disambiguation

contexts

precision

recall

words

task

precision

research

techniques

Results

lexical

sample

task

provided

paper

system

patterns

contexts


Input: Joining Forces To Resolve Lexical Ambiguity: East Meets West In Barcelona " This paper describes the component models and combination model built as a joint effort between Swarthmore College, Hong Kong PolyU, and HKUST. Though other models described elsewhere contributed to the final combination model, this paper focuses solely on the joint contributions to the " " Swat-HK " " effort. "

Lexical

Ambiguity

paper

component

models

combination

model

effort

Hong

models

combination

model

paper

focuses

contributions

effort

paper

models

paper

contributions


Input: Semantic Forensics: An Application Of Ontological Semantics To Information Assurance " The paper deals with the latest application of natural language processing (NLP), specifically of ontological semantics (ONSE) to natural language information assurance and security (NL IAS). It demonstrates how the existing ideas, methods, and resources of ontological semantics can be applied to detect deception in NL text (and, eventually, in data and other media as well). After stating the problem, the paper proceeds to a brief introduction to ONSE, followed by an equally brief survey of our 5-year-old effort in " " colonizing " " IAS. The main part of the paper deals with the following issues: human deception detection abilities and NLP modeling of it; manipulation of fact repositories for this purpose beyond the current state of the art; acquisition of scripts for complex ontological concepts; degrees of lying complexity and feasibility of their automatic detection. This is not a report on a system implementation but rather an application-establishing proof-of-concept effort based on the algorithmic and machine-tractable recombination and extension of the previously implemented ONSE modules. The strength of the approach is that it emphasizes the use of the existing NLP applications, with very few domain-and goal-specific adjustments, in a most promising and growing new area of IAS. So, while clearly dealing with a new application, the paper addresses theoretical and methodological extensions of ONSE, as defined currently, that will be useful for other applications as well. 1 "

Semantic

Forensics

Application

Semantics

Information

paper

deals

application

natural language processing

semantics

natural language

information

methods

resources

semantics

applied

text

data

problem

paper

proceeds

introduction

survey

effort

main

part

paper

deals

following

issues

detection

abilities

modeling

manipulation

repositories

purpose

current

acquisition

scripts

complex

concepts

degrees

complexity

automatic

detection

report

system

implementation

application-establishing

proof-of-concept

effort

based

machine-tractable

extension

implemented

modules

strength

approach

NLP applications

domain-

goal-specific

area

dealing

application

paper

extensions

applications

paper

semantics

paper

introduction

paper

extensions

resources

text


Input: Exploiting Context For Biomedical Entity Recognition: From Syntax To The Web We describe a machine learning system for the recognition of names in biomedical texts. The system makes extensive use of local and syntactic features within the text, as well as external resources including the web and gazetteers. It achieves an F-score of 70% on the Coling 2004 NLPBA/BioNLP shared task of identifying five biomedical named entities in the GENIA corpus.

Context

Entity

Recognition

Syntax

machine

system

recognition

names

texts

system

syntactic features

features

text

resources

including

gazetteers

task

named

entities

corpus

Context

Recognition

system

recognition

syntactic features

system

names

texts

entities

corpus


Input: Biomedical Named Entity Recognition Using Conditional Random Fields And Rich Feature Sets As the wealth of biomedical knowledge in the form of literature increases, there is a rising need for effective natural language processing tools to assist in organizing, curating, and retrieving this information. To that end, named entity recognition (the task of identifying words and phrases in free text that belong to certain classes of interest) is an important first step for many of these larger information management goals. In recent years, much attention has been focused on the problem of recognizing gene and protein mentions in biomedical abstracts. This paper presents a framework for simultaneously recognizing occurrences of PROTEIN, DNA, RNA, CELL-LINE, CELL-TYPE F1

Named

Entity

Recognition

Feature

knowledge

form

literature

increases

natural language processing

tools

information

named

entity

recognition

task

words

phrases

text

classes

first step

information

management

goals

focused

problem

gene

protein

mentions

abstracts

paper

framework

occurrences

PROTEIN

CELL-TYPE

Feature

Recognition

paper

framework


Input: Audio Hot Spotting And Retrieval Using Multiple Features This paper reports our on-going efforts to exploit multiple features derived from an audio stream using source material such as broadcast news, teleconferences, and meetings. These features are derived from algorithms including automatic speech recognition, automatic speech indexing, speaker identification, prosodic and audio feature extraction. We describe our research prototype-the Audio Hot Spotting System-that allows users to query and retrieve data from multimedia sources utilizing these multiple features. The system aims to accurately find segments of user interest, i. e., audio hot spots within seconds of the actual event. In addition to spoken keywords, the system also retrieves audio hot spots by speaker identity, word spoken by a specific speaker, a change of speech rate, and other non-lexical features, including applause and laughter. Finally, we discuss our approach to semantic, morphological, phonetic query expansion to improve audio retrieval performance and to access cross-lingual data.

Retrieval

Features

paper

reports

efforts

features

stream

source

broadcast news

meetings

features

algorithms

including

automatic speech recognition

automatic

speech

indexing

identification

feature

extraction

research

prototype

System

users

query

data

sources

features

system

segments

user

event

addition

keywords

system

word

speech

rate

non-lexical features

including

approach

semantic

query expansion

expansion

improve

retrieval

performance

access

cross-lingual

data

paper

efforts

features

stream

algorithms

features

query expansion

performance

data

sources


Input: Error Detection And Recovery In Spoken Dialogue Systems " This paper describes our research on both the detection and subsequent resolution of recognition errors in spoken dialogue systems. The paper consists of two major components. The first half concerns the design of the error detection mechanism for resolving city names in our mercury flight reservation system, and an investigation of the behavioral patterns of users in subsequent subdialogues involving keypad entry for disambiguation. An important observation is that, upon a request for keypad entry, users are frequently unresponsive to the extent of waiting for a time-out or hanging up the phone. The second half concerns a pilot experiment investigating the feasibility of replacing the solicitation of a keypad entry with that of a " " speak-and-spell " " entry. A novelty of our work is the introduction of a speech synthesizer to simulate the user, which facilitates development and evaluation of our proposed strategy. We have found that the speak-and-spell strategy is quite effective in simulation mode, but it remains to be tested in real user dialogues. "

Error

Detection

Recovery

Spoken Dialogue

Systems

paper

research

detection

resolution

recognition

errors

dialogue systems

paper

components

concerns

design

error

detection

mechanism

names

system

investigation

patterns

users

entry

disambiguation

observation

entry

users

extent

time-out

phone

concerns

pilot

experiment

entry

speak-and-spell

entry

novelty

introduction

speech

user

development

evaluation

proposed

strategy

speak-and-spell

strategy

simulation

mode

tested

user

dialogues

research

errors

paper

components

investigation

patterns

evaluation

strategy


Input: Part Of Speech Tagging For Amharic Using Conditional Random Fields We applied Conditional Random Fields (CRFs) to the tasks of Amharic word segmentation and POS tagging using a small annotated corpus of 1000 words. Given the size of the data and the large number of unknown words in the test corpus (80%), an accuracy of 84% for Amharic word segmentation and 74% for POS tagging is encouraging, indicating the applicability of CRFs for a morphologically complex language like Amharic.

Part Of Speech Tagging

applied

tasks

word segmentation

POS tagging

corpus

words

size

data

number

unknown words

words

test

corpus

accuracy

word segmentation

POS tagging

applicability

complex

language

words

corpus

unknown words

corpus

size

data

word segmentation

accuracy


Input: Association-Based Bilingual Word Alignment Bilingual word alignment forms the foundation of current work on statistical machine translation. Standard word-alignment methods involve the use of probabilistic generative models that are complex to implement and slow to train. In this paper we show that it is possible to approach the alignment accuracy of the standard models using algorithms that are much faster, and in some ways simpler, based on basic word-association statistics.

Association-

Based

Word Alignment

word alignment

forms

current

statistical machine translation

Standard

word-alignment

methods

generative models

models

complex

implement

train

paper

approach

alignment

accuracy

standard

models

algorithms

based

basic

word-association

statistics

generative models

methods

statistics

algorithms


Input: Mining Atomic Chinese Abbreviation Pairs: A Probabilistic Model For Single Character Word Recovery " An HMM-based Single Character Recovery (SCR) Model is proposed in this paper to extract a large set of " " atomic abbreviation pairs " " from a large text corpus. By an " " atomic abbreviation pair, " " it refers to an abbreviated word and its root word (i. e., unabbreviated form) in which the abbreviation is a single Chinese character. This task is interesting since the abbreviation process for Chinese compound words seems to be " " compositional " "; in other words, one can often decode an abbreviated word, such as " " nX " " (Taiwan University), character-by-character back to its root form. With a large atomic abbreviation dictionary, one may be able to recover multiple-character abbreviations more easily. With only a few training iterations, the acquisition accuracy of the proposed SCR model achieves 62% and 50% precision for training set and test set, respectively, from the ASWSC-2001 corpus. "

Chinese

Abbreviation

Probabilistic Model

Model

Word

Recovery

Recovery

Model

proposed

paper

extract

abbreviation

pairs

text

corpus

abbreviation

pair

word

word

form

abbreviation

Chinese

task

abbreviation

process

Chinese

words

words

word

University

form

abbreviation

dictionary

abbreviations

training

iterations

acquisition

accuracy

proposed

model

precision

training set

test set

corpus

Probabilistic Model

Recovery

paper

Model

pairs

corpus

model

precision


Input: Towards A Validated Model For Affective Classification Of Texts In this paper, we present the results of experiments aiming to validate a two-dimensional typology of affective states as a suitable basis for affective classification of texts. Using a corpus of English weblog posts, annotated for mood by their authors, we trained support vector machine binary classifiers to distinguish texts on the basis of their affiliation with one region of the space. We then report on experiments which go a step further, using four-class classifiers based on automated scoring of texts for each dimension of the typology. Our results indicate that it is possible to extend the standard binary sentiment analysis (positive/negative) approach to a two dimensional model (positive/negative; active/passive), and provide some evidence to support a more fine-grained classification along these two axes.

Model

Classification

Texts

paper

results

experiments

typology

basis

classification

texts

corpus

English

mood

trained

support vector machine

vector

machine

classifiers

texts

basis

region

space

report

experiments

step

four-class

classifiers

based

texts

dimension

typology

results

standard

sentiment analysis

approach

model

provide

evidence

support

classification

paper

results

corpus

mood

support vector machine

texts


Input: Automatic Dating Of Documents And Temporal Text Classification The frequency of occurrence of words in natural languages exhibits a periodic and a non-periodic component when analysed as a time series. This work presents an unsupervised method of extracting periodicity information from text, enabling time series creation and filtering to be used in the creation of sophisticated language models that can discern between repetitive trends and non-repetitive writing patterns. The algorithm performs in O (n

Automatic

Documents

Text Classification

frequency

occurrence

words

natural languages

component

time

series

method

extracting

information

text

time

series

creation

creation

language models

trends

patterns

algorithm

performs

occurrence

words

method

extracting

information

text


Input: Evaluation Of Several Phonetic Similarity Algorithms On The Task Of Cognate Identification We investigate the problem of measuring phonetic similarity, focusing on the identification of cognates, words of the same origin in different languages. We compare representatives of two principal approaches to computing phonetic similarity: manually-designed metrics, and learning algorithms. In particular, we consider a stochastic transducer, a Pair HMM, several DBN models, and two constructed schemes. We test those approaches on the task of identifying cognates among Indoeuropean languages, both in the supervised and unsupervised context. Our results suggest that the averaged context DBN model and the Pair HMM achieve the highest accuracy given a large training set of positive examples.

Evaluation

Similarity

Algorithms

Task

Identification

problem

similarity

focusing

identification

words

languages

representatives

approaches

computing

similarity

metrics

algorithms

Pair

models

constructed

schemes

test

approaches

task

languages

context

results

context

model

Pair

accuracy

training set

examples

Evaluation

Algorithms


Input: Multilingual Deep Lexical Acquisition For HPSGs Via Supertagging We propose a conditional random field-based method for supertagging, and apply it to the task of learning new lexical items for HPSG-based precision grammars of English and Japanese. Using a pseudo-likelihood approximation we are able to scale our model to hundreds of supertags and tens-of-thousands of training sentences. We show that it is possible to achieve start-of-the-art results for both languages using maximally language-independent lexical features. Further, we explore the performance of the models at the type-and token-level, demonstrating their superior performance when compared to a unigram-based baseline and a transformation-based learning approach.

Lexical

Acquisition

propose

conditional random field-based

field-based

method

apply

task

lexical items

items

precision

English

Japanese

approximation

scale

model

training

sentences

results

languages

language-independent

lexical features

performance

models

type-

token-level

performance

transformation-based

learning approach

conditional random field-based

lexical items


Input: Paraphrasing Of Japanese Light-Verb Constructions Based On Lexical Conceptual Structure Some particular classes of lexical paraphrases such as verb alteration and compound noun decomposition can be handled by a handful of general rules and lexical semantic knowledge. In this paper, we attempt to capture the regularity underlying these classes of paraphrases, focusing on the paraphrasing of Japanese light-verb constructions (LVCs). We propose a paraphrasing model for LVCs that is based on transforming the Lexical Conceptual Structures (LCSs) of verbal elements. We also propose a refinement of an existing LCS dictionary. Experimental results show that our LCS-based paraphrasing model characterizes some of the semantic features of those verbs required for generating paraphrases, such as the direction of an action and the relationship between arguments and surface cases.

Japanese

Verb

Based

Lexical

Conceptual Structure

classes

lexical

verb

noun

decomposition

handful

rules

lexical

semantic knowledge

paper

regularity

classes

focusing

Japanese

light-verb

constructions

propose

model

based

Lexical

Structures

verbal

propose

refinement

dictionary

Experimental

results

model

semantic features

verbs

required

generating

direction

action

relationship

arguments

surface

cases

paper

regularity

Structures

model


Input: A Standoff Annotation Interface Between DELPH-In Components We present a standoff annotation framework for the integration of NLP components, currently implemented in the context of the DELPH-IN tools

Interface

framework

integration

components

implemented

context

tools

framework

integration


Input: Lexicalized Tree Automata-Based Grammars For Translating Conversational Texts We propose a new lexicalized grammar formalism called Lexicalized Tree Automata-based Grammar, which lexicalizes tree acceptors instead of trees themselves. We discuss the properties of the grammar and present a chart parsing algorithm. We have implemented a translation module for conversational texts using this formalism, and applied it to an experimental automatic interpretation system (speech translation system).

Tree

Automata-

Based

Texts

propose

formalism

called

Tree

Automata-based

tree

trees

properties

parsing

algorithm

implemented

translation

module

texts

formalism

applied

experimental

automatic

interpretation

system

translation system

module

texts


Input: Induction Of Greedy Controllers For Deterministic Treebank Parsers Most statistical parsers have used the grammar induction approach, in which a stochastic grammar is induced from a treebank. An alternative approach is to induce a controller for a given parsing automaton. Such controllers may be stochastic; here, we focus on greedy controllers, which result in deterministic parsers. We use decision trees to learn the controllers. The resulting parsers are surprisingly accurate and robust, considering their speed and simplicity. They are almost as fast as current part-of-speech taggers, and considerably more accurate than a basic unlexicalized PCFG parser. We also describe Markov parsing models, a general framework for parser modeling and control, of which the parsers reported here are a special case.

Induction

statistical

parsers

grammar induction

approach

alternative

approach

parsing

focus

result

parsers

decision trees

resulting

parsers

robust

speed

simplicity

current

part-of-speech

basic

parser

parsing

models

framework

parser

modeling

control

parsers

reported

special case

approach

parsers

framework

modeling

robust

parsers


Input: Classifying The Semantic Relations In Noun Compounds Via A Domain-Specific Lexical Hierarchy We are developing corpus-based techniques for identifying semantic relations at an intermediate level of description (more specific than those used in case frames, but more general than those used in traditional knowledge representation systems). In this paper we describe a classification algorithm for identifying relationships between two-word noun compounds. We find that a very simple approach using a machine learning algorithm and a domain-specific lexical hierarchy successfully generalizes from training instances, performing better on previously unseen words than a baseline consisting of training on the words themselves.

Semantic

Relations

Noun

Domain-

Lexical

developing

corpus-based

techniques

semantic relations

relations

level

description

case

frames

knowledge representation

systems

paper

classification

algorithm

relationships

two-word

noun

simple

approach

machine

algorithm

domain-specific

lexical

training

instances

performing

words

training

words

techniques

semantic relations

algorithm

approach

paper

algorithm


Input: Chunk-Level Reordering of Source Language Sentences with Automatically Learned Rules for Statistical Machine Translation In this paper, we describe a source-side reordering method based on syntactic chunks for phrase-based statistical machine translation. First, we shallow parse the source language sentences. Then, reordering rules are automatically learned from source-side chunks and word alignments. During translation, the rules are used to generate a reordering lattice for each sentence. Experimental results are reported for a Chinese-to-English task, showing an improvement of 0. 5%-1. 8% BLEU score absolute on various test sets and better computational efficiency than reordering during decoding. The experiments also show that the reordering at the chunk-level performs better than at the POS-level.

Chunk-

Level

Source Language

Sentences

Statistical Machine Translation

paper

source-side

method

based

syntactic

chunks

phrase-based

statistical machine translation

parse

source language

sentences

rules

source-side

chunks

word alignments

translation

rules

generate

lattice

sentence

Experimental

results

reported

Chinese-to-

English

task

improvement

test sets

computational

efficiency

experiments

chunk-level

performs

POS-level

chunks

method

rules

chunks

lattice

sentence


Input: Generating Lexical Analogies Using Dependency Relations A lexical analogy is a pair of word-pairs that share a similar semantic relation. Lexical analogies occur frequently in text and are useful in various natural language processing tasks. In this study, we present a system that generates lexical analogies automatically from text data. Our system discovers semantically related pairs of words by using dependency relations, and applies novel machine learning algorithms to match these word-pairs to form lexical analogies. Empirical evaluation shows that our system generates valid lexical analogies with a precision of 70%, and produces quality output although not at the level of the best human-generated lexical analogies.

Generating

Lexical

Dependency

Relations

lexical

analogy

pair

word-pairs

semantic relation

Lexical

analogies

text

natural language processing tasks

study

system

generates

lexical

analogies

text

data

system

pairs

words

dependency relations

relations

applies

machine

algorithms

match

word-pairs

form

lexical

analogies

evaluation

system

generates

lexical

analogies

precision

quality

output

level

lexical

analogies

analogies

text

analogies

data

study

system

dependency relations

system

system

precision


Input: An Optimal Order of Factors for the Computational Treatment of Personal Anaphoric Devices in Urdu Discourse Handling of human language by computer is a very intricate and complex task. In natural languages, sentences are usually part of discourse units just as words are part of sentences. Anaphora resolution plays a significant role in discourse analysis for chopping larger discourse units into smaller ones. This process is done for the purpose of better understanding and making easier the further processing of text by computer. This paper is focused on the discussion of various factors and their optimal order that play an important role in personal anaphora resolution in Urdu. Algorithms are developed that resolves pronominal anaphoric devices with 77-80% success rate.

Optimal

Order

Computational

Treatment

Discourse

human language

computer

complex

task

natural languages

sentences

part

discourse

units

words

part

sentences

Anaphora resolution

resolution

role

discourse analysis

analysis

discourse

units

process

purpose

understanding

processing

text

computer

paper

focused

discussion

factors

optimal

order

role

anaphora resolution

resolution

Algorithms

developed

devices

success

rate

sentences

discourse

words

sentences

Anaphora resolution

discourse analysis

paper

factors

order

anaphora resolution

Algorithms

rate


Input: An Effective Hybrid Machine Learning Approach for Coreference Resolution We present a hybrid machine learning approach for coreference resolution. In our method, we use CRFs as basic training model, use active learning method to generate combined features so as to make existed features used more effectively; at last, we proposed a novel clustering algorithm which used both the linguistics knowledge and the statistical knowledge. We built a coreference resolution system based on the proposed method and evaluate its performance from three aspects: the contributions of active learning; the effects of different clustering algorithms; and the resolution performance of different kinds of NPs. Experimental results show that additional performance gain can be obtained by using active learning method; clustering algorithm has a great effect on coreference resolution' s performance and our clustering algorithm is very effective; and the key of coreference resolution is to improve the performance of the normal noun' s resolution, especially the pronoun' s resolution.

Machine Learning Approach

Learning Approach

Coreference Resolution

Resolution

machine

approach

coreference resolution

resolution

method

basic

training

model

learning method

method

generate

features

features

proposed

clustering

algorithm

linguistics

knowledge

statistical

knowledge

coreference resolution system

resolution

system

based

proposed

method

evaluate

performance

aspects

contributions

active learning

effects

clustering

algorithms

resolution

performance

kinds

Experimental

results

performance

gain

active learning

method

clustering

algorithm

effect

coreference resolution

performance

clustering

algorithm

coreference resolution

improve

performance

noun

resolution

resolution

Machine Learning Approach

Coreference Resolution

approach

coreference resolution

knowledge

algorithm

method

coreference resolution system

learning method

features

method

gain

algorithm

performance


Input: Generalizing Tree Transformations for Inductive Dependency Parsing Previous studies in data-driven dependency parsing have shown that tree transformations can improve parsing accuracy for specific parsers and data sets. We investigate to what extent this can be generalized across languages/treebanks and parsers, focusing on pseudo-projective parsing, as a way of capturing non-projective dependencies, and transformations used to facilitate parsing of coordinate structures and verb groups. The results indicate that the beneficial effect of pseudo-projective parsing is independent of parsing strategy but sensitive to language or treebank specific properties. By contrast, the construction specific transformations appear to be more sensitive to parsing strategy but have a constant positive effect over several languages.

Tree

Dependency

Parsing

studies

data-driven

dependency

parsing

tree

transformations

improve

parsing

accuracy

parsers

data

extent

generalized

languages

parsers

focusing

parsing

dependencies

transformations

parsing

structures

verb

results

effect

parsing

parsing

strategy

language

properties

contrast

construction

transformations

parsing

strategy

effect

languages

studies

parsing

transformations

accuracy

language

parsing

strategy

transformations


Input: Construction of Domain Dictionary for Fundamental Vocabulary Guthrie, Joe A.; Guthrie, Louise; Aidinejad, Homa; Wilks, Yorick, Subject-Dependent Co-Occurrence And Word Sense Disambiguation, Annual Meeting Of The Association For Computation al Linguistics, 1991

Construction

Domain

Dictionary

Vocabulary

Occurrence

Word Sense Disambiguation

Association

Computation

Linguistics

Vocabulary

Dictionary


Input: Extracting a Representation from Text for Semantic Analysis We present a novel fine-grained semantic representation of text and an approach to constructing it. This representation is largely extractable by today' s technologies and facilitates more detailed semantic analysis. We discuss the requirements driving the representation, suggest how it might be of value in the automated tutoring domain, and provide evidence of its validity.

Extracting

Representation

Text

Semantic Analysis

semantic representation

representation

text

approach

constructing

representation

technologies

semantic analysis

requirements

representation

domain

provide

evidence

validity

Representation

Text

semantic representation

text

representation

technologies


Input: KnowNet: Building a Large Net of Knowledge from the Web This paper presents a new fully automatic method for building highly dense and accurate knowledge bases from existing semantic resources. Basically, the method uses a wide-coverage and accurate knowledge-based Word Sense Disambiguation algorithm to assign the most appropriate senses to large sets of topically related words acquired from the web. KnowNet, the resulting knowledge-base which connects large sets of semantically-related concepts is a major step towards the autonomous acquisition of knowledge from raw corpora. In fact, KnowNet is several times larger than any available knowledge resource encoding relations between synsets, and the knowledge KnowNet contains outperform any other resource when is empirically evaluated in a common framework.

Knowledge

paper

automatic

method

building

knowledge bases

bases

semantic

resources

method

wide-coverage

knowledge-based

Word Sense Disambiguation

algorithm

words

resulting

knowledge-base

semantically-related

concepts

step

acquisition

knowledge

corpora

times

knowledge

resource

relations

knowledge

resource

evaluated

common

framework

paper

method

knowledge bases

resources

concepts

knowledge-base

knowledge

corpora

algorithm

method

knowledge

resource


Input: A Two-Stage Parser for Multilingual Dependency Parsing We present a two-stage multilingual dependency parsing system submitted to the Multilingual Track of CoNLL-2007. The parser first identifies dependencies using a deterministic parsing method and then labels those dependencies as a sequence labeling problem. We describe the features used in each stage. For four languages with different values of ROOT, we design some special features for the ROOT labeler. Then we present evaluation results and error analyses focusing on Chinese.

Parser

Dependency

Parsing

dependency

parsing

system

parser

dependencies

parsing

method

dependencies

sequence

problem

features

languages

design

features

evaluation results

error analyses

focusing

Chinese

method

parser


Input: Parsing The Wall Street Journal Using A Lexical-Functional Grammar And Discriminative Estimation Techniques We present a stochastic parsing system consisting of a Lexical-Functional Grammar (LFG), a constraint-based parser and a stochastic disambiguation model. We report on the results of applying this system to parsing the UPenn Wall Street Journal (WSJ) treebank. The model combines full and partial parsing techniques to reach full grammar coverage on unseen data. The treebank annotations are used to provide partially labeled data for discriminative statistical estimation using exponential models. Disambiguation performance is evaluated by measuring matches of predicate-argument relations on two distinct test sets. On a gold standard of manually annotated f-structures for a subset of the WSJ treebank, this evaluation reaches 79% F-score. An evaluation on a gold standard of dependency relations for Brown corpus data achieves 76% F-score.

Parsing

Wall Street Journal

Journal

Lexical-

Estimation

Techniques

parsing

system

Lexical-

constraint-based

parser

disambiguation

model

report

results

applying

system

parsing

Wall Street Journal

Journal

model

partial

parsing

techniques

reach

coverage

data

provide

data

statistical

estimation

models

Disambiguation

performance

evaluated

matches

predicate-argument

relations

test sets

gold standard

f-structures

evaluation

reaches

evaluation

gold standard

standard

dependency relations

Brown corpus

data

Parsing

Wall Street Journal

parsing

Wall Street Journal

models

estimation

evaluation

gold standard

parser

system

techniques

model


Input: Thumbs Up Or Thumbs Down? Semantic Orientation Applied To Unsupervised Classification Of Reviews This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended not recommended semantic orientation

Semantic

Orientation

Applied

Classification

paper

simple

learning algorithm

algorithm

reviews

semantic

orientation

Orientation

Classification

paper

learning algorithm


Input: Self-Organizing Markov Models And Their Application To Part-Of-Speech Tagging This paper presents a method to develop a class of variable memory Markov models that have higher memory capacity than traditional (uniform memory) Markov models. The structure of the variable memory models is induced from a manually annotated corpus through a decision tree learning algorithm. A series of comparative experiments show the resulting models outperform uniform memory Markov models in a part-of-speech tagging task.

Models

Application

Part-

Speech

Tagging

paper

method

develop

class

variable

memory

Markov models

models

memory

capacity

memory

Markov models

models

structure

variable

memory

models

corpus

decision tree

algorithm

series

experiments

resulting

models

memory

Markov models

models

part-of-speech

tagging

task

Models

Tagging

paper

method

Markov models

Markov models

models

Markov models

structure

corpus


Input: Unsupervised Sense Disambiguation Using Bilingual Probabilistic Models We describe two probabilistic models for unsupervised word-sense disambiguation using parallel corpora. The first model, which we call the Sense model, builds on the work of Diab and Resnik (2002) that uses both parallel text and a sense inventory for the target language, and recasts their approach in a probabilistic framework. The second model, which we call the Concept model, is a hierarchical model that uses a concept latent variable to relate different language specific sense labels. We show that both models improve performance on the word sense disambiguation task over previous unsu-pervised approaches, with the Concept model showing the largest improvement. Furthermore, in learning the Concept model, as a by-product, we learn a sense inventory for the parallel language.

Sense Disambiguation

Disambiguation

Models

probabilistic models

models

word-sense

word-sense disambiguation

disambiguation

parallel corpora

model

call

Sense

model

parallel text

sense

inventory

target language

approach

framework

model

call

Concept

model

model

concept

variable

language

sense

models

improve

performance

word sense disambiguation

task

approaches

Concept

model

improvement

Concept

model

by-product

sense

inventory

language

Models

Sense Disambiguation

probabilistic models

word-sense disambiguation

variable

model

models

performance

inventory

language


Input: Adaptive Chinese Word Segmentation This paper presents a Chinese word segmentation system which can adapt to different domains and standards. We first present a statistical framework where domain-specific words are identified in a unified approach to word segmentation based on linear models. We explore several features and describe how to create training data by sampling. We then describe a transformation-based learning method used to adapt our system to different word segmentation standards. Evaluation of the proposed system on five test sets with different standards shows that the system achieves stateof-the-art performance on all of them.

Chinese Word Segmentation

paper

Chinese word segmentation

system

adapt

domains

standards

statistical

framework

domain-specific

words

approach

word segmentation

based

linear models

models

features

training

data

sampling

transformation-based

learning method

adapt

system

word segmentation

standards

Evaluation

proposed

system

test sets

standards

system

performance

paper

system

Evaluation

system

linear models

approach

system

performance


Input: Multimodal Database Access On Handheld Devices We present the final MIAMM system, a multimodal dialogue system that employs speech, haptic interaction and novel techniques of information visualization to allow a natural and fast access to large multimedia databases on small handheld devices.

Database

Access

system

dialogue system

system

speech

interaction

techniques

information

visualization

natural

access

databases

devices

techniques

dialogue system


Input: Corpus-Oriented Development Of Japanese HPSG Parsers This paper reports the corpus-oriented development of a wide-coverage Japanese HPSG parser. We first created an HPSG treebank from the EDR corpus by using heuristic conversion rules, and then extracted lexical entries from the tree-bank. The grammar developed using this method attained wide coverage that could hardly be obtained by conventional manual development. We also trained a statistical parser for the grammar on the tree-bank, and evaluated the parser in terms of the accuracy of semantic-role identification and dependency analysis.

Corpus-

Development

Japanese

paper

reports

corpus-oriented

development

wide-coverage

Japanese

parser

corpus

conversion

rules

extracted

lexical entries

entries

tree-bank

developed

method

coverage

manual

development

trained

statistical

parser

tree-bank

evaluated

parser

terms

accuracy

semantic-role

identification

dependency

analysis

paper

parser

lexical entries

tree-bank


Input: Man-Assisted Machine Construction Of A Semantic Dictionary For Natural Language Processing This is a report on the semantic dictionary for natural language processing we are constructing now. This paper explains how to obtain the semantic information for the dictionary from an ordinary Japanese language dictionary with about 60, 000 items (which had already been put into machine readable form) and also explains what should be the frame for the representation of meaning of each item (word). Then a man-assisted machine procedure that embeds the semantic graph with respect to the head word of the ordinary dictionary into the frame of a head word is discussed.

Machine

Construction

Semantic

Dictionary

Natural Language

Processing

report

semantic

dictionary

natural language processing

constructing

paper

semantic information

information

dictionary

Japanese

language

dictionary

items

machine

form

frame

representation

item

word

machine

procedure

semantic

respect

head word

dictionary

frame

head word

Dictionary

Processing

report

dictionary

semantic information

dictionary

representation

item


Input: Strategies For Interactive Machine Translation: The Experience And Implications Of The UMIST Japanese Project At the Contre for Computational Linguistics, we are designing and implementing an Eng. lish-to-Japanese interactive machine translation system. The project is funded jointly by the Alvey Directorate and International Computers Limited (ICL). The prototype system runs on the ICL PERQ, though much of the development work has been done on a VAX 11/750. It is implemented in Prolog, in the interests of rapid prototyping, but intended for optimization. The informing principles are those of modern comp1 ex-feature-based linguistic theories, in particular Lexical-Functional Grammar (Bresnan (ed.) 1982, Kaplan and Bresnan 1982), and Generalized Phrase Structure Grammar (Gazdar et al. 1985). For development purposes we are using an existing corpus of 10, 000 words of continuous prose from the PERQ' s graphics documentation; in the long term, the system will be extended for use by technical writers in fields other than software. At the time of writing, we have well-developed system development software, user interface, and grammar and dictionary handling utilities. The English analysis grammar handles most of the syntactic structures of the corpus, and we have a range of formats for output of linguistic representations and Japanese text. A transfer grammar for English-Japanese has been prototyped, but is not not yet fully adequate to handle all constructions in the corpus; a facility for dictionary entry in kanji is incorporated. The aspect of the system we will focus on in the present paper is its interactive nature, discussing the range of different types of interaction which are provided or permitted for different types of user.

Strategies

Machine Translation

Experience

Japanese

Project

Computational Linguistics

designing

implementing

Japanese

machine translation system

project

Computers

prototype

system

development

implemented

prototyping

optimization

principles

ex-feature-based

linguistic theories

theories

Lexical-

Generalized Phrase Structure Grammar

development

purposes

corpus

words

documentation

term

system

fields

software

time

system

development

software

user interface

dictionary

utilities

English

analysis

syntactic structures

corpus

formats

output

representations

Japanese

text

transfer

English-

Japanese

constructions

corpus

dictionary

entry

kanji

aspect

system

focus

paper

nature

types

interaction

provided

types

user

corpus

documentation


Input: Exploiting Lexical Regularities In Designing Natural Language Systems This paper presents the lexical component of the START Question Answering system developed at the MIT Artificial Intelligence Laboratory. START is able to interpret correctly a wide range of semantic relationships associated with alternate expressions of the arguments of verbs. The design of the system takes advantage of the results of recent linguistic research into the structure of the lexicon, allowing START to attain a broader range of coverage than many existing systems while maintaining modular organization.

Lexical

Natural Language

Systems

paper

lexical

component

Question Answering system

Answering system

developed

Artificial Intelligence

Laboratory

semantic

relationships

expressions

arguments

verbs

design

system

advantage

results

research

structure

lexicon

coverage

systems

organization

paper

Question Answering system

research

design


Input: Reasons Why I Do Not Care Grammar Formalism has borrowed a lot of ideas from We could not have developed even a simple parser without the research results in It is obviously nonsense to claim that we, computational linguists, do not care research results in However, the researchers in it seems to me, are very fond of especially, those who are called They always fight with each other by asserting that their are superior to the others'. They are oversensitive and tend to distinguish people into two groups, and A computational linguist using LFG (or LFG) as a small part in his total system is taken as the ally of LFG, and is certainly accused by the other groups. They promptly demonstrate that LFG is wrong, by showing a lot of peculiar sentences which rarely appear in real texts. Formalisms are prepared for accomplishing specific purposes. The formalisms in have been proposed, roughly speaking, for describing the of distinguishing from arbitrary/ramraa/ica/sequences, and of relating the grammatical sequences with the other representational levels. On the other hand, a formalism we need in CL is for different purposes. That is, we need a formalism for describing the rules of distinguishing the most feasible grammatical structures from other less feasible but still ones of the same sentences We also need a formalism in which we can manage systematically a large amount of knowledge of various sorts necessary for NLP. Formalisms for different purposes, of course, should be evaluated based on different standards. The current discussions of different formalisms in TL are irrelevant to our standards, though they may be important for their The following is a list of the reasons why I think so.

Reasons

Formalism

developed

simple

parser

research

results

claim

computational linguists

research

results

researchers

called

computational linguist

part

system

ally

sentences

texts

purposes

formalisms

proposed

sequences

sequences

levels

hand

formalism

purposes

formalism

rules

structures

sentences

formalism

amount

knowledge

purposes

evaluated

based

standards

current

discussions

formalisms

standards

list

reasons

results

parser

sentences

texts


Input: Directing The Generation Of Living Space Descriptions We have developed a Computational model of the process of describing the layout of an apartment or house, a much-studied discourse task first characterized linguistically by Linde (1974). The model is embodied in a program, APT, that can reproduce segments of actual tape-recorded descriptions, using organizational and discourse strategies derived through analysis of our corpus.

Generation

Space

developed

Computational model

process

layout

discourse

task

model

program

segments

descriptions

discourse

strategies

analysis

corpus

analysis

corpus