Input: Process Model for Composing High-quality Text Corpora The Teko corpus composing model offers a decentralized, dynamic way of collecting high-quality text corpora for linguistic research. The resulting corpus consists of independent text sets. The sets are composed in cooperation with linguistic research projects, so each of them responds to a specific research need. The corpora are morphologically annotated and XML-based, with in-built compatibilty with the Kaino user interface used in the corpus server of the Research Institute for the Languages of Finland. Furthermore, software for extracting standard quantitative reports from the text sets has been created during the project. The paper describes the project, and estimates its benefits and problems. It also gives an overview of the technical qualities of the corpora and corpus interface connected to the Teko project.

Process

Model

High-quality

Text

Corpora

corpus

model

high-quality

text

corpora

research

resulting

corpus

text

cooperation

research projects

research

corpora

user interface

corpus

server

Research

Languages

software

extracting

standard

reports

text

project

paper

project

benefits

problems

overview

qualities

corpora

corpus

interface

project

Process

Corpora

model

corpus

corpora

research

software

extracting

text

corpus

paper

project

overview

qualities


Input: AnCora: Multilevel Annotated Corpora for Catalan and Spanish This paper presents AnCora, a multilingual corpus annotated at different linguistic levels consisting of 500, 000 words in Catalan (AnCora-Ca) and in Spanish (AnCora-Es). At present AnCora is the largest multilayer annotated corpus of these languages freely available from http://clic. ub. edu/ancora. The two corpora consist mainly of newspaper texts annotated at different levels of linguistic description: morphological (PoS and lemmas), syntactic (constituents and functions), and semantic (argument structures, thematic roles, semantic verb classes, named entities, and WordNet nominal senses). All resulting layers are independent of each other, thus making easier the data management. The annotation was performed manually, semiautomatically, or fully automatically, depending on the encoded linguistic information. The development of these basic resources constituted a primary objective, since there was a lack of such resources for these languages. A second goal was the definition of a consistent methodology that can be followed in further annotations. The current versions of AnCora have been used in several international evaluation competitions

Corpora

paper

corpus

levels

words

corpus

languages

corpora

newspaper

texts

levels

linguistic description

lemmas

syntactic

constituents

functions

semantic

argument structures

roles

semantic

verb classes

named

entities

resulting

layers

data

management

performed

linguistic information

development

basic

resources

objective

lack

resources

languages

goal

definition

methodology

current

versions

evaluation

competitions

words

corpus

languages

corpus

texts

corpora


Input: Studying Discourse and Dialogue with SIDGrid Teaching Computational Linguistics is inherently multi-disciplinary and frequently poses challenges and provides opportunities in teaching to a student body with diverse educational backgrounds and goals. This paper describes the use of a computational environment (SIDGrid) that facilitates interdisciplinary instruction by providing support for students with little computational background as well as extending the scale of projects accessible to students with more advanced computational skills. The environment facilitates the use of hands-on exercises and is being applied to interdisciplinary instruction in Discourse and Dialogue.

Discourse

Dialogue

Computational Linguistics

challenges

provides

backgrounds

goals

paper

computational

environment

instruction

providing

support

computational

background

scale

projects

advanced

computational

environment

applied

instruction

Discourse

Dialogue

environment

instruction

environment

instruction


Input: A Combined Memory-Based Semantic Role Labeler of English We describe the system submitted to the closed challenge of the CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies. Syntactic dependencies are processed with the Malt-Parser 0. 4. Semantic dependencies are processed with a combination of memory-based classifiers. The system achieves 78. 43 labeled macro Fl for the complete problem, 86. 07 labeled attachment score for syntactic dependencies, and 70. 51 labeled Fl for semantic dependencies.

Memory-

Based

Semantic Role

English

system

challenge

task

parsing

syntactic

semantic

dependencies

Syntactic

dependencies

processed

Parser

Semantic

dependencies

processed

combination

memory-based

classifiers

system

problem

attachment

syntactic

dependencies

semantic

dependencies

system

challenge

parsing

dependencies

Parser

dependencies

classifiers

dependencies

system

problem


Input: Applying Many-to-Many Alignments and Hidden Markov Models to Letter-to-Phoneme Conversion Letter-to-phoneme conversion generally requires aligned trainingdata of letters and phonemes. Typically, the alignments are limited to one-to-one alignments. We present a novel technique of training with many-to-many alignments. A letter chunking bigram prediction manages double letters and double phonemes automatically as opposed to preprocessing with fixed lists. We also apply an HMM method in conjunction with a local classification model to predict a global phoneme sequence given a word. The many-to-many alignments result in significant improvements over the traditional one-to-one approach. Our system achieves state-of-the-art performance on several languages anddata sets.

Applying

Models

Phoneme

Conversion

Letter-to-phoneme

conversion

requires

trainingdata

trainingdata

phonemes

alignments

limited

alignments

technique

training

alignments

chunking

prediction

phonemes

lists

apply

method

conjunction

classification model

phoneme

sequence

word

alignments

result

improvements

approach

system

performance

languages

anddata

Models

Conversion

alignments

training

prediction

phonemes

classification model

sequence

phonemes

trainingdata

alignments

approach

system

performance


Input: Conquest---An Open-Source Dialog System for Conferences We describe ConQuest, an open-source, reusable spoken dialog system that provides technical program information during conferences. The system uses a transparent, modular and open infrastructure, and aims to enable applied research in spoken language interfaces. The conference domain is a good platform for applied research since it permits periodical redeployments and evaluations with a real user-base. In this paper, we describe the system' s functionality, overall architecture, and we discuss two initial deployments.

Source

Dialog System

open-source

dialog system

provides

program

information

system

infrastructure

applied

research

language

interfaces

domain

platform

applied

research

evaluations

user-base

paper

system

functionality

architecture

deployments

dialog system

information

infrastructure

system

domain

research

research

interfaces

paper

functionality


Input: Attention Shifting For Parsing Speech We present a technique that improves the efficiency of word-lattice parsing as used in speech recognition language modeling. Our technique applies a probabilistic parser iteratively where on each iteration it focuses on a different subset of the word-lattice. The parser' s attention is shifted towards word-lattice subsets for which there are few or no syntactic analyses posited. This attention-shifting technique provides a six-times increase in speed (measured as the number of parser analyses evaluated) while performing equivalently when used as the first-stage of a multi-stage parsing-based language model.

Parsing

Speech

technique

improves

efficiency

word-lattice

parsing

speech recognition

language modeling

technique

applies

parser

iteration

focuses

word-lattice

parser

shifted

word-lattice

syntactic analyses

technique

provides

increase

speed

number

parser

analyses

evaluated

performing

parsing-based

language model

technique

parsing

parser

technique

word-lattice

parser

technique

increase


Input: A Hybrid Relational Approach For WSD-First Results We present a novel hybrid approach for Word Sense Disambiguation (WSD) which makes use of a relational formalism to represent instances and background knowledge. It is built using Inductive Logic Programming techniques to combine evidence coming from both sources during the learning process, producing a rule-based WSD model. We experimented with this approach to disambiguate 7 highly ambiguous verbs in English-Portuguese translation. Results showed that the approach is promising, achieving an average accuracy of 75%, which outperforms the other machine learning techniques investigated (66%).

Relational

Approach

Results

approach

Word Sense Disambiguation

relational

formalism

instances

background knowledge

Logic Programming

techniques

evidence

sources

process

rule-based

model

experimented

approach

disambiguate

verbs

English-

translation

Results

approach

accuracy

machine

techniques

approach

Word Sense Disambiguation

techniques

evidence

approach

verbs

formalism

instances

process

model

approach

accuracy


Input: Machine Translation Based On Logically Isomorphic Montague Grammars " Usually two approaches to machine translation are distinguished: the interlingual approach and the transfer approach (cf. Hutchins [1]). In the interlingual approach translation is a two-stage process: from source language to interlingua and from interlingua to target language. In the transfer approach there are three stages: source language analysis, transfer and target language generation. The approach advanced in this paper is a variant of the interlingual one. It requires that' logically isomorphic grammars' are written for the languages, under consideration. The syntactic rules of these grammars must correspond with logical operations, in accordance with the compositionality principle of Montague grammar. Moreover, the grammars must be attuned to each other as follows: if one grammar contains a rule corresponding with a particular logical operation, the other grammars must contain rules corresponding with the same operation. Syntactically, these rules may differ considerably. If the grammars are attuned to each other in this way,' logical derivation trees', representations of both the syntactical and the logical structure of sentences, can be used as intermediate expressions. The paper is organized as follows. In section 2 the relevant concepts of Montague grammar and the notion' logically isomorphic grammars' are introduced. In section 3 a version of Montague grammar is described, called M-grammar, which is more suitable for computational use than Montague' s original proposals. The property of logical isomorphy is then defined for M-grammars. In section 4 the design of the Rosetta translation system, based on this approach, is outlined, followed by a brief discussion in section 5. "

Machine Translation

Based

approaches

machine translation

approach

transfer

approach

approach

translation

process

source language

target language

transfer

approach

source language

analysis

transfer

target language

generation

approach

advanced

paper

variant

requires

languages

consideration

syntactic

rules

operations

principle

rule

operation

rules

operation

rules

derivation

trees

representations

structure

sentences

expressions

paper

section

concepts

notion

section

version

called

computational

proposals

property

section

design

translation system

based

approach

outlined

discussion

section

approach

approach

process

approach

analysis

approach

paper

approach

section

concepts

section

version

section

design

principle

rules

approach

translation system

operation

rule

operation

rules

trees

structure


Input: A Compositional Semantics For Directional Modifiers-Locative Case Reopened This paper presents a model-theoretic semantics for directional modifiers in English. The semantic theory presupposed for the analysis is that of Montague Grammar (cf. Montague 1970, 1973) which makes it possible to develop a strongly compositional treatment of directional modifiers. Such a treatment has significant computational advantages over case-based treatments of directional modifiers that are advocated in the Al literature.

Semantics

Case

paper

model-theoretic

semantics

modifiers

English

semantic

theory

analysis

develop

treatment

modifiers

treatment

computational

advantages

case-based

treatments

modifiers

literature

paper

semantics

modifiers

English

theory

analysis

treatment

treatments


Input: Stylistic Grammars In Language Translation We are developing stylistic grammars to provide the basis for a French and English stylistic parser. Our stylistic grammar is a branching stratificational model, built upon a foundation dealing with lexical, syntactic, and semantic stylistic realizations. Its central level uses a vocabulary of constituent stylistic elements common to both English and French, while the top level correlates stylistic goals, such as clarity and concreteness, with patterns of these elements. Overall, we are implementing a computational schema of stylistics in French-to-English translation. We believe that the incorporation of stylistic analysis into machine translation systems will significantly reduce the current reliance on human post-editing and improve the quality of the systems' output.

Language

Translation

developing

provide

basis

English

parser

model

dealing

lexical

syntactic

semantic

realizations

level

vocabulary

constituent

common

English

level

goals

patterns

implementing

computational

schema

English

translation

analysis

machine translation systems

current

improve

quality

systems'

output

model

realizations

schema

translation

vocabulary

level

analysis

machine translation systems

systems'

quality


Input: Segmenting A Sentence Into Morphemes Using Statistic Information Between Words This paper is on dividing non-separated language sentences (whose words are not separated from each other with a space or other separater) into morphemes using statistical information, not grammatical information which is often used in NLP. In this paper we describe our method and experimental result on Japanese and Chinese sentences. As will be seen in the body of this paper, the result shows that this system is efficient for most of the sentences.

Sentence

Information

Words

paper

language

sentences

words

space

statistical

information

information

paper

method

experimental

result

Japanese

Chinese

sentences

paper

result

system

sentences

Information

Words

words

sentences

information

information

paper

method

sentences

result

system

result


Input: A System Of Verbal Semantic Attributes Focused On The Syntactic Correspondence Between Japanese And English This paper proposes a system of 97 verbal semantic attributes for Japanese verbs which considers both dynamic characteristics and the relationship of verbs to cases. These attribute values are used to disambiguate the meanings of all Japanese and English pattern pairs in a Japanese to English transfer pattern dictionary consisting of 15, 000 pairs of Japanese valence patterns and equivalent English syntactic structures.

System

Verbal

Semantic

Syntactic

Correspondence

Japanese

English

paper

proposes

system

verbal

semantic

Japanese

verbs

characteristics

relationship

verbs

cases

disambiguate

Japanese

English

pattern

pairs

Japanese

English

transfer

pattern

dictionary

pairs

Japanese

valence

patterns

English

syntactic structures

paper

system

characteristics

verbs

patterns

dictionary


Input: Dependency Unification Grammar This paper describes the analysis component of the language processing system PLAIN from the viewpoint of unification grammars. The principles of Dependency Unification Grammar (DUG) are discussed. The computer language DRL (Dependency Representation Language) is introduced in which DUGs can be formulated. A unification-based parsing procedure is part of the formalism. PLAIN is implemented at. the universities of Heidelberg, Bonn, Flensburg, Kiel, Zurich and Cambridge U. K.

Dependency

Unification

paper

analysis

component

language processing system

unification

principles

Dependency

Unification

computer

language

Dependency

Representation Language

unification-based

parsing

procedure

part

formalism

implemented

universities

component

language processing system

procedure

formalism


Input: Acquiring Domain-Specific Dialog Information from Task-Oriented Human-Human Interaction through an Unsupervised Learning We describe an approach for acquiring the domain-specific dialog knowledge required to configure a task-oriented dialog system that uses human-human interactiondata. The key aspects of this problem are the design of a dialog information representation and a learning approach that supports capture of domain information from in-domain dialogs.

Domain-

Dialog

Information

Task-

Interaction

Learning

approach

domain-specific

dialog

knowledge

required

task-oriented

dialog system

interactiondata

interactiondata

aspects

problem

design

dialog

information

representation

approach

supports

domain

information

in-domain

dialogs

Learning

Information

knowledge

dialog system

approach

information

design

representation


Input: A Development Environment for Configurable Meta-Annotators in a Pipelined NLP Architecture Information extraction from largedata repositories is critical to Information Management solutions. In addition to prerequisite corpus analysis, to determine domain-specific characteristics of text resources, developing, refining and evaluating analytics entails a complex and lengthy process, typically requiring more than just domain expertise. Modern architectures for text processing, while facilitating reuse and (re-) composition of analytical pipelines, place additional constraints upon the analytics development, as domain experts need not only configure individual annotator components, but situate these within a fully functional annotator pipeline. We present the design, and current status, of a tool for configuring model-driven annotators, which abstracts away from annotator implementation details, pipeline composition constraints, anddata management. Instead, the tool embodies support for all stages of ontology-centric model development cycle-from corpus analysis and concept definition, to model development and testing, to large scale evaluation, to easy and rapid composition of text applications deploying these concept models. With our design, we aim to meet the needs of domain experts, who are not necessarily expert NLP practitioners.

Development

Environment

Architecture

Information extraction

largedata

repositories

Information

Management

solutions

addition

corpus

analysis

domain-specific

characteristics

text

resources

developing

evaluating

complex

process

requiring

domain

architectures

text processing

pipelines

constraints

development

domain

experts

individual

components

pipeline

design

current

status

tool

model-driven

abstracts

implementation

details

pipeline

constraints

anddata

management

tool

support

ontology-centric

model

development

cycle

corpus

analysis

concept

definition

model

development

scale

evaluation

text

applications

concept

models

design

domain

experts

expert

Environment

Architecture

components

pipeline

Information extraction

solutions

architectures

text processing

tool

cycle

characteristics

resources

process

evaluating

status

tool


Input: Combining Multiple Models for Speech Information Retrieval In this article we present a method for combining different information retrieval models in order to increase the retrieval performance in a Speech Information Retrieval task. The formulas for combining the models are tuned on trainingdata. Then the system is evaluated on testdata. The task is particularly difficult because the text collection is automatically transcribed spontaneous speech, with many recognition errors. Also, the topics are real information needs, difficult to satisfy. Information Retrieval systems are not able to obtain good results on this data set, except for the case when manual summaries are included.

Combining

Models

Speech

Information Retrieval

method

information retrieval

models

order

increase

retrieval

performance

Speech

Information Retrieval

task

formulas

models

trainingdata

trainingdata

system

evaluated

testdata

testdata

task

text

collection

spontaneous speech

recognition

errors

topics

information

Information Retrieval systems

results

data

case

manual

summaries

included

Models

Information Retrieval

method

task

system

testdata

task

spontaneous speech

topics

information

Information Retrieval systems

results

summaries

data


Input: Subdomain Sensitive Statistical Parsing using Raw Corpora Modern statistical parsers are trained on large annotated corpora (treebanks). These treebanks usually consist of sentences addressing different subdomains (e. g. sports, politics, music), which implies that the statistics gathered by current statistical parsers are mixtures of subdomains of language use. In this paper we present a method that exploits raw subdomain corpora gathered from the web to introduce subdomain sensitivity into a given parser. We employ statistical techniques for creating an ensemble of domain sensitive parsers, and explore methods for amalgamating their predictions. Our experiments show that introducing domain sensitivity by exploiting raw corpora can improve over a tough, state-of-the-art baseline.

Statistical

Parsing

Corpora

statistical

parsers

trained

corpora

sentences

sports

statistics

current

statistical

parsers

mixtures

language

paper

method

corpora

sensitivity

parser

statistical

techniques

domain

parsers

methods

predictions

experiments

domain

sensitivity

corpora

improve

Corpora

Parsing

statistics

parsers

techniques

parsers

methods

predictions

corpora

sensitivity

paper

method

sensitivity

parser


Input: An Experiment On The Upper Bound Of Interjudge Agreement: The Case Of Tagging We investigate the controversial issue about the upper bound of interjudge agreement in the use of a low-level grammatical representation. Pessimistic views suggest that several percent of words in running text are undecidable in terms of part-of-speech categories. Our experiments with 55kWdata give reason for optimism: linguists with only 30 hours' training apply the EngCG-2 morphological tags with almost 100% interjudge agreement.

Experiment

Bound

Agreement

Case

Tagging

issue

agreement

low-level

representation

words

text

terms

part-of-speech

categories

experiments

reason

linguists

training

apply

tags

agreement

agreement

representation

training

linguists

agreement

tags

words

text


Input: Pointing To Events Although there is an extensive body of research concerned with anaphora resolution (e. g. (Fox, 1987; Grosz et al., 1995)), event anaphora has been widely neglected. This paper describes the results of an empirical study regarding event reference. The experiment investigated event anaphora in narrative discourse via a sentence completion task.

Events

research

concerned

anaphora resolution

event

paper

results

study

event

reference

experiment

event

discourse

sentence

completion

task

research

anaphora resolution

paper

results

study

reference

task

experiment


Input: Learning PP Attachment For Filtering Prosodic Phrasing We explore learning prepositional-phrase attachment in Dutch, to use it as a filter in prosodic phrasing. From a syntactic treebank of spoken Dutch we extract instances of the attachment of prepositional phrases to either a governing verb or noun. Using cross-validated parameter and feature selection, we train two learning algorithms, iBl and RIPPER, on making this distinction, based on unigram and bigram lexical features and a cooccurrence feature derived from WWW counts. We optimize the learning on noun attachment, since in a second stage we use the attachment decision for blocking the incorrect placement of phrase boundaries before prepositional phrases attached to the preceding noun. On noun attachment, IBl attains an F-score of 82; RIPPER an F-score of 78. When used as a filter for prosodic phrasing, using attachment decisions from IBl yields the best improvement on precision (by six points to 71) on phrase boundary placement.

Learning

Attachment

prepositional-phrase

attachment

syntactic

extract

instances

attachment

prepositional phrases

verb

noun

cross-validated

parameter

feature selection

train

algorithms

distinction

based

lexical features

feature

noun

attachment

attachment

decision

phrase

boundaries

prepositional phrases

noun

noun

attachment

attachment

decisions

yields

improvement

precision

phrase

boundary

attachment

prepositional phrases

feature selection

algorithms

lexical features

distinction

decisions

improvement


Input: A Distributed Architecture For Text Analysis In French: An Application To Complex Linguistic Phenomena Processing Most Natural Language Processing systems use a sequential architecture embodying classical linguistic layers. When one works with a general language; and not a sublanguage, there are different cases of ambiguities at different classical levels; and more particularly when one works on complex language phenomena analysis (coordination, ellipsis, negation...) it is difficult to take into account all the different types of these constructions with a general grammar. Indeed, the inconvenience of this approach is the possible risk of a combinatory explosion. So, we have defined the TALISMAN architecture that includes linguistic agents that correspond either to classical levels in linguistics (morphology, syntax, semantic) or to complex language phenomena analysis.

Distributed

Architecture

Text Analysis

Application

Complex

Processing

Natural Language Processing systems

architecture

layers

language

cases

ambiguities

levels

complex

language

phenomena

analysis

coordination

ellipsis

negation

types

constructions

approach

explosion

architecture

includes

agents

levels

linguistics

morphology

syntax

semantic

complex

language

phenomena

analysis

Architecture

Text Analysis

layers

Natural Language Processing systems

ambiguities

language

agents

architecture

explosion

approach


Input: Automatic Thesaurus Generation Through Multiple Filtering In this paper, we propose a method of generating bilingual keyword clusters or thesauri from parallel or comparable bilingual corpora. The method combines morphological and lexical processing, bilingual word alignment, and graph-theoretic cluster generation. An experiment shows that the method is promising.

Automatic

Thesaurus

Generation

paper

propose

method

generating

keyword

clusters

thesauri

corpora

method

lexical

processing

word alignment

cluster

generation

experiment

method

paper

method

processing

method


Input: Implicit Ambiguity Resolution Using Incremental Clustering In Korean-To-English Cross-Language Information Retrieval This paper presents a method to implicitly resolve ambiguities using dynamic incremental clustering in Korean-to-English cross-language information retrieval. In the framework we propose, a query in Korean is first translated into English by looking up Korean-English dictionary, then documents are retrieved based on the vector space retrieval for the translated query terms. For the top-ranked retrieved documents, query-oriented document clusters are incrementally created and the weight of each retrieved document is re-calculated by using clusters. In experiment on TREC-6 CLIR test collection, our method achieved 28. 29% performance improvement for translated queries without ambiguity resolution for queries. This corresponds to 97. 27% of the monolingual performance for original queries. When we combine our method with query ambiguity resolution, our method even outperforms the monolingual retrieval.

Ambiguity

Resolution

English

Cross-

Language

Information Retrieval

paper

method

ambiguities

clustering

English

cross-language

information retrieval

framework

propose

query

translated

English

English

dictionary

documents

based

vector

space

retrieval

translated

query

terms

documents

query-oriented

document

clusters

weight

document

clusters

experiment

test

collection

method

performance improvement

translated

queries

ambiguity

resolution

queries

performance

queries

method

query

ambiguity

resolution

method

retrieval

paper

method

clustering

ambiguities

dictionary

query

retrieval

documents

clusters

weight

clusters

documents

method

performance improvement

method

retrieval


Input: An Algorithmic Framework For Solving The Decoding Problem In Statistical Machine Translation The decoding problem in Statistical Machine Translation (SMT) is a computationally hard combinatorial optimization problem. In this paper, we propose a new algorithmic framework for solving the decoding problem and demonstrate its utility. In the new algorithmic framework, the decoding problem can be solved both exactly and approximately. The key idea behind the framework is the modeling of the decoding problem as one that involves alternating maximization of two relatively simpler subproblems. We show how the subproblems can be solved efficiently and how their solutions can be combined to arrive at a solution for the decoding problem. A family of provably fast decoding algorithms can be derived from the basic techniques underlying the framework and we present a few illustrations. Our first algorithm is a prov-ably linear time search algorithm. We use this algorithm as a subroutine in the other algorithms. We believe that decoding algorithms derived from our framework can be of practical significance.

Framework

Solving

Problem

Statistical Machine Translation

problem

Statistical Machine Translation

optimization problem

paper

propose

framework

solving

problem

utility

framework

problem

solved

framework

modeling

problem

maximization

solved

solutions

solution

problem

algorithms

basic

techniques

framework

algorithm

time

search algorithm

algorithm

algorithms

algorithms

framework

significance

Framework

Problem

framework

problem

algorithm

algorithms

maximization

problem

search algorithm

algorithm

techniques

algorithms

algorithms

significance


Input: A Flexible Example Annotation Schema: Translation Corresponding Tree Representation This paper presents work on the task of constructing an example base from a given bilingual corpus based on the annotation schema of Translation Corresponding Tree (TCT). Each TCT describes a translation example (a pair of bilingual sentences). It represents the syntactic structure of source language sentence, and more importantly is the facility to specify the correspondences between string (both the source and target sentences) and the representation tree. Furthermore, syntax transformation clues are also encapsulated at each node in the TCT representation to capture the differentiation of grammatical structure between the source and target languages. With this annotation schema, translation examples are effectively represented and organized in the bilingual knowledge database that we need for the Portuguese to Chinese machine translation system.

Example

Schema

Translation

Tree

Representation

paper

task

constructing

example

base

corpus

based

schema

Translation

Tree

translation

example

pair

sentences

syntactic structure

source language

sentence

correspondences

string

source

target

sentences

representation

tree

syntax

transformation

node

representation

structure

source

target languages

schema

translation

examples

knowledge

database

Chinese

machine translation system

schema

base

syntactic structure

sentence

tree

string

structure

target languages

node

representation

examples

database


Input: Deep Linguistic Analysis For The Accurate Identification Of Predicate-Argument Relations This paper evaluates the accuracy of HPSG parsing in terms of the identification of predicate-argument relations. We could directly compare the output of HPSG parsing with Prop-Bank annotations, by assuming a unique mapping from HPSG semantic representation into PropBank annotation. Even though PropBank was not used for the training of a disambiguation model, an HPSG parser achieved the accuracy competitive with existing studies on the task of identifying PropBank annotations.

Linguistic Analysis

Identification

Argument

Relations

paper

evaluates

accuracy

parsing

terms

identification

predicate-argument

relations

output

parsing

Prop-

Bank

mapping

semantic representation

training

disambiguation

model

parser

accuracy

studies

task

Linguistic Analysis

Relations

accuracy

parsing

mapping

semantic representation

output

Bank

parser

studies


Input: Pearl: A Probabilistic Chart Parser This paper describes a natural language parsing algorithm for unrestricted text which uses a probability-based scoring function to select the " " best " " parse of a sentence. The parser, Pearl, is a time-asynchronous bottom-up chart parser with Earley-type top-down prediction which pursues the highest-scoring theory in the chart, where the score of a theory represents the extent to which the context, of the sentence predicts that interpretation. This parser differs from previous attempts at stochastic parsers in that it uses a richer form of conditional probabilities based on context, to predict, likelihood.' Pearl also provides a framework for incorporating the results of previous work in pait-ol'-speech assignment, unknown word models, and other probabilistic models of linguistic features into one parsing tool, interleaving these techniques instead of using the traditional pipeline architecture. In preliminary tests,' Pearl has been successful at resolving part-of-speech and word (in speech processing) ambiguity, determining categories for unknown words, and selecting correct parses first using a very loosely fitting covering grammar. "

Parser

paper

natural language

parsing

algorithm

text

probability-based

function

parse

sentence

parser

time-asynchronous

parser

Earley-type

prediction

theory

theory

extent

context

sentence

interpretation

parser

parsers

form

conditional probabilities

based

context

provides

framework

results

speech

assignment

word models

probabilistic models

linguistic features

parsing

tool

techniques

pipeline

architecture

tests

part-of-speech

word

speech processing

ambiguity

categories

unknown words

parses

paper

algorithm

parse

sentence

interpretation

extent

categories

unknown words

prediction

parser

context

conditional probabilities

results

tool

parser

parsers


Input: Relationships between Nursing Converstaions and Activities In this paper, we determine the relationships between nursing activities and nurseing conversations based on the principle of maximum entropy. For analysis of the features of nursing activities, we built nursing corpora from actual nursing conversation sets collected in hospitals that involve various information about nursing activities. Ex-nurses manually assigned nursing activity information to the nursing conversations in the corpora. Since it is inefficient and too expensive to attach all information manually, we introduced an automatic nursing activity determination method for which we built models of relationships between nursing conversations and activities. In this paper, we adopted a maximum entropy approach for learning. Even though the conversation data set is not large enough for learning, acceptable results were obtained.

paper

relationships

conversations

based

principle

maximum entropy

analysis

features

corpora

conversation

information

information

conversations

corpora

information

automatic

determination

method

models

relationships

conversations

paper

maximum entropy

approach

conversation

data

results

analysis

features

paper

approach

conversation

corpora

information

conversations


Input: Forest-based Translation Rule Extraction Translation rule extraction is a fundamental problem in machine translation, especially for linguistically syntax-based packed forest

Forest-based

Translation

Rule

Extraction

Translation

rule

extraction

problem

machine translation

translation

syntax-based

forest

extraction

machine translation


Input: Automatic induction of FrameNet lexical units Most attempts to integrate FrameNet in NLP systems have so far failed because of its limited coverage. In this paper, we investigate the applicability of distributional and WordNet-based models on the task of lexical unit induction,

Automatic

induction

lexical

units

NLP systems

coverage

paper

applicability

WordNet-based models

models

task

lexical

unit

induction

paper

applicability

WordNet-based models

induction


Input: Classification of Multiple-Sentence Questions Abstract.

Classification

Sentence

Questions

Abstract

Classification

Questions


Input: Using the Structure of a Conceptual Network in Computing Semantic Relatedness Abstract. proper

Structure

Network

Computing

Semantic

Relatedness

Abstract

Structure

Computing


Input: Investigating NLG Architectures: Taking Style Into Consideration In this paper we propose a methodology for investigating the relationship between architectures of natural language generation (NLG)

Consideration

paper

propose

methodology

relationship

architectures

natural language generation

paper

methodology


Input: Automatic Evaluation For A Palpable Measure Of A Speech Translation System' s Capability The main goal of this paper is to propose automatic schemes for the translation paired comparison method. This method was proposed to precisely evaluate a speech translation system' s capability. Furthermore, the method gives an objective evaluation result, i. e., a score of the Test of English for International Communication (TOEIC). The TOEIC score is used as a measure of one' s speech translation capability. However, this method requires tremendous evaluation costs. Accordingly, automatization of this method is an important subject for study. In the proposed method, currently available automatic evaluation methods are applied to automate the translation paired comparison method. In the experiments, several automatic evaluation methods (BLEU, NIST, DP-based method) are applied. The experimental results of these automatic measures show a good correlation with evaluation results of the translation paired comparison method.

Automatic Evaluation

Evaluation

Translation System

System

Capability

main

goal

paper

propose

automatic

schemes

translation

paired

comparison

method

method

proposed

evaluate

speech translation

system

capability

method

objective

evaluation result

result

Test

English

Communication

speech translation

capability

method

requires

evaluation

costs

method

study

proposed

method

evaluation methods

methods

applied

translation

paired

comparison

method

experiments

automatic evaluation

methods

DP-based method

applied

experimental

results

automatic

correlation

evaluation results

results

translation

paired

comparison

method

Automatic Evaluation

Translation System

method

speech translation

evaluation methods

method

paper

schemes

method

evaluation result


Input: Arabic Syntactic Trees: From Constituency To Dependency This research note reports on the work in progress which regards automatic transformation of phrase-structure syntactic trees of Arabic into dependency-driven analytical ones. Guidelines for these descriptions have been developed at the Linguistic Data Consortium, University of Pennsylvania, and at the Faculty of Mathematics and Physics and the Faculty of Arts, Charles University in Prague, respectively. The transformation consists of (i) a recursive function translating the topology of a phrase tree into a corresponding dependency tree, and (ii) a procedure assigning analytical functions to the nodes of the dependency tree. Apart from an outline of the annotation schemes and a deeper insight into these procedures, model application of the transformation is given herein.

Syntactic

Constituency

Dependency

research

note

reports

progress

automatic

transformation

phrase-structure

syntactic

trees

dependency-driven

Guidelines

descriptions

developed

Data

University

Faculty

Faculty

University

transformation

function

translating

topology

phrase

tree

dependency tree

procedure

functions

nodes

dependency tree

tree

outline

schemes

insight

procedures

model

application

transformation

research

transformation

Guidelines

descriptions

translating

tree

nodes

dependency tree

model

transformation


Input: Latent Semantic Information In Maximum Entropy Language Models For Conversational Speech Recognition Latent semantic analysis (LSA), first exploited in indexing documents for information retrieval, has since been used by several researchers to demonstrate impressive reductions in the perplexity of statistical language models on text corpora such as the Wall Street Journal. In this paper we present an investigation into the use of LSA in language modeling for conversational speech recognition. We find that previously proposed methods of combining an LSA-based unigram model with an N-feature

Semantic Information

Maximum Entropy

Language

Models

Speech Recognition

Recognition

Latent semantic analysis

semantic analysis

indexing

documents

information retrieval

retrieval

researchers

reductions

perplexity

statistical language models

text

corpora

Wall Street Journal

paper

investigation

language modeling

modeling

speech recognition

recognition

proposed

methods

model

feature

Models

Speech Recognition

Latent semantic analysis

information retrieval

language modeling

speech recognition

paper

investigation


Input: A Categorial Variation Database For English " We describe our approach to the construction and evaluation of a large-scale database called " " CatVar " " which contains categorial variations of English lexemes. Due to the prevalence of cross-language categorial variation in multilingual applications, our categorial-variation resource may serve as an integral part of a diverse range of natural language applications. Thus, the research reported herein overlaps heavily with that of the machine-translation, lexicon-construction, and information-retrieval communities. We apply the information-retrieval metrics of precision and recall to evaluate the accuracy and coverage of our database with respect to a human-produced gold standard. This evaluation reveals that the categorial database achieves a high degree of precision and recall. Additionally, we demonstrate that the database improves on the linkability of Porter stemmer by over 30%. "

Variation

Database

English

approach

construction

evaluation

large-scale

database

called

variations

English

Due

cross-language

variation

applications

categorial-variation

resource

part

natural language

applications

research

reported

machine-translation

lexicon-construction

information-retrieval

communities

apply

information-retrieval

metrics

precision

recall

evaluate

accuracy

coverage

database

respect

gold standard

standard

evaluation

database

degree

precision

recall

database

improves

variations

database

resource

applications

accuracy

gold standard


Input: GE NLTOOLSET: MUC-3 Test Results And Analysis This paper reports on the GE NLTOOLSET customization effort for MUC-3, and analyzes the results of the TST2 run. Although our own tests had shown steady improvement between TST1 and TST2, our official scores on TST2 were lo wer than on TST1. The analysis of this unexpected result explains some of the details of th e MUC-3 test, and we propose ways of looking at the scores to distinguish different aspects of system performance.

Test

Results

Analysis

paper

reports

effort

results

tests

improvement

analysis

result

details

test

propose

aspects

system

performance

analysis

result


Input: Text Filtering In MUC-3 And MUC-4 " One of the changes from the Third (MUC-3) to the Fourth (MUC-4) Message Understanding Conference was the emergence of text filtering as an explicit topic of discussion. In this paper we examine text filtering in MUC systems with three goals in mind. First, we clarify the difference between two uses of the term " " text filtering " " in the context of data extraction systems, and put these phenomena in the context of prior research on information retrieval (IR). Secondly, we discuss the use of text filtering components in MUC-3 and MUC-4 systems, and present a preliminary scheme for classifying data extraction systems in terms of the features over which they do text filtering. Finally, we examine the text filtering effectiveness of MUC-3 and MUC-4 systems, and introduce some approaches to the evaluation of text filtering systems which may be of interest themselves. Two questions of crucial interest are whether sites improved their system level text filtering effectiveness from MUC-3 to MUC-4, and what the effectiveness of MUC systems would be on real world data streams. Because of changes in both test set and system design since MUC-3 we were not able to address the first question. However, with respect to the second question, we present preliminary evidence suggesting that the text filtering precision of MUC systems declines with the generality of the data stream they process, i. e. the proportion of relevant documents. The ramifications of this for future research and for operational systems are discussed. "

Text

Message

Understanding

text

topic

discussion

paper

text

systems

goals

difference

term

text

context

data

extraction systems

phenomena

context

research

information retrieval

text

components

systems

scheme

data

extraction systems

systems

terms

features

text

text

effectiveness

systems

approaches

evaluation

text

systems

questions

sites

improved

system

level

text

effectiveness

effectiveness

systems

real world

data

streams

test set

system

design

question

respect

question

evidence

text

precision

systems

generality

data

stream

process

documents

research

systems

paper

text

components

systems

scheme

extraction systems

approaches

evaluation

generality

precision


Input: Template Design For Information Extraction The design of the template for an information extraction application (or exercise) reflects the nature of the task and therefore crucially affects the success of the attempt to capture information from text. This paper addresses the template design requirement by discussing the general principles or template definition effort which is explicitly discussed in a Case Study in the last section of this paper.

Template

Design

Information Extraction

Extraction

design

template

information extraction

application

nature

task

affects

success

information

text

paper

template

design

requirement

principles

template

definition

effort

Case Study

Study

section

paper

Design

Information Extraction

template

application

paper

principles

section

Case Study


Input: Selection: Salience, Relevance And The Coupling Between Domain-Level Tasks And Text Planning In this paper we examine some issues pertaining to the task of selection in text planning. We attempt to distinguish salience and relevance, and characterize their role as important fundamental notions governing selection. We also formulate the problem of selection of text content in terms of the coupling between domain-level tasks and text planning tasks. We describe our research on generating bus route descriptions. Keywords: Natural Language Generation, Text Planning, Selection, Salience, Relevance, Coupling, Route Descriptions

Selection

Relevance

Domain-

Level

Text

paper

issues

task

selection

text

relevance

role

notions

selection

problem

selection

text

content

terms

domain-level

tasks

text

tasks

research

generating

route

descriptions

Natural Language Generation

Text

Selection

Relevance

Route

paper

issues

research

descriptions


Input: Using Cue Phrases To Determine Rhetorical Relations' Relation based' approaches to discourse analysis and text generation suffer from a common problem: there is considerable disagreement between researchers over the set of relations which is proposed. Few researchers use identical sets of relations, and many use relations not found in any other sets. This proliferation of relations has been pointed out before (eg Hovy [1]), and several methods for justifying a standard set of relations have been proposed: this paper reviews some of these, and presents a new method of justification which overcomes some awkward problems.

Cue

Relations

Relation

based

approaches

discourse analysis

analysis

text generation

common

problem

disagreement

researchers

relations

proposed

researchers

relations

relations

relations

methods

standard

relations

proposed

paper

reviews

method

justification

problems

approaches

discourse analysis

paper

method


Input: Domain Structure, Rhetorical Structure, And Text Structure It is generally agreed that text has structure (at least, coherent text does). Therefore, an understanding and appreciation of text structure must play some role in building computational systems that are capable of using text as people do. What is less clear is what are necessary and sufficient sources of structure for a text-using system, and further, what such a system needs to know about and do with these structures in the process of using text. By using text, I mean understanding it or producing it; speaking it, writing it, or thinking about it. Li this paper, I present a case for the importance of domain structure in structuring text, and discuss the role of rhetorical structure and intentionality.

Domain

Structure

Structure

Text Structure

text

structure

text

understanding

text structure

structure

role

computational

systems

text

sources

system

structures

process

text

text

understanding

paper

case

importance

domain

structure

structuring

text

role

structure

structure

text

understanding

text structure

paper

case

structure

text


Input: Corpus-Based Adaptation Mechanisms For Chinese Homophone Disambiguation Based on the concepts of bidirectional conversion and automatic evaluation, we propose two nser-adapiation mechanisms, character-preference learning and pstlido-word learning, for resolving Chinese homophone ambiguities in syllable-to-character conversion. The 1991 United Daily corpus oj approximately 10 million Chinese characters is used for extraction of 10 reporter-specific article databases and for computation of word frequencies and character bi-grams. Experiments show that 20. 5 percent (testing sets) to 71. 8 percent (training sets) of conversion errors can be eliminated through the proposed mechanisms. These concepts are thus very useful m applications such as Chinese input methods and speech recognition systems

Corpus-

Based

Adaptation

Chinese

Disambiguation

Based

concepts

conversion

automatic evaluation

propose

mechanisms

character-preference

learning

pstlido-word

learning

Chinese

ambiguities

conversion

corpus

Chinese

extraction

databases

computation

word

frequencies

Experiments

training sets

conversion

errors

proposed

mechanisms

concepts

applications

Chinese

input

methods

speech recognition systems

Adaptation

Disambiguation

ambiguities

conversion

databases

corpus


Input: Automatic Construction Of A Chinese Electronic Dictionary In this paper, an unsupervised approach for constructing a large-scale Chinese electronic dictionary is surveyed. The main purpose is to enable cheap and quick acquisition of a large-scale dictionary from a large untagged text corpus with the aid of the information in a small tagged seed corpus. The basic model is based on a Viterbi reestimation technique. During the dictionary construction process, it tries to optimize the automatic segmentation and tagging process by repeatedly refining the set of parameters of the underlying language model. The refined parameters are then used to further get a better tagging result. In addition, a two-class classifier, which is capable of classifying an n-gram either as a word or a non-word, is used in combination with the Viterbi training module to improve the system performance. Two different system configurations had been developed to construct the dictionary. The configurations include (1) a Viterbi word identification module followed by a Viterbi POS tagging module and (2) a two-class classification module as the postfilter for the above Viterbi word identification module. With a seed of 1, 000 sentences and an untagged corpus of 311, 591 sentences, the performance for bigram word identification is 56. 88% in precision and 77. 37% in recall when the two-class classifier is applied to the word list suggested by the Viterbi word identification module. The Viterbi part of speech tag reestimation stage gives the figures of 71. 16% and 71. 81% weighted precision rates and 73. 42% and 73. 83% weighted recall rates for the 2 different configurations when using a seed corpus of 9676 sentences.

Automatic

Construction

Chinese

Electronic Dictionary

paper

approach

constructing

large-scale

Chinese

electronic dictionary

surveyed

main

purpose

acquisition

large-scale

dictionary

text

corpus

information

tagged

seed

corpus

basic

model

based

technique

dictionary

construction

process

automatic

tagging

process

parameters

language model

model

parameters

tagging

result

addition

two-class

classifier

n-gram

word

non-word

combination

training

module

improve

system

performance

system

configurations

developed

construct

dictionary

configurations

include

word

identification

module

POS tagging

module

two-class

classification

module

word

identification

module

seed

sentences

corpus

sentences

performance

word

identification

precision

recall

two-class

classifier

applied

word

list

word

identification

module

part of speech tag

speech

tag

figures

precision

rates

recall

rates

configurations

seed

corpus

sentences

paper

approach

dictionary

corpus

information

corpus

module

configurations

technique

model

language model

process

parameters

tagging

classifier

n-gram

classifier

list

identification

precision

part of speech tag

rates


Input: A Geometric Approach To Mapping Bitext Correspondence The first step in most corpus-based multilingual NLP work is to construct a detailed map of the correspondence between a text and its translation. Several automatic methods for this task have been proposed in recent years. Yet even the best of these methods can err by several typeset pages. The Smooth Injective Map Recognizer (SIMR) is a new bitext mapping algorithm. SIMR' s errors are smaller than those of the previous front-runner by more than a factor of 4. Its robustness has enabled new commercial-quality applications. The greedy nature of the algorithm makes it independent of memory resources. Unlike other bitext mapping algorithms, SIMR allows crossing correspondences to account for word order differences. Its output can be converted quickly and easily into a sentence alignment. SIMR' s output has been used to align more than 200 megabytes of the Canadian Hansards for publication by the Linguistic Data Consortium.

Approach

Mapping

Correspondence

first step

corpus-based

construct

detailed

map

correspondence

text

translation

automatic

methods

task

proposed

methods

pages

Smooth

Map

mapping

algorithm

errors

factor

robustness

commercial-quality

applications

nature

algorithm

memory

resources

mapping

algorithms

crossing

correspondences

word

order

differences

output

sentence

alignment

output

Data

Approach

Mapping

map

correspondence


Input: The Measure Of A Model This paper describes measures for evaluating the three determinants of how well a probabilistic classifier performs on a given test set. These determinants are the appropriateness, for the test set, of the results of (1) feature selection, (2) formulation of the parametric form of the model, and (3) parameter estimation. These are part of any model formulation procedure, even if not broken out as separate steps, so the tradeoffs explored in this paper are relevant to a wide variety of methods. The measures are demonstrated in a large experiment, in which they are used to analyze the results of roughly 300 classifiers that perform word-sense disambiguation.

Model

paper

evaluating

classifier

performs

test set

appropriateness

test set

results

feature selection

formulation

form

model

parameter estimation

part

model

formulation

procedure

steps

paper

variety

methods

experiment

results

classifiers

perform

word-sense

word-sense disambiguation

disambiguation

classifier

test set

classifiers

word-sense disambiguation

appropriateness

results


Input: Approximate Generation From Non-Hierarchical Representations This paper presents a technique for sentence generation. We argue that the input to generators should have a non-hierarchical nature. This allows us to investigate a more general version of the sentence generation problem where one is not pre-committed to a choice of the syntactically prominent elements in the initial semantics. We also consider that a generator can happen to convey more (or less) information than is originally specified in its semantic input. In order to constrain this approximate matching of the input we impose additional restrictions on the semantics of the generated sentence. Our technique provides flexibility to address cases where the entire input cannot be precisely expressed in a single sentence. Thus the generator does not rely on the strategic component having linguistic knowledge. We show clearly how the semantic structure is declaratively related to linguistically motivated syntactic representation.

Generation

paper

technique

sentence generation

input

generators

nature

version

sentence generation

problem

choice

semantics

generator

information

semantic

input

order

input

restrictions

semantics

generated

sentence

technique

provides

flexibility

cases

input

sentence

generator

component

linguistic knowledge

semantic structure

structure

syntactic representation

representation

paper

technique

semantic structure

syntactic representation


Input: Input Specification In The WAG Sentence Generation System This paper describes the input specification language of the WAG Sentence Generation system. The input is described in terms of Halliday' s (1978) three meaning components, ideational meaning (the propositional content to be expressed), interactional meaning (what the speaker intends the listener to do in making the utterance), and textual meaning (how the content is structured as a message, in terms of theme, reference, etc.).

Input

Specification

Sentence Generation

System

paper

input

specification

language

Sentence Generation

system

input

terms

components

content

listener

utterance

content

structured

message

terms

reference

paper

language


Input: Dealing With Multilinguality In A Spoken Language Query Translator Robustness is an important issue for multilingual speech interfaces for spoken language translation systems. We have studied three aspects of robustness in such a system: accent differences, mixed language input, and the use of common feature sets for HMM-based speech recognizers for English and Cantonese. The results of our preliminary experiments show that accent differences cause recognizer performance to degrade. A rather surprising finding is that for mixed language input, a straight forward implementation of a mixed language model-based speech recognizer performs less well than the concatenation of pure language recognizers. Our experimental results also show that a common feature set, parameter set, and common algorithm lead to different performance output for Cantonese and English speech recognition modules.

Dealing

Spoken Language

Query

Translator

Robustness

issue

speech

interfaces

language

translation systems

systems

studied

aspects

robustness

system

differences

language

input

common

feature sets

speech

English

results

experiments

differences

performance

finding

language

input

implementation

language model-based

model-based

speech

performs

language

experimental

results

common

feature set

parameter

common

algorithm

performance

output

English

speech recognition

modules

interfaces

translation systems

differences

performance

common

output

language model-based

language


Input: English-To-Mandarin Speech Translation With Head Transducers We describe the head transducer model used in an experimental English-to-Mandarin speech translation system. Head transduction is a translation method in which weighted finite state transducers are associated with source-target word pairs. The method is suitable for speech translation because it allows efficient bottom up processing. The head transducers in the experimental system have a wider range of output positions than input positions. This asymmetry is motivated by a tradeoff between model complexity and search efficiency.

English-

Speech Translation

model

experimental

English-to-

translation system

system

transduction

translation

method

finite state transducers

source-target word

pairs

method

speech translation

translation

processing

experimental

system

output

input

model

complexity

search

efficiency

model

translation system

method

speech translation


Input: Filtering Errors And Repairing Linguistic Anomalies For Spoken Dialogue Systems Our work addresses the integration of speech recognition and language processing for whole spoken dialogue systems. To filter ill-recognized words, we design an on-line computing of word confidence scores based on the recognizer output hypothesis. To infer as much information as possible from the retained sequence of words, we propose a bottom-up syntactico-semantic robust parsing relying on a lexi-calized tree grammar and on integrated repairing strategies.

Spoken Dialogue

Systems

integration

speech recognition

language processing

processing

dialogue systems

systems

words

design

computing

word

confidence

based

output

hypothesis

information

sequence

words

propose

syntactico-semantic

robust

parsing

tree

repairing

strategies

language processing

dialogue systems

strategies

parsing


Input: A Programmable Multi-Blackboard Architecture For Dialogue Processing Systems In current Natural Language Processing Systems, different components for different processing tasks and input/output modalities have to be integrated. Once integrated, the interactions between the components have to be specified. Interactions in dialogue systems can be complex due in part to the many states the system can be in. When porting the system to another domain, parts of the integration process have to be repeated. To overcome these difficulties, we propose a multi-blackboard architecture that is controlled by a set of expert-system like rules. These rules may contain typed variables. Variables can be substituted by representations with an appropriate type stored in the blackboards. Furthermore, the representations in the blackboards allow to represent partial information and to leave disjunctions unresolved. Moreover, the conditions of the rule may depend on the specificity of the representations with which the variables are instantiated. For this reason, the interaction is information-driven. The described system has been implemented and has been integrated with the speech recognizer JANUS.

Architecture

Dialogue

Processing

Systems

current

Natural Language Processing

Systems

components

processing

tasks

input

output

modalities

interactions

components

dialogue systems

complex

due

part

system

porting

system

domain

parts

integration

process

difficulties

propose

architecture

expert-system

rules

rules

typed

variables

representations

type

representations

partial

information

disjunctions

rule

specificity

representations

variables

reason

interaction

information-driven

system

implemented

speech

Architecture

Systems

components

tasks

specificity

rule

variables

rules

representations

information


Input: Phrase Structure Trees Bear More Fruit Than You Would Have Thought In this paper we will present several results concerning phrase structure trees. These results show that phrase structure trees, when viewed in certain ways, have much more descriptive power than one would have thought. We have given a brief account of local constraints on structural descriptions and an intuitive proof of a theorem about local constraints. We have compared the local constraints approach to some aspects of Gazdar' s framework and that of Peters and Ritchie and of Karttunen. We have also presented some results on skeletons (phrase structure trees without labels) which show that phrase structure trees, even when deprived of the labels, retain in a certain sense all the structural information. This result has implications for grammatical inference procedures.

Phrase Structure

paper

results

concerning

phrase structure trees

results

phrase structure trees

constraints

structural

descriptions

theorem

constraints

constraints

approach

aspects

framework

results

phrase structure trees

phrase structure trees

structure

trees

sense

structural

information

result

implications

inference

procedures

paper

results

theorem

constraints

approach

framework

phrase structure trees

information


Input: Natural-Language Interface A major problem faced by would-be users of computer systems is that computers generally make use of special-purpose languages familiar only to those trained in computer science. For a large number of applications requiring interaction between humans and computer systems, it would be highly desirable for machines to converse in English or other natural languages familiar to their human users. Over the last decade, in laboratories around the world, several computer systems have been developed that support at least elementary levels of natural-language interaction. Among these are such systems as those described in the several references at the end of this paper.

Natural-

Language

Interface

problem

users

computer

systems

computers

special-purpose

languages

trained

computer science

number

applications

requiring

interaction

computer

systems

machines

English

natural languages

users

laboratories

computer

systems

developed

support

levels

natural-language

interaction

systems

references

paper

languages

computers

paper

references


Input: Combining Multiple, Large-Scale Resources in a Reusable Lexicon for Natural Language Generation A lexicon is an essential component in a generation system but few efforts have been made to build a rich, large-scale lexicon and make it reusable for different generation applications. In this paper, we describe our work to build such a lexicon by combining multiple, heterogeneous linguistic resources which have been developed for other purposes. Novel transformation and integration of resources is required to reuse them for generation. We also applied the lexicon to the lexical choice and realization component of a practical generation application by using a multi-level feedback architecture. The integration of the lexicon and the architecture is able to effectively improve the system paraphrasing power, minimize the chance of grammatical errors, and simplify the development process substantially.

Combining

Scale

Lexicon

Natural Language Generation

Language Generation

lexicon

component

generation system

system

efforts

large-scale

lexicon

generation

applications

paper

lexicon

linguistic resources

developed

purposes

transformation

integration

resources

required

generation

applied

lexicon

lexical choice

choice

realization

component

generation

application

multi-level

feedback

architecture

integration

lexicon

architecture

improve

system

errors

development

process

Lexicon

Natural Language Generation

lexicon

lexical choice

architecture

generation

lexicon

generation system

paper

lexicon

integration

errors


Input: A Method for Correcting Errors in Speech Recognition using the Statistical Features of Character Co-occurrence It is important to correct the errors in the results of speech recognition to increase the performance of a speech translation system. This paper proposes a method for correcting errors using the statistical features of character co-occurrence, and evaluates the method. The proposed method comprises two successive correcting processes. The first process uses pairs of strings: the first string is an erroneous substring of the utterance predicted by speech recognition, the second string is the corresponding section of the actual utterance. Errors are detected and corrected according to the database learned from erroneous-correct utterance pairs. The remaining errors are passed to the posterior process which uses a string in the corpus that is similar to the string including recognition errors. The results of our evaluation show that the use of our proposed method as a post-processor for speech recognition is likely to make a significant contribution to the performance of speech translation systems. method also obtains reliably recognized partial segments of an utterance by cooperatively using both grammatical and n-gram based statistical language constraints, and uses a robust parsing technique to apply the grammatical constraints described by context-free grammar (Tsukada

Method

Speech Recognition

Recognition

Statistical

Features

Co-occurrence

errors

results

speech recognition

increase

performance

translation system

paper

proposes

method

errors

statistical

features

co-occurrence

evaluates

method

proposed

method

processes

process

pairs

strings

string

utterance

speech recognition

recognition

string

section

utterance

database

utterance

pairs

errors

process

string

corpus

string

including

recognition

errors

results

evaluation

proposed

method

post-processor

speech recognition

recognition

contribution

performance

translation systems

method

partial

segments

utterance

n-gram

based

statistical

language

constraints

robust

parsing

technique

apply

constraints

context-free

Co-occurrence

Speech Recognition

method

errors

pairs

process

string

process

post-processor

speech recognition

errors

results

string

utterance

translation system

performance

speech recognition

string

method

segments

processes

method

pairs

database

errors

string


Input: Bitext Correspondences through Rich Mark-up Rich mark-up can considerably benefit the process of establishing bitext correspondences, that is, the task of providing correct identification and alignment methods for text segments that are translation equivalences of each other in a parallel corpus. We present a sentence alignment algorithm that, by taking advantage of previously annotated texts, obtains accuracy rates close to 100%. The algorithm evaluates the similarity of the linguistic and extra-linguistic mark-up in both sides of a bitext. Given that annotations are neutral with respect to typological, grammatical and orthographical differences between languages, rich mark-up becomes an optimal foundation to support bitext correspondences. The main originality of this approach is that it makes maximal use of annotations, which is a very sensible and efficient method for the exploitation of parallel corpora when annotations exist.

benefit

process

correspondences

task

providing

identification

alignment

methods

text

segments

translation

equivalences

parallel corpus

sentence

alignment

algorithm

advantage

texts

accuracy

rates

algorithm

evaluates

similarity

sides

respect

differences

languages

optimal

support

correspondences

main

approach

method

exploitation

parallel corpora

alignment

segments

algorithm

accuracy


Input: Translating a Unification Grammar with Disjunctions into Logical Constraints This paper proposes a method for generating a logical-constraint-based internal representation from a unification grammar formalism with disjunctive information. Unification grammar formalisms based on path equations and lists of pairs of labels and values are better than those based on first-order terms in that the former is easier to describe and to understand. Parsing with term-based internal representations is more efficient than parsing with graph-based representations. Therefore, it is effective to translate unification grammar formalism based on path equations and lists of pairs of labels and values into a term-based internal representation. Previous translation methods cannot deal with disjunctive feature descriptions, which reduce redundancies in the grammar and make parsing efficient. Since the proposed method translates a formalism without expanding disjunctions, parsing with the resulting representation is efficient.

Unification

paper

proposes

method

generating

logical-constraint-based

representation

unification

formalism

information

Unification

formalisms

based

path

equations

lists

pairs

based

first-order

terms

Parsing

term-based

representations

parsing

representations

translate

unification

formalism

based

path

equations

lists

pairs

term-based

representation

translation

methods

deal

feature

descriptions

redundancies

parsing

proposed

method

translates

formalism

disjunctions

parsing

resulting

representation

paper

method

representation

information

lists

formalisms

lists

formalism

Parsing

parsing


Input: ParaMetric: An Automatic Evaluation Metric for Paraphrasing We present ParaMetric, an automatic evaluation metric for data-driven approaches to paraphrasing. ParaMetric provides an objective measure of quality using a collection of multiple translations whose paraphrases have been manually annotated. ParaMetric calculates precision and recall scores by comparing the paraphrases discovered by automatic paraphrasing techniques against gold standard alignments of words and phrases within equivalent sentences. We report scores for several established paraphrasing techniques.

Automatic Evaluation

Metric

automatic evaluation

metric

data-driven

approaches

provides

objective

quality

collection

translations

precision

recall

automatic

techniques

gold standard

alignments

words

phrases

sentences

report

techniques

alignments

sentences


Input: Re-estimation of Lexical Parameters for Treebank PCFGs We present procedures which pool lexical information estimated from unlabeled data via the Inside-Outside algorithm, with lexical information from a treebank PCFG. The procedures produce substantial improvements (up to 31. 6% error reduction) on the task of determining subcategorization frames of novel verbs, relative to a smoothed Penn Treebank-trained PCFG. Even with relatively small quantities of unlabeled training data, the re-estimated models show promising improvements in labeled bracketing/scores on Wall Street Journal parsing, and substantial benefit in acquiring the subcategorization preferences of low-frequency verbs.

Re-estimation

Lexical

procedures

lexical information

information

data

algorithm

lexical information

procedures

improvements

error

reduction

task

frames

verbs

relative

smoothed

Penn Treebank-trained

quantities

training

models

improvements

bracketing

Wall Street Journal

parsing

benefit

preferences

low-frequency

verbs

lexical information

data

frames

Penn Treebank-trained

procedures

improvements


Input: Semantic Classification with Distributional Kernels Distributional measures of lexical similarity and kernel methods for classification are well-known tools in Natural Language Processing. We bring these two methods together by introducing distributional kernels

Semantic

Classification

lexical

similarity

kernel methods

methods

classification

tools

Natural Language Processing

methods

kernel methods

classification


Input: Domain Adaptation for Statistical Machine Translation with Domain Dictionary and Monolingual Corpora Statistical machine translation systems are usually trained on large amounts of bilingual text and monolingual text. In this paper, we propose a method to perform domain adaptation for statistical machine translation, where in-domain bilingual corpora do not exist. This method first uses out-of-domain corpora to train a baseline system and then uses in-domain translation dictionaries and in-domain monolingual corpora to improve the indomain performance. We propose an algorithm to combine these different resources in a unified framework. Experimental results indicate that our method achieves absolute improvements of 8. 16 and 3. 36 BLEU scores on Chinese to English translation and English to French translation respectively, as compared with the baselines using only out-of-domain corpora.

Domain Adaptation

Statistical Machine Translation

Domain

Dictionary

Corpora

Statistical machine translation systems

machine translation systems

trained

amounts

text

text

paper

propose

method

perform

domain adaptation

statistical machine translation

in-domain

corpora

method

out-of-domain

corpora

train

baseline system

in-domain

translation

dictionaries

in-domain

corpora

improve

performance

propose

algorithm

resources

framework

Experimental

results

method

improvements

Chinese

English

translation

English

translation

out-of-domain

corpora

text

Statistical machine translation systems

corpora

method

paper

method


Input: Sentence Type Based Reordering Model for Statistical Machine Translation Many reordering approaches have been proposed for the statistical machine translation (SMT) system. However, the information about the type of source sentence is ignored in the previous works. In this paper, we propose a group of novel reordering models based on the source sentence type for Chinese-to-English translation. In our approach, an SVM-based classifier is employed to classify the given Chinese sentences into three types: special interrogative sentences, other interrogative sentences, and non-question sentences. The different reordering models are developed oriented to the different sentence types. Our experiments show that the novel reordering models have obtained an improvement of more than 2. 65% in BLEU for a phrase-based spoken language translation system.

Sentence

Type

Based

Reordering Model

Model

Statistical Machine Translation

Machine Translation

approaches

proposed

statistical machine translation

system

information

type

source sentence

sentence

paper

propose

reordering models

models

based

source sentence

type

Chinese-to-

English

translation

approach

classifier

Chinese

sentences

types

sentences

sentences

non-question

sentences

reordering models

models

developed

sentence

types

experiments

reordering models

models

improvement

phrase-based

language

translation system

Reordering Model

Statistical Machine Translation

classifier

sentences

types

reordering models

information

source sentence

paper

reordering models

reordering models

improvement


Input: Automatic Processing Of Written French Language An automatic processor of written French language is described. This processor uses syntactic and semantic informations about words in order to construct a semantic net representing the meaning of the sentences. The structure of the network and the principles of the parser are explained. An application to the processing of the medical records is then discussed.

Automatic

Processing

Language

automatic

processor

language

processor

syntactic

semantic informations

informations

words

order

construct

semantic

sentences

structure

network

principles

parser

application

processing

records

processor

language

semantic informations

processor

processing

records


Input: Computer-Aided Grammatical Tagging Of Spoken English

Computer-

Tagging

English

Tagging

English


Input: A Rule-Based Approach To Ill-Formed Input Though natural language understanding systems have improved markedly in recent years, they have only begun to consider a major problem of truly natural input: ill-formedness. Quite often natural language input is ill-formed in the sense of being misspelled, ungrammatical, or not entirely meaningful. A requirement for any successful natural language interface must be that the system either intelligently guesses at a user' s intent, requests direct clarification, or at the very least, accurately identifies the ill-formedness. This paper presents a proposal for the proper treatment of ill-formed input. Our conjecture is that ill-formedness should be treated as rule-based. Violation of the rules of normal processing should be used to signal ill-formedness. Meta-rules modifying the rules of normal processing should be used for error identification and recovery. These meta-rules correspond to types of errors. Evidence for this conjecture is presented as well as some open questions.

Rule-

Based

Approach

Formed

Input

natural language understanding systems

improved

problem

natural

input

natural language

input

sense

requirement

natural language interface

system

user

clarification

paper

proposal

treatment

input

rule-based

rules

processing

signal

rules

processing

error

identification

recovery

types

errors

Evidence

questions

paper

proposal

rules

identification


Input: The Knowledge Representation For A Story Understanding And Simulation System TOYONAKA, OSAKA 560, JAPAN!!!! MATSUSHITA ELECTRIC INDUSTRIAL CO., LTD. KADOMA, OSAKA 571, JAPAN Abstruet

Knowledge Representation

Representation

Understanding

Simulation

System

Knowledge Representation

Understanding


Input: Language Resources and Chemical Informatics Chemistry research papers are a primary source of information about chemistry, as in any scientific field. The presentation of the data is, predominantly, unstructured information, and so not immediately susceptible to processes developed within chemical informatics for carrying out chemistry research by information processing techniques. At one level, extracting the relevant information from research papers is a text mining task, requiring both extensive language resources and specialised knowledge of the subject domain. However, the papers also encode information about the way the research is conducted and the structure of the field itself. Applying language technology to research papers in chemistry can facilitate eScience on several different levels. The SciBorg project sets out to provide an extensive, analysed corpus of published chemistry research. This relies on the cooperation of several journal publishers to provide papers in an appropriate form. The work is carried out as a collaboration involving the Computer Laboratory, Chemistry Department and eScience Centre at Cambridge University, and is funded under the UK eScience programme.

Language

Chemical

research

papers

source

information

field

presentation

data

information

processes

developed

chemical

research

information processing

techniques

level

extracting

information

research

papers

text

task

requiring

language resources

knowledge

domain

papers

information

research

structure

field

Applying

language technology

research

papers

levels

project

provide

corpus

research

cooperation

journal

provide

papers

form

collaboration

Computer

Laboratory

Department

University

papers

information

information

papers

information

papers

research

corpus


Input: MeSH: from a Controlled Vocabulary to a Processable Resource Large repositories of life science data in the form of domain-specific literature, textual databases and other large specialised textual collections (corpora) in electronic form increase on a daily basis to a level beyond the human mind can grasp and interpret. As the volume of data continues to increase, substantial support from new information technologies and computational techniques grounded in the form of the ever increasing applications of the mining paradigm

Vocabulary

Resource

repositories

science

data

form

domain-specific

literature

databases

collections

corpora

form

increase

basis

level

volume

data

increase

support

information technologies

computational

techniques

form

increasing

applications

paradigm

data

repositories


Input: An Evaluation of Spoken and Textual Interaction in the RITEL Interactive Question Answering System The RITEL project aims to integrate a spoken language dialogue system and an open-domain information retrieval system in order to enable human users to ask a general question and to refine their search for information interactively. This type of system is often referred to as an Interactive Question Answering (IQA) system. In this paper, we present an evaluation of how the performance of the RITEL system differs when users interact with it using spoken versus textual input and output. Our results indicate that while users do not perceive the two versions to perform significantly differently, many more questions are asked in a typical text-based dialogue.

Evaluation

Interaction

Question Answering System

Answering System

project

language

dialogue system

open-domain

information retrieval system

order

users

question

search

information

type

system

Question Answering

system

paper

evaluation

performance

system

users

input

output

results

users

versions

perform

questions

text-based

dialogue

Evaluation

Question Answering System

paper

evaluation

system

performance


Input: Automatic Prediction of Parser Accuracy Statistical parsers have become increasingly accurate, to the point where they are useful in many natural language applications. However, estimating parsing accuracy on a wide variety of domains and genres is still a challenge in the absence of gold-standard parse trees. In this paper, we propose a technique that automatically takes into account certain characteristics of the domains of interest, and accurately predicts parser performance on data from these new domains. As a result, we have a cheap (no annotation involved) and effective recipe for measuring the performance of a statistical parser on any given domain.

Automatic

Prediction

Parser

Accuracy

Statistical

parsers

natural language

applications

parsing

accuracy

variety

domains

genres

challenge

absence

gold-standard

parse trees

paper

propose

technique

characteristics

domains

parser

performance

data

domains

result

performance

statistical

parser

domain

paper

technique

parser

performance


Input: French-English Terminology Extraction from Comparable Corpora Abstract.

English

Terminology

Extraction

Corpora

Abstract

Terminology

Corpora


Input: Turn-taking in Mandarin Dialogue: Interactions of Tone and Intonation Fluent dialogue requires that speakers successfully negotiate and signal turn-taking. While many cues to turn change have been proposed, especially in multi-modal frameworks, here we focus on the use ofprosodic cues to these functions. In particular, we consider the use of prosodic cues in a tone language, Mandarin Chinese, where variations in pitch height and slope additionally serve to determine word meaning. Within a corpus of spontaneous Chinese dialogues, we find that turn-unit final syllables are significantly lower in average pitch and intensity than turn-unit initial syllables in both smooth turn changes and segments ended by speaker overlap. Interruptions are characterized by significant prosodic differences from smooth turn initiations. Furthermore, we demonstrate that these contrasts correspond to an overall lowering across all tones in final position, which largely preserves the relative heights of the lexical tones. In classification tasks, we contrast the use of text and prosodic features. Finally, we demonstrate that, on balanced training and test sets, we can distinguish turn-unit final words from other words at 93% accuracy and interruptions from smooth turn unit initiations at 62% accuracy.

Dialogue

Intonation

dialogue

requires

signal

cues

proposed

frameworks

focus

cues

functions

cues

language

Chinese

variations

pitch

word meaning

corpus

Chinese

dialogues

turn-unit

pitch

turn-unit

smooth

segments

differences

smooth

contrasts

relative

lexical

classification tasks

contrast

text

features

training

test sets

turn-unit

words

words

accuracy

smooth

unit

accuracy

variations

pitch

dialogues

corpus


Input: Multi-Modal Combinatory Categorial Grammar The paper shows how Combinatory Categorial Grammar (CCG) can be adapted to take advantage of the extra resource-sensitivity provided by the Categorial Type Logic framework. The resulting reformulation, Multi-Modal CCG, supports lexically specified control over the applicability of combinatory rules, permitting a universal rale component and shedding the need for language-specific restrictions on rules. We discuss some of the linguistic motivation for these changes, define the Multi-Modal CCG system and demonstrate how it works on some basic examples. We furthermore outline some possible extensions and address computational aspects of Multi-Modal CCG.

Categorial Grammar

paper

Categorial Grammar

adapted

advantage

resource-sensitivity

provided

Type

Logic

framework

resulting

supports

control

applicability

rules

component

language-specific

restrictions

rules

motivation

system

basic

examples

outline

extensions

computational

aspects

paper

Categorial Grammar


Input: Automatic Acquisition Of Script Knowledge From A Text Collection In this paper, we describe a method for automatic acquisition of script knowledge from a Japanese text collection. Script knowledge represents a typical sequence of actions that occur in a particular situation. We extracted sequences (pairs) of actions occurring in time order from a Japanese text collection and then chose those that were typical of certain situations by ranking these sequences (pairs) in terms of the frequency of their occurrence. To extract sequences of actions occurring in time order, we constructed a text collection in which texts describing facts relating to a similar situation were clustered together and arranged in time order. We also describe a preliminary experiment with our acquisition system and discuss the results.

Automatic

Acquisition

Script

Knowledge

Text

Collection

paper

method

automatic

acquisition

script

knowledge

Japanese

text

collection

Script

knowledge

sequence

actions

situation

extracted

sequences

pairs

actions

time

order

Japanese

text

collection

situations

ranking

sequences

pairs

terms

frequency

occurrence

extract

sequences

actions

time

order

constructed

text

collection

texts

situation

clustered

time

order

experiment

acquisition

system

results

Knowledge

Collection

knowledge

collection

sequences

collection

paper

method

frequency

occurrence


Input: Comparing Automatic And Human Evaluation Of NLG Systems We consider the evaluation problem in Natural Language Generation (nlg) and present results for evaluating several nlg systems with similar functionality, including a knowledge-based generator and several statistical systems. We compare evaluation results for these systems by human domain experts, human non-experts, and several automatic evaluation metrics, including nist, bleu, and rouge. We find that nist scores correlate best (> 0. 8) with human judgments, but that all automatic metrics we examined are biased in favour of generators that select on the basis of frequency alone. We conclude that automatic evaluation of nlg systems has considerable potential, in particular where high-quality reference texts and only a small number of human evalua-tors are available. However, in general it is probably best for automatic evaluations to be supported by human-based evaluations, or at least by studies that demonstrate that a particular metric correlates well with human judgments in a given domain.

Automatic

Evaluation

Systems

evaluation

problem

Natural Language Generation

Language Generation

results

evaluating

systems

functionality

including

knowledge-based

generator

statistical

systems

evaluation results

systems

domain

experts

evaluation metrics

including

rouge

judgments

automatic

metrics

biased

generators

basis

frequency

automatic evaluation

evaluation

systems

high-quality

reference

texts

number

automatic evaluations

supported

evaluations

studies

metric

judgments

domain

Evaluation

Systems

automatic evaluation

systems

evaluation

Natural Language Generation

biased

metrics


Input: Word Alignment With Cohesion Constraint We present a syntax-based constraint for word alignment, known as the cohesion constraint. It requires disjoint English phrases to be mapped to non-overlapping intervals in the French sentence. We evaluate the utility of this constraint in two different algorithms. The results show that it can provide a significant improvement in alignment quality.

Word Alignment

Alignment

Cohesion

Constraint

syntax-based

constraint

word alignment

cohesion

constraint

requires

English

phrases

mapped

intervals

sentence

evaluate

utility

constraint

algorithms

results

provide

improvement

alignment

quality

Cohesion

Word Alignment


Input: Bayesian Nets For Syntactic Categorization Of Novel Words This paper presents an application of a Dynamic Bayesian Network (DBN) to the task of assigning Part-of-Speech (PoS) tags to novel text. This task is particularly challenging for non-standard corpora, such as Internet lingo, where a large proportion of words are unknown. Previous work reveals that PoS tags depend on a variety of morphological and contextual features. Representing these dependencies in a DBN results into an elegant and effective PoS tagger.

Syntactic

Categorization

Words

paper

application

Network

task

Part-of-

Speech

tags

text

task

challenging

non-standard

corpora

words

PoS tags

tags

variety

contextual features

features

dependencies

results

Categorization

Words

tags

text

paper

application

contextual features

PoS tags


Input: Detecting Structural Metadata With Decision Trees And Transformation-Based Learning The regular occurrence of disfluencies is a distinguishing characteristic of spontaneous speech. Detecting and removing such disfluencies can substantially improve the usefulness of spontaneous speech transcripts. This paper presents a system that detects various types of disfluencies and other structural information with cues obtained from lexical and prosodic information sources. Specifically, combinations of decision trees and language models are used to predict sentence ends and interruption points and, given these events, transformation-based learning is used to detect edit disfluencies and conversational fillers. Results are reported on human and automatic transcripts of conversational telephone speech.

Structural

Decision

Transformation-

Based

occurrence

characteristic

spontaneous speech

improve

usefulness

spontaneous speech

transcripts

paper

system

types

structural

information

cues

lexical

information sources

sources

combinations

decision trees

language models

sentence

events

transformation-based

learning

fillers

Results

reported

automatic

transcripts

telephone

speech

paper

system

cues

information sources

transcripts

speech


Input: Feature-Based Pronunciation Modeling For Speech Recognition We present an approach to pronunciation modeling in which the evolution of multiple linguistic feature streams is explicitly represented. This differs from phone-based models in that pronunciation variation is viewed as the result of feature asynchrony and changes in feature values, rather than phone substitutions, insertions, and deletions. We have implemented a flexible feature-based pronunciation model using dynamic Bayesian networks. In this paper, we describe our approach and report on a pilot experiment using phonetic transcriptions of utterances from the Switchboard corpus. The experimental results, as well as the model' s qualitative behavior, suggest that this is a promising way of accounting for the types of pronunciation variation often seen in spontaneous speech.

Feature-

Based

Pronunciation

Modeling

Speech Recognition

Recognition

approach

pronunciation

modeling

evolution

linguistic feature

streams

phone-based models

pronunciation

variation

result

feature

feature values

phone

substitutions

insertions

deletions

implemented

feature-based

pronunciation

model

networks

paper

approach

report

pilot

experiment

transcriptions

utterances

corpus

experimental

results

model

behavior

accounting

types

pronunciation

variation

spontaneous speech

Modeling

Speech Recognition

networks

model

paper

approach

transcriptions

corpus