Input: A LAF/GrAF based Encoding Scheme for underspecified Representations of syntactic Annotations. Data models and encoding formats for syntactically annotated text corpora need to deal with syntactic ambiguity; underspecified representations are particularly well suited for the representation of ambiguousdata because they allow for high informational efficiency. We discuss the issue of being informationally efficient, and the trade-off between efficient encoding of linguistic annotations and complete documentation of linguistic analyses. The main topic of this article is adata model and an encoding scheme based on LAF/GrAF (Ide and Romary, 2006; Ide and Suderman, 2007) which provides a flexible framework for encoding underspecified representations. We show how a set of dependency structures and a set of TiGer graphs (Brants et al., 2002) representing the readings of an ambiguous sentence can be encoded, and we discuss basic issues in querying corpora which are encoded using the framework presented here.

based

Scheme

syntactic

Data

models

formats

text

corpora

deal

syntactic

ambiguity

representations

representation

ambiguousdata

efficiency

issue

documentation

linguistic analyses

main

topic

adata

model

scheme

based

Ide

Ide

provides

framework

representations

dependency structures

TiGer

sentence

basic

issues

querying

corpora

framework

representations

representation

framework

representations

documentation

linguistic analyses

dependency structures

sentence

issues

querying

framework

corpora


Input: A Study of Parentheticals in Discourse Corpora-Implications for NLG Systems This paper presents a corpus study of parenthetical constructions in two different corpora: the Penn Discourse Treebank (PDTB, (PDTB-Group, 2008)) and the RST Discourse Treebank (Carlson et al., 2001). The motivation for the study is to gain a better understanding of the rhetorical properties of parentheticals in order to enable a natural language generation system to produce parentheticals as part of a rhetorically well-formed output. We argue that there is a correlation between syntactic and rhetorical types of parentheticals and establish two main categories: elaboration/expansion-type NP-modifier parentheticals and non-elaboration/expansion-type VP-or S-modifier parentheticals. We show several strategies for extracting these from the two corpora and discuss how the seemingly contradictory results obtained can be reconciled in light of the rhetorical and syntactic properties of parentheticals as well as the decisions taken in the annotation guidelines.

Study

Discourse

Corpora

Systems

paper

corpus

study

constructions

corpora

Discourse

Discourse

motivation

study

gain

understanding

properties

order

natural language generation system

part

output

correlation

syntactic

types

main

categories

expansion-type

NP-modifier

expansion-type

modifier

strategies

extracting

corpora

results

syntactic

properties

decisions

guidelines

paper

study

understanding

properties

constructions

corpora

natural language generation system

output

properties

results

correlation

types

strategies

extracting


Input: Machine Learning Approach to Augmenting News Headline Generation In this paper, we present the HybridTrim system which uses a machine learning technique to combine linguistic, statistical and positional information to identify topic labels for headlines in a text. We compare our system with the Topiary system which, in contrast, uses a statistical learning approach to finding topic descriptors for headlines. The Topiary system, developed at the University of Maryland with BBN, was the top performing headline generation system at DUC 2004. Topiary-style headlines consist of a number of general topic labels followed by a compressed version of the lead sentence of a news story. The Topiary system uses a statistical learning approach to finding topic labels. The performance of these systems is evaluated using the ROUGE evaluation suite on the DUC 2004 news stories collection.

Machine Learning Approach

News

Headline

Generation

paper

system

machine

technique

statistical

information

topic

headlines

text

system

system

contrast

statistical

learning approach

topic

headlines

system

developed

University

headline

generation system

headlines

number

topic

version

sentence

news

system

statistical

learning approach

topic

performance

systems

evaluated

ROUGE

evaluation

suite

news

collection

Machine Learning Approach

Generation

information

technique

learning approach

system

collection

ROUGE

paper

system

headlines

text

system

system

topic

headlines

systems

performance


Input: Inducing History Representations For Broad Coverage Statistical Parsing We present a neural network method for inducing representations of parse histories and using these history representations to estimate the probabilities needed by a statistical left-corner parser. The resulting statistical parser achieves performance (89. 1% F-measure) on the Penn Treebank which is only 0. 6% below the best current parser for this task, despite using a smaller vocabulary size and less prior linguistic knowledge. Crucial to this success is the use of structurally determined soft biases in inducing the representation of the parse history, and no use of hard independence assumptions.

Coverage

Statistical

Parsing

neural network

method

representations

parse

representations

probabilities

statistical

parser

resulting

statistical

parser

performance

Penn Treebank

current

parser

task

vocabulary

size

linguistic knowledge

success

biases

representation

parse

independence

assumptions

method

representations

representations

probabilities

performance

parser

biases

success


Input: Precision And Recall Of Machine Translation Machine translation can be evaluated using precision, recall, and the F-measure. These standard measures have significantly higher correlation with human judgments than recently proposed alternatives. More importantly, the standard measures have an intuitive interpretation, which can facilitate insights into how MT systems might be improved. The relevant software is publicly available.

Precision

Recall

Machine Translation

Machine translation

evaluated

precision

recall

standard

correlation

judgments

proposed

alternatives

standard

interpretation

insights

MT systems

improved

software

Machine Translation

Recall

judgments

alternatives

insights

MT systems


Input: Learning Morphological Disambiguation Rules For Turkish In this paper, we present a rule based model for morphological disambiguation of Turkish. The rules are generated by a novel decision list learning algorithm using supervised training. Morphological ambiguity (e. g. lives = live + s or life + s) is a challenging problem for agglutinative languages like Turkish where close to half of the words in running text are morphologically ambiguous. Furthermore, it is possible for a word to take an unlimited number of suffixes, therefore the number of possible morphological tags is unlimited. We attempted to cope with these problems by training a separate model for each of the 126 morphological features recognized by the morphological analyzer. The resulting decision lists independently vote on each of the potential parses of a word and the final parse is selected based on our confidence on these votes. The accuracy of our model (96%) is slightly above the best previously reported results which use statistical models. For comparison, when we train a single decision list on full tags instead of using separate models on each feature we get 91% accuracy.

Learning

Disambiguation

paper

rule

based

model

disambiguation

rules

generated

decision

list

algorithm

training

ambiguity

challenging

problem

languages

words

text

word

number

suffixes

number

tags

problems

training

model

morphological features

analyzer

resulting

decision

lists

parses

word

parse

based

confidence

accuracy

model

reported

results

statistical models

comparison

train

decision

list

tags

models

feature

accuracy

model

disambiguation

algorithm

training

ambiguity

languages

suffixes

word

number

tags

model

morphological features

parses

word

confidence

parse

words

text

tags

list

accuracy

results


Input: SRA SOLOMON: MUC-4 Test Results And Analysis In this paper, we report SRA' s results on the MUC-4 task and describe how we trained our natural language processing system for MUC-4. We also report on what worked, what didn' t work, and lessons learned. Our MUC-4 system embeds the SOLOMON knowledge-based NLP shell which is designed for both and We are currently using SOLOMON for a Spanish and Japanese text understanding project in a different domain. Although this was our first year participating in MUC, we have built and are currently building otherdata extraction systems.

Test

Results

Analysis

paper

report

results

task

trained

natural language processing system

report

system

knowledge-based

designed

Japanese

text

understanding

project

domain

otherdata

extraction systems

paper

results


Input: Corpus-Based Method For Automatic Identification Of Support Verbs For Nominalizations Nominalization is a highly productive phenomena in most languages. The process of nominalization ejects a verb from its syntactic role into a nominal position. The original verb is often replaced by a semantically emptied support verb (e. g., make a proposal).

Corpus-

Based

Method

Automatic

Identification

Support

phenomena

languages

process

verb

syntactic

role

verb

support

verb

proposal

Method

Identification

phenomena

languages

role

verb

verb

verb


Input: Aggressive Morphology For Robust Lexical Coverage This paper describes an approach to providing lexical information for natural language processing in unrestricted domains. A system of approximately 1200 morphological rules is used to extend a core lexicon of 39, 000 words to provide lexical coverage that exceeds that of a lexicon of 80, 000 words or 150, 000 word forms. The morphological system is described, and lexical coverage is evaluated for random words chosen from a previously unanalyzed corpus.

Morphology

Robust

Lexical

Coverage

paper

approach

providing

lexical information

natural language processing

domains

system

rules

core

lexicon

words

provide

lexical

coverage

lexicon

words

word

forms

system

lexical

coverage

evaluated

words

corpus

Morphology

Coverage

lexical information

natural language processing

rules

system

paper

approach

words

lexicon

words

corpus


Input: Predicting Automatic Speech Recognition Performance Using Prosodic Cues In spoken dialogue systems, it is important for a system to know how likely a speech recognition hypothesis is to be correct, so it can reprompt for fresh input, or, in cases where many errors have occurred, change its interaction strategy or switch the caller to a human attendant. We have discovered prosodie features which more accurately predict when a recognition hypothesis contains a word error than the acoustic confidence score thresholds traditionally used in automatic speech recognition. We present analytic results indicating that there are significant prosodie differences between correctly and incorrectly recognized turns.

Automatic Speech Recognition

Performance

dialogue systems

system

speech recognition

hypothesis

input

cases

errors

interaction

strategy

features

recognition

hypothesis

word

error

confidence

thresholds

automatic speech recognition

results

differences

hypothesis

dialogue systems

thresholds

automatic speech recognition

errors

strategy

differences

results

error

hypothesis


Input: Evaluating The Use Of Prosodic Information In Speech Recognition And Understanding The goal of this project is to investigate the use of different levels of prosodie information in speech recognition and understanding. In particular, the current focus of the work is the use of prosodie phrase boundary information in parsing. The research involves determining a representation of prosodie information suitable for use in a speech understanding system, developing reliable algorithms for detection of the prosodie cues in speech, investigating architectures for integrating prosodie cues in a parser, and evaluating the potential improvements of prosody in the context of the SRI Spoken Language System. This research is sponsored jointly by DARPA and NSF.

Evaluating

Information

Speech Recognition

Understanding

goal

project

levels

information

speech recognition

understanding

current

focus

phrase

boundary

information

parsing

research

representation

information

speech understanding system

developing

algorithms

detection

cues

speech

architectures

cues

parser

evaluating

improvements

prosody

context

Spoken Language System

research

Information

Speech Recognition

information

parsing

information

speech understanding system

algorithms

detection

cues

parser

project

information

research

representation

cues

speech

Spoken Language System

improvements


Input: Prediction Of Lexicalized Tree Fragments In Text There is a mismatch between the distribution of information in text, and a variety of grammatical formalisms for describing it, including ngrams, context-free grammars, and dependency grammars. Rather than adding probabilities to existing grammars, it is proposed to collect the distributions of flexibly sized partial trees. These can be used to enhance an ngram model, and in analogical parsing.

Prediction

Tree

Text

mismatch

distribution

information

text

variety

formalisms

including

context-free

dependency grammars

probabilities

proposed

distributions

sized

partial

trees

model

parsing

Tree

Text

information

text

variety

formalisms

distributions

trees


Input: The COMLEX Syntax Project " Developing more shareable resources to support natural language analysis will make it easier and cheaper to create new language processing applications and to support research in computational linguistics. One natural candidate for such a resource is a broad-coverage dictionary, since the work required to create such a dictionary is large but there is general agreement on at least some of the information to be recorded for each word. The Linguistic Data Consortium has begun an effort to create several such lexical resources, under the rubric " " COMLEX " " (COMmon LEXicon); one of these projects is the COMLEX Syntax Project. The goal of the COMLEX Syntax Project is to create a moderately-broad-coverage shareable dictionary containing the syntactic features of English words, intended for automatic language analysis. We are initially aiming for a dictionary of 35, 000 to 40, 000 base forms, although this of course may be enlarged if the initial effort is positively received. The dictionary should include detailed syntactic specifications, particularly for subcategorization; our intent is to provide sufficient detail so that the information required by a number of major English analyzers can be automatically derived from the information we provide. As with other Linguistic Data Consortium resources, our intent is to provide a lexicon available without license constraint to all Consortium members. Finally, our goal is to provide an initial lexicon relatively quickly within about a year, funding permitting. This implies a certain flexibility, where some of the features will probably be changed and refined as the coding is taking place. "

Syntax

Project

Developing

resources

support

natural language

analysis

language processing

applications

support

research

computational linguistics

natural

candidate

resource

broad-coverage

dictionary

required

dictionary

agreement

information

recorded

word

Data

effort

lexical resources

COMmon

LEXicon

projects

Syntax

Project

goal

Syntax

Project

moderately-broad-coverage

dictionary

syntactic features

English

words

automatic

language analysis

dictionary

base

forms

effort

dictionary

include

syntactic

specifications

provide

detail

information

required

number

English

analyzers

information

provide

Data

resources

provide

lexicon

license

constraint

goal

provide

lexicon

flexibility

features

coding

resources

analysis

information

analyzers

research

computational linguistics

Project

dictionary

information

word

syntactic features

words

forms

dictionary

specifications

dictionary


Input: An Evaluation Of Strategies For Selective Utterance Verification For Spoken Natural Language Dialog As with human-human interaction, spoken human-computer dialog will contain situations where there is miscommunication. In experimental trials consisting of eight different users, 141 problem-solving dialogs, and 2840 user utterances, the Circuit Fix-It Shop natural language dialog system misinterpreted 18. 5% of user utterances. These miscommunications created various problems for the dialog interaction, ranging from repetitive dialog to experimenter intervention to occasional failure of the dialog. One natural strategy for reducing the impact of miscommunication is selective verification of the user' s utterances. This paper reports on both context-independent and context-dependent strategies for utterance verification that show that the use of dialog context is crucial for intelligent selection of which utterances to verify.

Evaluation

Strategies

Utterance

Verification

Natural Language

Dialog

interaction

human-computer

dialog

situations

experimental

users

problem-solving

dialogs

user

utterances

natural language

dialog system

user

utterances

problems

dialog

interaction

dialog

dialog

natural

strategy

impact

verification

user

utterances

paper

reports

context-independent

context-dependent

strategies

utterance

verification

dialog

context

selection

utterances

Strategies

Verification

dialog system

utterances

verification

utterances

context

selection

situations

dialog

paper

strategies


Input: Computing Prosodic Properties In A Data-To-Speech System We propose a set of rules for the computation of prosody which are implemented in an existing generic Data-to-Speech system. The rules make crucial use of both sentence-internal and sentence-external semantic and syntactic information provided by the system. In a Text-to-Speech system, this information would have to be obtained through text analysis, but in Data-to-Speech it is readily available, and its reliable and detailed character makes it possible to compute the prosodie properties of generated sentences in a sophisticated way. This in turn allows for a close control of prosodie realization, resulting in natural-sounding intonation.

Computing

Data-

Speech

System

propose

rules

computation

prosody

implemented

Data-to-

Speech

system

rules

sentence-internal

sentence-external

semantic

syntactic information

provided

system

Text-to-

Speech

system

information

text analysis

Data-to-

Speech

compute

properties

generated

sentences

control

realization

resulting

natural-sounding

intonation

rules

computation

syntactic information

rules

text analysis

information

properties

sentences


Input: Standardisation Efforts On The Level Of Dialogue Act In The MATE Project This paper describes the state of the art of coding schemes for dialogue acts and the efforts to establish a standard in this field. We present a review and comparison of currently available schemes and outline the comparison problems we had due to domain, task, and language dependencies of schemes. We discuss solution strategies which have in mind the reusability of corpora. Reusability is a crucial point because production and annotation of corpora is very time and cost consuming but the current broad variety of schemes makes reusability of annotated corpora very hard. The work of this paper takes place in the framework of the European Union funded MATE project. MATE aims to develop general methodological guidelines for the creation, annotation, retrieval and analysis of annotated corpora.

Efforts

Level

Dialogue Act

MATE

Project

paper

coding

schemes

dialogue acts

efforts

standard

field

review

comparison

schemes

outline

comparison

problems

due

domain

task

language

dependencies

schemes

solution

strategies

corpora

corpora

time

cost

current

variety

schemes

corpora

paper

framework

MATE

project

MATE

develop

guidelines

creation

retrieval

analysis

corpora

schemes

dialogue acts

comparison

schemes

guidelines

creation

dependencies

schemes

variety

schemes


Input: ADAM-An Architecture For XML-Based Dialogue Annotation On Multiple Levels In this paper annotation modularity and use of annotation meta-schemes are identified as basic requirements for achieving actual corpora reusability. We discuss these concepts and the way they are implemented in the architectural framework of the ADAM corpus, which is a corpus of 450 Italian spontaneous dialogues. The design of ADAM architecture is compatible with as many practices of dialogue annotation as possible, as well as approaches to annotation at different levels.

Architecture

Dialogue

paper

basic

requirements

corpora

concepts

implemented

framework

corpus

corpus

dialogues

design

architecture

practices

dialogue

approaches

levels

dialogues

corpus


Input: Annotating Information Structures In Chinese Texts Using HowNet This paper reported our work on annotating Chinese texts with information structures derived from HowNet. An information structure consists of two components: HowNet definitions and dependency relations. It is the unit of representation of the meaning of texts. This work is part of a multi-sentential approach to Chinese text understanding. An overview of HowNet and information structure are described in this paper.

Information

Structures

Chinese

Texts

paper

reported

Chinese

texts

information structures

information structure

components

definitions

dependency relations

unit

representation

texts

part

approach

Chinese

text

understanding

overview

information structure

paper

Structures

Texts

components

information structure

information structures

texts

unit

texts

approach

understanding

paper

overview


Input: Training A Dialogue Act Tagger For Human-Human And Human-Computer Travel Dialogues While dialogue acts provide a useful schema for characterizing dialogue behaviors in human-computer and human-human dialogues, their utility is limited by the huge effort involved in hand-labelling dialogues with a dialogue act labelling scheme. In this work, we examine whether it is possible to fully automate the tagging task with the goal of enabling rapid creation of corpora for evaluating spoken dialogue systems and comparing them to human-human dialogues. We report results for training and testing an automatic classifier to label the information provider' s utterances in spoken human-computer and human-human dialogues with DATE (Dialogue Act Tagging for Evaluation) dialogue act tags. We train and test the DATE tagger on various combinations of the DARPA Communicator June-2000 and October-2001 human-computer corpora, and the CMU human-human corpus in the travel planning domain. Our results show that we can achieve high accuracies on the humancomputer data, and surprisingly, that the human-computerdata improves accuracy on the human-human data, when only small amounts of human-human trainingdata are available.

Training

Dialogue Act

Computer

dialogue acts

provide

schema

dialogue

behaviors

human-computer

dialogues

utility

limited

effort

hand-labelling

dialogues

dialogue act

scheme

tagging

task

goal

creation

corpora

evaluating

dialogue systems

dialogues

report

results

training

testing

automatic

classifier

information

utterances

human-computer

dialogues

DATE

Dialogue Act

Tagging

Evaluation

dialogue act

tags

train

test

DATE

combinations

human-computer

corpora

corpus

domain

results

accuracies

human-computerdata

computerdata

improves

accuracy

amounts

trainingdata

trainingdata

dialogue acts

schema

creation

evaluating

DATE

corpora

behaviors

dialogues

utterances

dialogues

effort

utility

classifier

results

computerdata

accuracy

scheme

dialogues

tags

dialogue act

dialogue systems

dialogues


Input: Machine Translation As A Testbed For Multilingual Analysis We propose that machine translation (MT) is a useful application for evaluating and deriving the development of NL components, especially in a wide-coverage analysis system. Given the architecture of our MT system, which is a transfer system based on linguistic modules, correct analysis is expected to be a prerequisite for correct translation, suggesting a correlation between the two, given relatively mature transfer and generation components. We show through error analysis that there is indeed a strong correlation between the quality of the translated output and the subjectively determined goodness of the analysis. We use this correlation as a guide for development of a coordinated parallel analysis effort in 7 languages.

Machine Translation

Analysis

propose

machine translation

application

evaluating

development

components

wide-coverage

analysis

system

architecture

MT system

transfer

system

based

modules

analysis

translation

correlation

transfer

generation

components

error analysis

correlation

quality

translated

output

analysis

correlation

development

analysis

effort

languages

Analysis

Machine Translation

components

machine translation

modules

system

analysis

translation

correlation

analysis

output

analysis


Input: A Simple Named Entity Extractor Using AdaBoost This paper presents a Named Entity Extraction (NEE) system for the CoNLL-2003 shared task competition. As in the past year edition (Carreras et al., 2002a), we have approached the task by treating the two main sub-tasks of the problem, recognition (NER) and classification (NEC), sequentially and independently with separate modules. Both modules are machine learning based systems, which make use of binary and multiclass AdaBoost classifiers. Named Entity recognition is performed as a greedy sequence tagging procedure under the well-known BIO labelling scheme. This tagging process makes use of three binary classifiers trained to be experts

Simple

Named

Entity

Extractor

paper

Named

Entity

Extraction

system

task

competition

approached

task

main

problem

recognition

classification

modules

modules

machine

based

systems

classifiers

Named

Entity

recognition

performed

sequence

tagging

procedure

scheme

tagging

process

classifiers

trained

experts

paper

system

recognition

task

classifiers

systems

procedure

recognition

classifiers

process


Input: WHAT: An XSLT-Based Infrastructure For The Integration Of Natural Language Processing Components The idea of the Whiteboard project is to integrate deep and shallow natural language processing components in order to benefit from their synergy. The project came up with the first fully integrated hybrid system consisting of a fast HPSG parser that utilizes tokenization, PoS, morphology, lexical, named entity, phrase chunk and (for German) topological sentence field analyses from shallow components. This integration increases robustness, directs the search space and hence reduces processing time of the deep parser. In this paper, we focus on one of the central integration facilities, the XSLT-based Whiteboard Annotation Transformer (WHAT), report on the benefits of XSLT-based NLP component integration, and present examples of XSL transformation of shallow and deep annotations used in the integrated architecture. The infrastructure is open, portable and well suited for, but not restricted to the development of hybrid NLP architectures as well as NLP applications.

Infrastructure

Integration

Natural Language Processing

project

natural language processing

components

order

benefit

project

system

parser

morphology

lexical

named

entity

phrase

chunk

sentence

field

analyses

components

integration

increases

robustness

search space

processing

time

parser

paper

focus

integration

report

benefits

component

integration

examples

transformation

architecture

infrastructure

development

architectures

NLP applications

Infrastructure

Integration

morphology

parser

infrastructure

development

project

system

integration

robustness

integration

benefits

paper

integration


Input: Detecting Sub-Topic Correspondence Through Bipartite Term Clustering This paper addresses a novel task of detecting sub-topic correspondence in a pair of text fragments, enhancing common notions of text similarity. This task is addressed by coupling corresponding term subsets through bipartite clustering. The paper presents a cost-based clustering scheme and compares it with a bipartite version of the single-link method, providing illustrating results.

Topic

Correspondence

Term

paper

task

sub-topic

correspondence

pair

text

fragments

common

notions

text

similarity

task

term

clustering

paper

cost-based

clustering

scheme

version

single-link

method

providing

results

paper

task

paper

scheme

correspondence

fragments

clustering

task


Input: Can we Relearn an RBMT System? This paper describes SYSTRAN submissions for the shared task of the third Workshop on Statistical Machine Translation at ACL. Our main contribution consists in a French-English statistical model trained without the use of any human-translated parallel corpus. In substitution, we translated a monolingual corpus with SYSTRAN rule-based translation engine to produce the parallel corpus. The results are provided herein, along with a measure of error analysis.

System

paper

submissions

task

Workshop

Statistical Machine Translation

main

contribution

English

statistical model

trained

parallel corpus

substitution

translated

corpus

rule-based

translation

engine

parallel corpus

results

provided

error analysis

paper

submissions

Workshop

Statistical Machine Translation

contribution

statistical model

engine

parallel corpus


Input: An Automatic Procedure For Topic-Focus Identification The dichotomy of topic and focus, based, in the Praguean Functional Generative Description, on the scale of communicative dynamism, is relevant not only for a possible placement of the sentence in a context, but also for its semantic interpretation. An automatic identification of topic and focus may use the input information on word order, on the systemic ordering of kinds of complementations (reflected by the underlying order of the items included in the focus), on definiteness, and on lexical semantic properties of words. An algorithm for the analysis of English sentences has been implemented and is discussed and illustrated on several examples.

Automatic

Procedure

Topic-

Focus

Identification

topic

focus

based

Description

scale

sentence

context

semantic interpretation

automatic

identification

topic

focus

input

information

word

order

ordering

kinds

order

items

included

focus

lexical

semantic properties

words

algorithm

analysis

English

sentences

implemented

examples

Procedure

Identification

information

identification

algorithm

analysis

semantic interpretation

sentence

order

items

semantic properties

words


Input: Efficient Multilingual Phoneme-To-Grapheme Conversion Based On HMM Grapheme-to-phoneme conversion (GTPC) has been achieved in most European languages by dictionary look-up or using rules. The application of these methods, however, in the reverse process, (i. e., in phoneme-to-grapheme conversion [PTGC]) creates serious problems, especially in inflectionally rich languages. In this paper the PTGC problem is approached from a completely different point of view. Instead of rules or a dictionary, the statistics of language connecting pronunciation to spelling are exploited. The novelty lies in modeling the natural language intraword features using the theory of hidden Markov models (HMM) and performing the conversion using the Viterbi algorithm. The PTGC system has been established and tested on various multilingual corpora. Initially, the first-order HMM and the common Viterbi algorithm were used to obtain a single transcription for each word. Afterwards, the second-order HMM and the N-best algorithm adapted to PTGC were implemented to provide one or more transcriptions for each word input (homophones). This system gave an average score of more than 99% correctly transcribed words (overall success in the first four candidates) for most of the seven languages it was tested on (Dutch, English, French, German, Greek, Italian, and Spanish). The system can be adapted to almost any language with little effort and can be implemented in hardware to serve in real-time speech recognition systems.

Phoneme-

Conversion

Based

Grapheme-to-phoneme

conversion

languages

dictionary

rules

application

methods

process

phoneme-to-grapheme

conversion

problems

languages

paper

problem

approached

point of view

rules

dictionary

statistics

language

pronunciation

spelling

novelty

modeling

natural language

features

theory

Markov models

performing

conversion

algorithm

system

tested

corpora

first-order

common

algorithm

transcription

word

second-order

algorithm

adapted

implemented

provide

transcriptions

word

input

system

words

success

candidates

languages

tested

English

Greek

system

adapted

language

effort

implemented

real-time

speech recognition systems

conversion

languages

methods

process

algorithm

conversion

system

corpora

algorithm

transcription

system

language

problems

languages

Markov models

features

transcriptions

input

paper

problem

rules

statistics

system

words


Input: Should Computers Write Spoken Language?

Computers

Spoken Language


Input: From Information Structure to Intonation: A Phonological Interface for Concept-to-Speech The paper describes an interface between generator and synthesizer of the German language concept-to-speech system VieCtoS. It discusses phenomena in German intonation that depend on the interaction between grammatical dependencies (projection of information structure into syntax) and prosodie context (performance-related modifications to intonation patterns). Phonological processing in our system comprises segmental as well as suprasegmental dimensions such as syllabification, modification of word stress positions, and a symbolic encoding of intonation. Phonological phenomena often touch upon more than one of these dimensions, so that mutual accessibility of thedata structures on each dimension had to be ensured. We present a linear representation of the multidimensional phonologicaldata based on a straightforward linearization convention, which suffices to bring this conceptually multilineardata set under the scope of the well-known processing techniques for two-level morphology.

Information Structure

Intonation

Interface

Concept-to-

Speech

paper

interface

generator

language

concept-to-speech

system

phenomena

intonation

interaction

dependencies

projection

information structure

syntax

context

performance-related

modifications

intonation

patterns

processing

system

dimensions

modification

word

intonation

phenomena

dimensions

thedata

structures

dimension

representation

phonologicaldata

based

convention

multilineardata

scope

processing

techniques

two-level

morphology

paper

interface

generator

system

phenomena

intonation

dimensions

processing

interaction

dependencies

dimensions

phenomena

representation

phonologicaldata

morphology

multilineardata


Input: An Intelligent Multi-Dictionary Environment An open, extendible multi-dictionary system is introduced in the paper. It supports the translator in accessing adequate entries of various bi-and monolingual dictionaries and translation examples from parallel corpora. Simultaneously an unlimited number of dictionaries can be held open, thus by a single interrogation step, all the dictionaries (translations, explanations, synonyms, etc.) can be surveyed. The implemented system (called MoBiDic) knows morphological rules of the dictionaries' languages. Thus, never the actual (inflected) words, but always their lemmas-that is, the right dictionary entries-are looked up. MoBiDic has an open, multimedial architecture, thus it is suitable for handling not only textual, but speaking or picture dictionaries, as well. The same system is also able to find words and expressions in corpora, dynamically providing the translators with examples from their earlier translations or other translators' works. MoBiDic has been designed for translator workgroups, where the translators' own glossaries (built also with the help of the system) may also be disseminated among the members of the group, with different access rights, if needed. The system has a TCP/IP-based client-server implementation for various platforms and available with a gradually increasing number of dictionaries for numerous language pairs.

Dictionary

Environment

multi-dictionary

system

paper

supports

translator

accessing

entries

dictionaries

translation

examples

parallel corpora

number

dictionaries

step

dictionaries

translations

explanations

synonyms

surveyed

implemented

system

called

rules

dictionaries

languages

words

lemmas

dictionary

entries

architecture

dictionaries

system

words

expressions

corpora

providing

translators

examples

translations

translators

designed

translator

translators

help

system

access

system

client-server

implementation

platforms

increasing

number

dictionaries

language pairs

paper

system

entries

dictionaries

examples

parallel corpora

expressions

corpora

translations

examples

rules

system

architecture

dictionaries

lemmas

words


Input: A Lemmatization Method for Modern Mongolian and its Application to Information Retrieval In Modern Mongolian, a content word can be inflected when concatenated with suffixes. Identifying the original forms of content words is crucial for natural language processing and information retrieval. We propose a lemmatization method for Modern Mongolian and apply our method to indexing for information retrieval. We use technical abstracts to show the effectiveness of our method experimentally.

Method

Application

Information Retrieval

content word

suffixes

Identifying

forms

content words

natural language processing

information retrieval

propose

method

apply

method

indexing

information retrieval

abstracts

effectiveness

method

Method

Information Retrieval

method

indexing

method

abstracts

forms

content words


Input: A Robust And Hybrid Deep-Linguistic Theory Applied To Large-Scale Parsing Modern statistical parsers are robust and quite fast, but their output is relatively shallow when compared to formal grammar parsers. We suggest to extend statistical approaches to a more deep-linguistic analysis while at the same time keeping the speed and low complexity of a statistical parser. The resulting parsing architecture suggested, implemented and evaluated here is highly robust and hybrid on a number of levels, combining statistical and rule-based approaches, constituency and dependency grammar, shallow and deep processing, full and near-full parsing. With its parsing speed of about 300, 000 words per hour and state-of-the-art performance the parser is reliable for a number of large-scale applications discussed in the article.

Robust

Linguistic Theory

Applied

Scale

Parsing

statistical

parsers

robust

output

parsers

statistical

approaches

deep-linguistic analysis

time

speed

complexity

statistical

parser

resulting

parsing

architecture

implemented

evaluated

levels

statistical

rule-based approaches

constituency

dependency grammar

processing

parsing

parsing

speed

words

performance

parser

number

large-scale

applications

Linguistic Theory

Parsing

parser

applications

parsers

parsers

complexity

parser


Input: Semi-Automatic Generation Of Dialogue Applications In The GEMINI Project GEMINI (Generic Environment for Multilingual Interactive Natural Interfaces) is an EC funded research project, which has two main objectives: First, the development of a flexible platform able to produce user-friendly interactive multilingual and multi-modal dialogue interfaces to databases with a minimum of human effort, and, second, the demonstration of the platform' s efficiency through the development of two different applications based on this platform: EG-Banking, a voice-portal for high-quality interactions for bank customers, and CitizenCare, an e-government platform framework for citizen-to-administration interaction which are available for spoken and web-based user interaction.

Automatic

Generation

Dialogue

Applications

Project

Environment

Natural

research project

main

objectives

development

platform

user-friendly

dialogue

interfaces

databases

minimum

effort

demonstration

platform

efficiency

development

applications

based

platform

high-quality

interactions

bank

customers

platform

framework

interaction

user

interaction

research project

development

platform

dialogue

platform

applications

framework

interaction

interfaces

databases

efficiency

platform


Input: Strategies For Advanced Question Answering Progress in Question Answering can be achieved by (1) combining multiple strategies that optimally resolve different question classes of various degrees of complexity; (2) enhancing the precision of question interpretation and answer extraction; and (3) question decomposition and answer fusion. In this paper we also present the impact of modeling the user background on Q/A and discuss the pragmatics pf processing negation in Q/A.

Strategies

Question Answering

Progress

Question Answering

strategies

question

classes

degrees

complexity

precision

question

interpretation

extraction

question

decomposition

fusion

paper

impact

modeling

user

background

processing

negation

Strategies

Question Answering

strategies

Question Answering

complexity

classes

interpretation

precision

paper

impact


Input: Morphology Induction From Term Clusters We address the problem of learning a morphological automaton directly from a monolingual text corpus without recourse to additional resources. Like previous work in this area, our approach exploits orthographic regularities in a search for possible morphological segmentation points. Instead of affixes, however, we search for affix transformation rules that express correspondences between term clusters induced from thedata. This focuses the system on substrings having syntactic function, and yields cluster-to-cluster transformation rules which enable the system to process unknown morphological forms of known words accurately. A stem-weighting algorithm based on Hubs and Authorities is used to clarify ambiguous segmentation points. We evaluate our approach using the CELEX database.

Morphology

Induction

Term

problem

text

corpus

resources

area

approach

regularities

search

search

transformation

rules

correspondences

term

clusters

thedata

focuses

system

syntactic function

yields

cluster-to-cluster

transformation

rules

system

process

forms

words

stem-weighting

algorithm

based

evaluate

approach

database

regularities

approach

system

forms

database

approach

rules

correspondences

clusters

thedata


Input: Cross Language Text Categorization By Acquiring Multilingual Domain Models From Comparable Corpora In a multilingual scenario, the classical monolingual text categorization problem can be reformulated as a cross language TC English Italian). English), Italian).

Cross

Language

Text Categorization

Domain

Models

Corpora

scenario

text categorization

problem

cross

language

English

English

Models

Text Categorization


Input: Chunk Parsing Revisited Chunk parsing is conceptually appealing but its performance has not been satisfactory for practical use. In this paper we show that chunk parsing can perform significantly better than previously reported by using a simple sliding-window method and maximum entropy classifiers for phrase recognition in each level of chunking. Experimental results with the Penn Treebank corpus show that our chunk parser can give high-precision parsing outputs with very high speed (14 msec/sentence). We also present a parsing method for searching the best parse by considering the probabilities output by the maximum entropy classifiers, and show that the search method can further improve the parsing accuracy.

Chunk

Parsing

Chunk

parsing

performance

paper

chunk

parsing

perform

reported

simple

sliding-window

method

maximum entropy

classifiers

phrase

recognition

level

chunking

Experimental

results

Penn Treebank

corpus

chunk

parser

high-precision

parsing

outputs

speed

sentence

parsing

method

searching

parse

probabilities

output

maximum entropy

classifiers

search

method

improve

parsing

accuracy

method

parsing

classifiers

recognition

method

searching

parser

outputs

method

accuracy


Input: Automatic Knowledge Representation Using A Graph-Based Algorithm For Language-Independent Lexical Chaining Lexical Chains are powerful representations of documents. In particular, they have successfully been used in the field of Automatic Text Summarization. However, until now, Lexical Chaining algorithms have only been proposed for English. In this paper, we propose a greedy Language-Independent algorithm that automatically extracts Lexical Chains from texts. For that purpose, we build a hierarchical lexico-semantic knowledge base from a collection of texts by using the Pole-Based Overlapping Clustering Algorithm. As a consequence, our methodology can be applied to any language and proposes a solution to language-dependent Lexical Chainers.

Automatic

Knowledge Representation

Based

Algorithm

Language-

Lexical

Lexical Chains

representations

documents

field

Automatic

Text Summarization

Lexical

algorithms

proposed

English

paper

propose

Language-

algorithm

extracts

Lexical Chains

texts

purpose

lexico-semantic knowledge

base

collection

texts

Based

Clustering Algorithm

methodology

applied

language

proposes

solution

language-dependent

Lexical

Algorithm

Knowledge Representation

algorithms

English

methodology

language

Lexical Chains

documents

paper

algorithm

Lexical Chains

texts

base

texts


Input: The Role Of Lexical Resources In CJK Natural Language Processing The role of lexical resources is often understated in NLP research. The complexity of Chinese, Japanese and Korean (CJK) poses special challenges to developers of NLP tools, especially in the area of word segmentation (WS), information retrieval (IR), named entity extraction (NER), and machine translation (MT). These difficulties are exacerbated by the lack of comprehensive lexical resources, especially for proper nouns, and the lack of a standardized orthography, especially in Japanese. This paper summarizes some of the major linguistic issues in the development NLP applications that are dependent on lexical resources, and discusses the central role such resources should play in enhancing the accuracy of NLP tools.

Role

Lexical

Natural Language

Processing

role

lexical resources

research

complexity

Chinese

Japanese

challenges

developers

tools

area

word segmentation

information retrieval

named

entity

extraction

machine translation

difficulties

lack

lexical resources

nouns

lack

Japanese

paper

issues

development

NLP applications

lexical resources

role

resources

accuracy

tools

research

lexical resources

paper

issues

complexity

Chinese

lack

Japanese

lexical resources

NLP applications

resources

accuracy


Input: Multilingual Collocation Extraction: Issues And Solutions Although traditionally seen as a language-independent task, collocation extraction relies nowadays more and more on the linguistic preprocessing of texts (e. g., lemmatization, POS tagging, chunking or parsing) prior to the application of statistical measures. This paper provides a language-oriented review of the existing extraction work. It points out several language-specific issues related to extraction and proposes a strategy for coping with them. It then describes a hybrid extraction system based on a multilingual parser. Finally, it presents a case-study on the performance of an association measure across a number of languages.

Collocation

Extraction

Issues

language-independent

task

collocation

extraction

texts

POS tagging

chunking

parsing

application

statistical

paper

provides

language-oriented

review

extraction

language-specific

issues

extraction

proposes

strategy

extraction system

based

parser

case-study

performance

association

number

languages

POS tagging

extraction

parser

extraction system

paper

review

case-study

performance

issues

extraction


Input: Building Effective Question Answering Characters In this paper, we describe methods for building and evaluation of limited domain question-answering characters. Several classification techniques are tested, including text classification using support vector machines, language-model based retrieval, and cross-language information retrieval techniques, with the latter having the highest success rate. We also evaluated the effect of speech recognition errors on performance with users, finding that retrieval is robust until recognition reaches over 50% WER.

Building

Question Answering

paper

methods

building

evaluation

domain

question-answering

classification

techniques

tested

including

text classification

support vector machines

language-model

based

retrieval

cross-language

information retrieval

techniques

success

rate

evaluated

effect

speech recognition

errors

performance

users

retrieval

robust

recognition

reaches

paper

methods

support vector machines

text classification

techniques

rate

errors

performance


Input: A Co-occurrence Graph-based Approach for Personal Name Alias Extraction from Anchor Texts A person may have multiple name aliases on the Web. Identifying aliases of a name is important for various tasks such as information retrieval, sentiment analysis and name disambiguation. We introduce the notion of a word co-occurrence graph to represent the mutual relations between words that appear in anchor texts. Words in anchor texts are represented as nodes in the co-occurrence graph and an edge is formed between nodes which link to the same url. For a given personal name, its neighboring nodes in the graph are considered as candidates of its aliases. We formalize alias identification as a problem of ranking nodes in this graph with respect to a given name. We integrate various ranking scores through support vector machines to leverage a robust ranking function and use it to extract aliases for a given name. Experimental results on a dataset of Japanese celebrities show that the proposed method outperforms all baselines, displaying a MRR score of 0. 562.

Co-occurrence

Graph-based Approach

Name

Alias

Extraction

Texts

name

aliases

Identifying

aliases

name

tasks

information retrieval

sentiment analysis

name

disambiguation

notion

word

co-occurrence

relations

words

anchor texts

Words

anchor texts

nodes

co-occurrence

edge

formed

nodes

link

name

neighboring

nodes

candidates

aliases

alias

identification

problem

nodes

respect

name

support vector machines

leverage

robust

function

extract

aliases

name

Experimental

results

Japanese

proposed

method

displaying

Graph-based Approach

Extraction

support vector machines

function

relations

anchor texts

nodes

Words

nodes

aliases


Input: Text Linkage In The Wiki Medium-A Comparative Study We analyze four different types of document networks with respect to their small world characteristics. These characteristics allow distinguishing wiki-based systems from citation and more traditional text-based networks augmented by hyperlinks. The study provides evidence that a more appropriate network model is needed which better reflects the specifics of wiki systems. It puts emphasize on their topological differences as a result of wiki-related linking compared to other text-based networks.

Text

Study

types

document

networks

respect

characteristics

characteristics

wiki-based systems

citation

text-based

networks

study

provides

evidence

network

model

systems

differences

result

wiki-related

linking

text-based

networks

characteristics

networks

wiki-based systems

networks

linking

networks

study

evidence


Input: Errors In Wikis This discussion document concerns the challenges to assessments of reliability posed by wikis and the potential for language processing techniques for aiding readers to decide whether to trust particular text.

discussion

document

concerns

challenges

assessments

reliability

language processing

techniques

text

document

challenges


Input: NTT System Description For The WMT2006 Shared Task " We present two translation systems experimented for the shared-task of " " Workshop on Statistical Machine Translation, " " a phrase-based model and a hierarchical phrase-based model. The former uses a phrasal unit for translation, whereas the latter is conceptualized as a synchronous-CFG in which phrases are hierarchically combined using non-terminals. Experiments showed that the hierarchical phrase-based model performed very comparable to the phrase-based model. We also report a phrase/rule extraction technique differentiating tokenization of corpora. "

System

Description

Shared Task

translation systems

experimented

shared-task

Workshop

Statistical Machine Translation

phrase-based model

phrase-based model

unit

translation

phrases

Experiments

phrase-based model

performed

phrase-based model

report

phrase

rule

extraction

technique

corpora

System

Shared Task

translation systems

shared-task

unit

translation

technique

corpora

phrase-based model

phrase-based model


Input: Web Corpus Mining By Instance Of Wikipedia In this paper we present an approach to structure learning in the area of web documents. This is done in order to approach the goal of webgenre tagging in the area of web corpus linguistics. A central outcome of the paper is that purely structure oriented approaches to web document classification provide an information gain which may be utilized in combined approaches of web content and structure analysis.

Corpus

Instance

paper

approach

structure

area

web documents

order

approach

goal

tagging

area

corpus

linguistics

outcome

paper

structure

approaches

web document

classification

provide

information gain

approaches

content

structure

analysis

paper

approach

structure

web documents

approaches

information gain

approaches

analysis


Input: BRUJA: Question Classification For Spanish Using Machine Translation and An English Classifier Question Classification is an important task in Question Answering Systems. This paper presents a Spanish Question Classifier based on machine learning, automatic online translators and different language features. Our system works with English collections and bilingual questions (English/Spanish). We have tested two Spanish-English online translators to identify the lost of precision. We have made experiments using lexical, syntactic and semantic features to test which ones made a better performance. The obtained results show that our system makes good classifications, over a 80% in terms of accuracy using the original English questions and over a 65% using Spanish questions and machine translation systems. Our conclusion about the features is that a lexical, syntactic and semantic features combination obtains the best result.

Question

Classification

Machine

English

Classifier

Question

Classification

task

Question Answering

Systems

paper

Question

Classifier

based

machine learning

automatic

translators

language

features

system

English

collections

questions

English

tested

English

translators

precision

experiments

lexical

syntactic

semantic features

test

performance

results

system

classifications

terms

accuracy

English

questions

questions

machine translation systems

conclusion

features

lexical

syntactic

semantic features

combination

result

Classifier

Classification

machine learning

Classifier

system

collections

Classification

Systems

semantic features

performance

system

classifications

combination

result


Input: Achilles: NiCT/ATR Chinese Morphological Analyzer for the Fourth Sighan Bakeoff We created a new Chinese morphological analyzer, Achilles, by integrating rule-based, dictionary-based, and statistical machine learning method, conditional random fields (CRF). The rule-based method is used to recognize regular expressions: numbers, time and alphabets. The dictionary-based method is used to find in-vocabulary (IV) words while out-of-vocabulary (OOV) words are detected by the CRFs. At last, confidence measure based approach is used to weigh all the results and output the best ones. Achilles was used and evaluated in the bakeoff. We participated the closed tracks of word segmentation and part-of-speech tagging for all the provided corpus. In spite of an unexpected file encoding errors, the system exhibited a top level performance. A higher word segmentation accuracy for the corpus ckip and ncc were achieved. We are ranked at the fifth and eighth position out of all 19 and 26 submissions respectively for the two corpus. Achilles uses a feature combined approach for part-of-speech tagging. Our post-evaluation results prove the effectiveness of this approach for POS tagging.

Chinese

Analyzer

Chinese

analyzer

rule-based

dictionary-based

statistical

machine

method

conditional random fields

rule-based method

expressions

numbers

time

dictionary-based method

in-vocabulary

words

out-of-vocabulary

words

confidence

based

approach

results

output

evaluated

word segmentation

part-of-speech

tagging

provided

corpus

spite

errors

system

level

performance

word segmentation

accuracy

corpus

ranked

submissions

corpus

feature

approach

part-of-speech

tagging

post-evaluation results

effectiveness

approach

POS tagging

method

analyzer

rule-based method

expressions

dictionary-based method

words

approach

results

tagging

corpus

word segmentation

corpus

approach

tagging

system

performance

approach

post-evaluation results


Input: Self-Training for Enhancement and Domain Adaptation of Statistical Parsers Trained on Small Datasets Creating large amounts of annotated data to train statistical PCFG parsers is expensive, and the performance of such parsers declines when training and testdata are taken from different domains. In this paper we use self-training in order to improve the quality of a parser and to adapt it to a different domain, using only small amounts of manually annotated seeddata. We report significant improvement both when the seed and testdata are in the same domain and in the out-of-domain adaptation scenario. In particular, we achieve 50% reduction in annotation cost for the in-domain case, yielding an improvement of 66% over previous work, and a 20-33% reduction for the domain adaptation case. This is the first time that self-training with small labeled datasets is applied successfully to these tasks. We were also able to formulate a characterization of when self-training is valuable.

Training

Enhancement

Domain Adaptation

Statistical

Trained

amounts

data

train

statistical

parsers

performance

parsers

training

testdata

testdata

domains

paper

self-training

order

improve

quality

parser

adapt

domain

amounts

seeddata

seeddata

report

improvement

seed

testdata

testdata

domain

out-of-domain adaptation

scenario

reduction

cost

in-domain

case

yielding

improvement

reduction

domain adaptation

case

time

self-training

applied

tasks

characterization

self-training

Training

Enhancement

data

parsers

self-training

parser

self-training

tasks

domains

performance


Input: Designing an Extensible API for Integrating Language Modeling and Realization " We present an extensible API for integrating language modeling and realization, describing its design and efficient implementation in the OpenCCG surface realizer. With OpenCCG, language models may be used to select realizations with preferred word orders, promote alignment with a conversational partner, avoid repetitive language use, and increase the speed of the best-first anytime search. The API enables a variety of n-gram models to be easily combined and used in conjunction with appropriate edge pruning strategies. The n-gram models may be of any order, operate in reverse (" " right-to-left " "), and selectively replace certain words with their semantic classes. Factored language models with generalized backoff may also be employed, over words represented as bundles of factors such as form, pitch accent, stem, part of speech, supertag, and semantic class. "

Language Modeling

Realization

language modeling

realization

design

implementation

surface

language models

realizations

word

orders

alignment

language

increase

speed

search

variety

n-gram models

conjunction

edge

strategies

n-gram models

order

words

semantic classes

language models

generalized

words

factors

form

pitch

stem

part of speech

semantic class

language models

realizations

speed

search

order

n-gram models

semantic classes

words

factors

words


Input: A Semantically Annotated Swedish Medical Corpus With the information overload in the life sciences there is an increasing need for annotated corpora, particularly with biological and biomedical entities, which is the driving force for data-driven language processing applications and the empirical approach to language study. Inspired by the work in the GENIA Corpus, MEDLEX Corpus.

Corpus

information

sciences

increasing

corpora

entities

data-driven

language processing

applications

approach

language

study

Corpus

Corpus

entities

corpora

approach

study


Input: Ontology Search with the OntoSelect Ontology Library OntoSelect is a dynamic web-based ontology library that harvests, analyzes and organizes ontologies published on the Semantic Web. OntoSelect allows searching as well as browsing of ontologies according to size (number of classes, properties), representation format (DAML, RDFS, OWL), connectedness (score over the number of included and referring ontologies) and human languages used for class-and object property-labels. Ontology search in OntoSelect is based on a combined measure of coverage, structure connectedness.

Ontology

Search

Ontology

Library

ontology

library

ontologies

Semantic

searching

ontologies

size

number

classes

properties

representation

format

number

included

ontologies

human languages

class-

object

property-labels

Ontology

search

based

coverage

structure

Library

Search

human languages

property-labels

coverage

search

size

ontologies

number

ontologies


Input: Speech Recognition And The Frequency Of Recently Used Words: A Modified Markov Model For Natural Language Speech recognition systems incorporate a language model which, at each stage of the recognition task, assigns a probability of occurrence to each word in the vocabulary. A class of Markov language models identified by Jelinek has achieved consider able success in this domain. A modification of the Markov approach, which assigns higher probabilities to recently used words, is proposed and tested against a pure Markov model. Parameter calculation and comparison of the two models both involve use of the LOB Corpus of tagged modern English.

Speech Recognition

Frequency

Words

Markov Model

Natural Language

Speech recognition systems

language model

recognition

task

probability

occurrence

word

vocabulary

class

language models

success

domain

modification

approach

probabilities

words

proposed

tested

Markov model

Parameter

calculation

comparison

models

Corpus

tagged

English

Frequency

Words

probability

word

probabilities

words

Markov Model

Natural Language

language model

Speech recognition systems

English

Corpus

language models

success


Input: Experimenting With The Interaction Between Aggregation And Text Structuring In natural language generation, different generation tasks often interact with each other in a complex way, which is hard to capture in the pipeline architecture described by Reiter (Reiter, 1994). This paper focuses on the interaction between a specific type of aggregation and text planning, in particular, maintaining local coherence, and tries to explore what preferences exist among the factors related to the two tasks. The evaluation result shows that it is these preferences that decide the quality of the generated text and capturing them properly in a generation system could lead to coherent text.

Interaction

Aggregation

Text

natural language generation

generation

tasks

complex

pipeline

architecture

paper

focuses

interaction

type

aggregation

text

coherence

preferences

factors

tasks

evaluation result

preferences

quality

generated

text

generation system

text

paper

interaction

factors

tasks

preferences

quality

generation system

text


Input: Quantitative Modeling Of Segmental Duration In natural speech, durations of phonetic segments are strongly dependent on contextual factors. Quantitative descriptions of these contextual effects have applications in text-to-speech synthesis and in automatic speech recognition. In this paper, we describe a speaker-dependent system for predicting segmental duration from text, with emphasis on the statistical methods used for its construction. We also report results of a subjective listening experiment evaluating an implementation of this system for text-to-speech synthesis purposes.

Modeling

Duration

natural

speech

durations

segments

factors

descriptions

effects

applications

text-to-speech

synthesis

automatic speech recognition

paper

system

duration

text

statistical methods

construction

report

results

experiment

evaluating

implementation

system

text-to-speech

synthesis

purposes

Modeling

Duration

text

duration

statistical methods

construction

implementation

synthesis

durations

segments

descriptions

effects

paper

system

experiment

results


Input: Flexible Mixed-Initiative Dialogue Management Using Concept-Level Confidence Measures Of Speech Recognizer Output We present a method to realize flexible mixed-initiative dialogue, in which the system can make effective confirmation and guidance using concept-level confidence measures (CMs) derived from speech recognizer output in order to handle speech recognition errors. We define two concept-level CMs, which are on content-words and on semantic-attributes, using 10-best outputs of the speech recognizer and parsing with phrase-level grammars. Content-word CM is useful for selecting plausible interpretations. Less confident interpretations are given to confirmation process. The strategy improved the interpretation accuracy by 11. 5%. Moreover, the semantic-attribute CM is used to estimate user' s intention and generates system-initiative guidances even when successful interpretation is not obtained.

Initiative

Dialogue Management

Concept-

Level

Confidence

Speech

Output

method

mixed-initiative

dialogue

system

confirmation

concept-level

confidence

speech

output

order

speech recognition

errors

concept-level

content-words

semantic-attributes

outputs

speech

parsing

phrase-level

Content-word

interpretations

interpretations

confirmation

process

strategy

improved

interpretation

accuracy

semantic-attribute

user

generates

system-initiative

interpretation

method

dialogue

system

confirmation

output

confidence

process

interpretations

strategy

accuracy


Input: The Automatic Acquisition Of Frequencies Of Verb Subcategorization Frames From Tagged Corpora We describe a mechanism for automatically acquiring verb subcategorization frames and their frequencies in a large corpus. A tagged corpus is first partially parsed to identify noun phrases and then a linear grammar is used to estimate the appropriate subcategorization frame for each verb token in the corpus. In an experiment involving the identification of six fixed subcategorization frames, our current system showed more than 80% accuracy. In addition, a new statistical approach substantially improves the accuracy of the frequency estimation.

Automatic

Acquisition

Verb

Corpora

mechanism

verb

frames

frequencies

corpus

tagged

corpus

noun phrases

frame

verb

corpus

experiment

identification

frames

current

system

accuracy

addition

statistical

approach

improves

accuracy

frequency

estimation

frequencies

frames

noun phrases

corpus

verb

corpus

system

accuracy

approach

accuracy


Input: The Semantic Representation Of Spatial Configurations: A Conceptual Motivation For Generation In Machine Translation This paper deals with the automatic translation of prepositions, which are highly polysemous. Moreover, the same real situation is often expressed by different prepositions in different languages. We proceed from the hypothesis that different usage patterns are due to different conceptualizations of the same real situation. Following cognitive principles of spatial conceptualization, we design a semantic interpretation process for spatial relations in which our translation system uses semantic features derived from a semantic sort hierarchy. Thus we can differentiate subtle distinctions between spatially significant configurations.

Semantic Representation

Motivation

Generation

Machine Translation

paper

deals

automatic

translation

prepositions

situation

prepositions

languages

hypothesis

usage

patterns

due

situation

principles

design

semantic interpretation

process

relations

translation system

semantic features

semantic

distinctions

configurations

Generation

Machine Translation

principles

process

semantic features

translation system

paper

translation

prepositions

languages

distinctions

configurations


Input: Investigating Complementary Methods For Verb Sense Pruning We present an approach for tagging verb sense that combines a domain-independent method based on subcategorization and alternations with a domain-dependent method utilizing statistically extracted verb clusters. Initial results indicate that verb senses can be pruned for highly polysemous verbs by up to 74% by the first method and by up to 85% by the second method.

Methods

Verb

Sense

approach

tagging

verb

sense

domain-independent

method

based

alternations

domain-dependent

method

extracted

verb

clusters

results

verb

verbs

method

method

approach

tagging

alternations

method

clusters

method


Input: Building A Sense Tagged Corpus With Open Mind Word Expert Open Mind Word Expert is an implemented active learning system for collecting word sense tagging from the general public over the Web. It is available at http://teach-computers. org. We expect the system to yield a large volume of high-quality training data at a much lower cost than the traditional method of hiring lexicographers. We thus propose a Senseval-3 lexical sample activity where the training data is collected via Open Mind Word Expert. If successful, the collection process can be extended to create the definitive corpus of word sense information.

Sense

Corpus

Word

Expert

Word

Expert

implemented

active learning

system

word sense

tagging

system

yield

volume

high-quality

training

data

cost

method

propose

lexical

sample

training

data

Word

Expert

collection

process

corpus

word sense

information

Sense

Corpus

system

tagging

system

data


Input: Mapping Collocational Properties Into Machine Learning Features This paper investigates interactions between collocational properties and methods for organizing them into features for machine learning. In experiments performing an event categorization task, Wiebe et al. (1997a) found that different organizations are best for different properties. This paper presents a statistical analysis of the results across different machine learning algorithms. In the experiments, the relationship between property and organization was strikingly consistent across algorithms. This prompted further analysis of this relationship, and an investigation of criteria for recognizing beneficial ways to include collocational properties in machine learning experiments. While many types of collocational properties and methods of organizing them into features have been used in NLP, systematic investigations of their interaction are rare.

Mapping

Machine Learning

Features

paper

interactions

properties

methods

features

machine learning

experiments

performing

event

categorization

task

organizations

properties

paper

statistical

analysis

results

machine

algorithms

experiments

relationship

property

organization

algorithms

analysis

relationship

investigation

criteria

include

properties

machine

experiments

types

properties

methods

features

investigations

interaction

paper

interactions

paper

analysis

analysis

relationship

investigation

criteria

investigations

interaction

properties

features

experiments

task

organizations

properties

properties

experiments

properties

features

algorithms

results


Input: KnoFusius: a New Knowledge Fusion System for Interpretation of Gene Expression Data This paper introduces a new architecture that aims at combining molecular biology data with information automatically extracted from relevant scientific literature (using text mining techniques on PubMed abstracts and fulltext papers) to help biomedical experts to interpret experimental results in hand. The infrastructural level bears on semantic-web technologies and standards that facilitate the actual fusion of the multi-source knowledge.

Knowledge

Fusion

System

Interpretation

Gene

Expression

Data

paper

architecture

biology

data

information

extracted

scientific literature

text

techniques

abstracts

papers

help

experts

experimental

results

hand

level

semantic-web

technologies

standards

fusion

multi-source

knowledge

System

Interpretation

techniques

abstracts

standards

fusion

paper

architecture

information

scientific literature


Input: Modelling Word Similarity: an Evaluation of Automatic Synonymy Extraction Algorithms. Vector-based models of lexical semantics retrieve semantically related words automatically from large corpora by exploiting the property that words with a similar meaning tend to occur in similar contexts. Despite their increasing popularity, it is unclear which kind of semantic similarity they actually capture and for which kind of words. In this paper, we use three vector-based models to retrieve semantically related words for a set of Dutch nouns and we analyse whether three linguistic properties of the nouns influence the results. In particular, we compare results from a dependency-based model with those from a 1st and 2nd order bag-of-words model and we examine the effect of the nouns' frequency, semantic speficity and semantic class. We find that all three models find more synonyms for high-frequency nouns and those belonging to abstract semantic classses. Semantic specificty does not have a clear influence.

Word

Similarity

Evaluation

Automatic

Extraction

Algorithms

Vector-based models

lexical semantics

words

corpora

property

words

contexts

increasing

popularity

kind

semantic similarity

kind

words

paper

vector-based models

words

nouns

properties

nouns

influence

results

results

dependency-based model

order

bag-of-words

model

effect

nouns

frequency

semantic

semantic class

models

synonyms

high-frequency

nouns

abstract

semantic

Semantic

influence

paper

vector-based models

properties

results

dependency-based model

results


Input: Childrens Oral Reading Corpus (CHOREC): Description and Assessment of Annotator Agreement Within the scope of the SPACE project, the CHildren' s Oral REading Corpus (CHOREC) is developed. This database contains recorded, transcribed and annotated read speech (42 GB or 130 hours) of 400 Dutch speaking elementary school children with or without reading difficulties. Analyses of inter-and intra-annotator agreement are carried out in order to investigate the consistency with which reading errors are detected, orthographic and phonetic transcriptions are made, and reading errors and reading strategies are labeled. Percentage agreement scores and kappa values both show that agreement between annotations, and therefore the quality of the annotations, is high. Taken all double or triple annotations (for 10% resp. 30% of the corpus) together,% agreement varies between 86. 4% and 98. 6%, whereas kappa varies between 0. 72 and 0. 97 depending on the annotation tier that is being assessed. School type and reading type seem to account for systematic differences in% agreement, but these differences disappear when kappa values are calculated that correct for chance agreement. To conclude, an analysis of the annotation differences with respect to the' * s' label (i. e. a label that is used to annotate undistinguishable spelling behaviour), phoneme labels, reading strategy and error labels is given.

Corpus

Description

Assessment

Agreement

scope

SPACE

project

Corpus

developed

database

recorded

speech

difficulties

Analyses

agreement

order

consistency

errors

transcriptions

errors

strategies

Percentage

agreement

kappa

agreement

quality

triple

corpus

agreement

kappa

type

type

differences

agreement

differences

kappa

agreement

analysis

differences

respect

behaviour

phoneme

strategy

error

speech

database

Analyses

consistency

analysis

differences

type

differences


Input: A Bilingual Corpus of Inter-linked Events This paper describes the creation of a bilingual corpus of inter-linked events for Italian and English. Linkage is accomplished through the Inter-Lingual Index (ILI) that links ItalWordNet with WordNet. The availability of this resource, on the one hand, enables contrastive analysis of the linguistic phenomena surrounding events in both languages, and on the other hand, can be used to perform multilingual temporal analysis of texts. In addition to describing the methodology for construction of the inter-linked corpus and the analysis of the data collected, we demonstrate that the ILI could potentially be used to bootstrap the creation of comparable corpora by exporting layers of annotation for words that have the same sense.

Corpus

Events

paper

creation

corpus

events

English

Lingual

Index

links

availability

resource

hand

analysis

phenomena

events

languages

hand

perform

analysis

texts

addition

methodology

construction

corpus

analysis

data

bootstrap

creation

corpora

layers

words

sense

Events

Corpus

events

corpus

paper

creation

analysis

phenomena

analysis

texts

methodology

construction


Input: New Resources for Document Classification, Analysis and Translation Technologies The goal of the DARPA MADCAT (Multilingual Automatic Document Classification Analysis and Translation) Program is to automatically convert foreign language text images into English transcripts, for use by humans and downstream applications. The first phase the program focuses on translation of handwritten Arabic documents. Linguistic Data Consortium (LDC) is creating publicly available linguistic resources for MADCAT technologies, on a scale and richness not previously available. Corpora will consist of existing LDC corpora and data donations from MADCAT partners, plus new data collection to provide high quality material for evaluation and to address strategic gaps (for genre, dialect, image quality, etc.) in the existing resources. Training and test data properties will expand over time to encompass a wide range of topics and genres: letters, diaries, training manuals, brochures, signs, ledgers, memos, instructions, post cards and forms among others. Data will be ground truthed, with line, word and token segmentation and zoning, and translations and word alignments will be produced for a subset. Evaluation data will be carefully selected from the available data pools and high quality references will be produced, which can be used to compare MADCAT system performance against the human-produced gold standard.

Document Classification

Analysis

Translation

goal

Automatic

Document Classification

Analysis

Translation

Program

foreign language

text

English

transcripts

applications

phase

program

focuses

translation

documents

Data

linguistic resources

technologies

scale

richness

Corpora

corpora

data

data

collection

provide

high quality

evaluation

gaps

genre

dialect

quality

resources

Training

test

data

properties

time

topics

genres

training

manuals

instructions

forms

Data

word

translations

word alignments

Evaluation

data

data

high quality

references

system

performance

gold standard

translation

documents

performance

gold standard


Input: Approximating Learning Curves for Active-Learning-Driven Annotation Active learning (AL) is getting more and more popular as a methodology to considerably reduce the annotation effort when building training material for statistical learning methods for various NLP tasks. A crucial issue rarely addressed, however, is when to actually stop the annotation process to profit from the savings in efforts. This question is tightly related to estimating the classifier performance after a certain amount of data has already been annotated. While learning curves are the default means to monitor the progress of the annotation process in terms of classifier performance, this requires a labeled gold standard which-in realistic annotation settings, at least-is often unavailable. We here propose a method for committee-based AL to approximate the progression of the learning curve based on the disagreement among the committee members. This method relies on a separate, unlabeled corpus and is thus well suited for situations where a labeled gold standard is not available or would be too expensive to obtain. Considering named entity recognition as a test case we provide empirical evidence that this approach works well under simulation as well as under real-world annotation conditions.

Learning

Learning-

Active learning

methodology

effort

building

training

statistical

learning methods

NLP tasks

issue

process

savings

efforts

question

classifier

performance

amount

data

default

progress

process

terms

classifier

performance

requires

gold standard

propose

method

based

disagreement

method

corpus

situations

gold standard

named

entity

recognition

test

case

provide

evidence

approach

simulation

question

performance

method

corpus

approach

recognition


Input: Lexicon Schemas and Related Data Models: when Standards Meet Users Lexicon schemas and their use are discussed in this paper from the perspective of lexicographers and field linguists. A variety of lexicon schemas have been developed, with goals ranging from computational lexicography (DATR) through archiving (LIFT, TEI) to standardization (LMF, FSR). A number of requirements for lexicon schemas are given. The lexicon schemas are introduced and compared to each other in terms of conversion and usability for this particular user group, using a common lexicon entry and providing examples for each schema under consideration. The formats are assessed and the final recommendation is given for the potential users, namely to request standard compliance from the developers of the tools used. This paper should foster a discussion between authors of standards, lexicographers and field linguists.

Lexicon

Related

Data

Models

Lexicon

schemas

paper

perspective

field

linguists

variety

lexicon

schemas

developed

goals

computational

archiving

number

requirements

lexicon

schemas

lexicon

schemas

terms

conversion

user

common

lexicon

entry

providing

examples

schema

consideration

formats

recommendation

users

standard

developers

tools

paper

discussion

standards

field

linguists

paper

schemas

schemas

archiving


Input: Arabic WordNet: Semi-automatic Extensions using Bayesian Inference This presentation focuses on the semi-automatic extension of Arabic WordNet (AWN) using lexical and morphological rules and applying Bayesian inference. We briefly report on the current status of AWN and propose a way of extending its coverage by taking advantage of a limited set of highly productive Arabic morphological rules for deriving a range of semantically related word forms from verb entries. The application of this set of rules, combined with the use of bilingual Arabic-English resources and Princeton' s WordNet, allows the generation of a graph representing the semantic neighbourhood of the original word. In previous work, a set of associations between the hypothesized Arabic words and English synsets was proposed on the basis of this graph. Here, a novel approach to extending AWN is presented whereby a Bayesian Network is automatically built from the graph and then the net is used as an inferencing mechanism for scoring the set of candidate associations. Both on its own and in combination with the previous technique, this new approach has led to improved results.

Semi-automatic

Extensions

Inference

presentation

focuses

semi-automatic

extension

lexical

rules

applying

inference

report

current

status

propose

coverage

advantage

rules

word

forms

verb

entries

application

rules

English

resources

generation

semantic

word

associations

words

English

proposed

basis

approach

Network

inferencing

mechanism

candidate

associations

combination

technique

approach

improved

results

Inference

Extensions

rules

generation

Network

mechanism

presentation

extension

approach

results


Input: Subjective Evaluation of an Emotional Speech Database for Basque///q This paper describes the evaluation process of an emotional speech database recorded for standard Basque, in order to determine its adequacy for the analysis of emotional models and its use in speech synthesis. The corpus consists of seven hundred semantically neutral sentences that were recorded for the Big Six emotions and neutral style, by two professional actors. The test results show that every emotion is readily recognized far above chance level for both speakers. Therefore the database is a valid linguistic resource for the research and development purposes it was designed for.

Evaluation

Speech

Database

paper

evaluation

process

speech

database

recorded

standard

order

adequacy

analysis

models

speech synthesis

corpus

sentences

recorded

emotions

test

results

emotion

level

database

linguistic resource

research

development

purposes

designed

paper

process

sentences

corpus

database

purposes


Input: How to Compare Treebanks Recent years have seen an increasing interest in developing standards for linguistic annotation, with a focus on the interoperability of the resources. This effort, however, requires a profound knowledge of the advantages and disadvantages of linguistic annotation schemes in order to avoid importing the flaws and weaknesses of existing encoding schemes into the new standards. This paper addresses the question how to compare syntactically annotated corpora and gain insights into the usefulness of specific design decisions. We present an exhaustive evaluation of two German treebanks with crucially different encoding schemes. We evaluate three different parsers trained on the two treebanks and compare results using EvalB, the Leaf-Ancestor metric, and a dependency-based evaluation. Furthermore, we present TePaCoC, a new testsuite for the evaluation of parsers on complex German grammatical constructions. The testsuite provides a well thought-out error classification, which enables us to compare parser output for parsers trained on treebanks with different encoding schemes and provides interesting insights into the impact of treebank annotation schemes on specific constructions like PP attachment or non-constituent coordination.

increasing

developing

standards

focus

resources

effort

requires

knowledge

advantages

schemes

order

schemes

standards

paper

question

corpora

gain

insights

usefulness

design

decisions

evaluation

schemes

evaluate

parsers

trained

results

metric

dependency-based

evaluation

evaluation

parsers

complex

constructions

provides

error

classification

parser

output

parsers

trained

schemes

provides

insights

impact

schemes

constructions

attachment

non-constituent

coordination

schemes

constructions


Input: The INFILE Project: a Crosslingual Filtering Systems Evaluation Campaign The InFile project (INformation, FILtering, Evaluation) is a cross-language adaptive filtering evaluation campaign, sponsored by the French National Research Agency. The campaign is organized by the CEA LIST, ELDA and the University of Lille3-GERiiCO. It has an international scope as it is a pilot track of the CLEF 2008 campaigns. The corpus is built from a collection of about 1, 4 millions newswires (10 GB) in three languages, Arabic, English and French provided by Agence France Press (AFP) and selected from a 3 years period. The profiles corpus is made of 50 profiles from which 30 concern general news and events (national and international affairs, politics, sports...) and 20 concern scientific and technical subjects.

Project

Systems

Evaluation

project

INformation

Evaluation

cross-language

evaluation

Research

LIST

University

scope

pilot

corpus

collection

languages

English

provided

corpus

concern

news

events

sports

concern

collection

corpus

events

corpus


Input: DIAC +: a Professional Diacritics Recovering System In languages that use diacritical characters, if these special signs are stripped-off from a word, the resulted string of characters may not exist in the language, and therefore its normative form is, in general, easy to recover. However, this is not always the case, as presence or absence of a diacritical sign attached to a base letter of a word which exists in both variants, may change its grammatical properties or even the meaning, making the recovery of the missing diacritics a difficult task, not only for a program but sometimes even for a human reader. We describe and evaluate an accurate knowledge-based system for automatic recovering the missing diacritics in MS-Office documents written in Romanian. For the rare cases when the system is not able to reliably make a decision, it either provides the user a list of words with their recovery suggestions, or probabilistically choose one of the possible changes, but leaves a trace (a highlighted comment) on each word the modification of which was uncertain.

System

languages

word

resulted

string

language

form

case

presence

absence

base

word

variants

properties

recovery

task

program

evaluate

knowledge-based system

automatic

documents

cases

system

decision

provides

user

list

words

recovery

suggestions

word

modification

string

language


Input: Annotating an Arabic Learner Corpus for Error This paper describes an ongoing project in which we are collecting a learner corpus of Arabic, developing a tagset for error annotation and performing Computer-aided Error Analysis (CEA) on the data. We adapted the French Interlanguage Database FRIDA tagset (Granger, 2003a) to the data. We chose FRIDA in order to follow a known standard and to see whether the changes needed to move from a French to an Arabic tagset would give us a measure of the distance between the two languages with respect to learner difficulty. The current collection of texts, which is constantly growing, contains intermediate and advanced-level student writings. We describe the need for such corpora, the learner data we have collected and the tagset we have developed. We also describe the error frequency distribution of both proficiency levels and the ongoing work.

Corpus

Error

paper

project

corpus

developing

error

performing

Computer-aided

Error Analysis

data

adapted

Database

data

order

standard

distance

languages

respect

difficulty

current

collection

texts

advanced-level

corpora

data

developed

error

frequency

distribution

proficiency

levels

paper

project

Error Analysis

data

difficulty

languages

advanced-level

collection

data

corpora


Input: All, and only, the Errors: more Complete and Consistent Spelling and OCR-Error Correction Evaluation Some time in the future, some spelling error correction system will correct all the errors, and only the errors. We need evaluation metrics that will tell us when this has been achieved and that can help guide us there. We survey the current practice in the form of the evaluation scheme of the latest major publication on spelling correction in a leading journal. We are forced to conclude that while the metric used there can tell us exactly when the ultimate goal of spelling correction research has been achieved, it offers little in the way of directions to be followed to eventually get there. We propose to consistently use the well-known metrics Recall and Precision, as combined in the F score, on 5 possible levels of measurement that should guide us more informedly along that path. We describe briefly what is then measured or measurable at these levels and propose a framework that should allow for concisely stating what it is one performs in one' s evaluations. We finally contrast our preferred metrics to Accuracy, which is widely used in this field to this day and to the Area-Under-the-Curve, which is increasingly finding acceptance in other fields.

OCR-Error

Correction

Evaluation

time

spelling

error

correction

system

errors

errors

evaluation metrics

help

survey

current

practice

form

evaluation

scheme

spelling

correction

journal

metric

goal

spelling

correction

research

directions

propose

metrics

Recall

Precision

levels

path

levels

propose

framework

performs

evaluations

contrast

metrics

Accuracy

field

Area-

fields

Correction

OCR-Error

system

errors

evaluation

current

metrics

Accuracy


Input: Using Movie Subtitles for Creating a Large-Scale Bilingual Corpora This paper presents a method for compiling a large-scale bilingual corpus from a database of movie subtitles. To create the corpus, we propose an algorithm based on Gale and Church' s sentence alignment algorithm (1993). However, our algorithm not only relies on character length information, but also uses subtitle-timing information, which is encoded in the subtitle files. Timing is highly correlated between subtitles in different versions (for the same movie), since subtitles that match should be displayed at the same time. However, the absolute time values can' t be used for alignment, since the timing is usually specified by frame numbers and not by real time, and converting it to real time values is not always possible, hence we use normalized subtitle duration instead. This results in a significant reduction in the alignment error rate.

Scale

Corpora

paper

method

large-scale

corpus

database

corpus

propose

algorithm

based

Gale

sentence

alignment

algorithm

algorithm

length

information

subtitle-timing

information

Timing

versions

match

displayed

time

time

alignment

timing

frame

numbers

real time

real time

duration

results

reduction

alignment

error rate

paper

method

database

corpus

algorithm

algorithm

information

algorithm

time

alignment


Input: The IFADV Corpus: a Free Dialog Video Corpus " Research into spoken language has become more visual over the years. Both fundamental and applied research have progressively included gestures, gaze, and facial expression. Corpora of multi-modal conversational speech are rare and frequently difficult to use due to privacy and copyright restrictions. A freely available annotated corpus is presented, gratis and libre, of high quality video recordings of face-to-face conversational speech. Within the bounds of the law, everything has been done to remove copyright and use restrictions. Annotations have been processed to RDBMS tables that allow SQL queries and direct connections to statistical software. From our experiences we would like to advocate the formulation of " " best practises " " for both legal handling and database storage of recordings and annotations. "

Corpus

Dialog

Video

Corpus

Research

language

applied

research

included

gestures

expression

Corpora

speech

due

restrictions

corpus

high quality

video

recordings

speech

bounds

restrictions

processed

tables

queries

statistical

software

experiences

formulation

database

storage

recordings

speech

Corpora

video

corpus

recordings

database


Input: WOZ Acoustic Data Collection for Interactive TV This paper describes a multichannel acoustic data collection recorded under the European DICIT project, during the Wizard of Oz (WOZ) experiments carried out at FAU and FBK-irst laboratories. The scenario is a distant-talking interface for interactive control of a TV. The experiments involve the acquisition of multichannel data for signal processing front-end and were carried out due to the need to collect a database for testing acoustic pre-processing algorithms. In this way, realistic scenarios can be simulated at a preliminary stage, instead of real-time implementations, allowing for repeatable experiments. To match the project requirements, the WOZ experiments were recorded in three languages: English, German and Italian. Besides the user inputs, the database also contains non-speech related acoustic events, room impulse response measurements and video data, the latter used to compute 3D labels. Sessions were manually transcribed and segmented at word level, introducing also specific labels for acoustic events.

Data

Collection

paper

data

collection

recorded

project

experiments

laboratories

scenario

interface

control

experiments

acquisition

data

signal processing

processing

due

database

pre-processing

algorithms

scenarios

real-time

implementations

experiments

match

project

requirements

experiments

recorded

languages

English

user

inputs

database

non-speech

events

video

compute

word

level

events

paper

data

interface

control

acquisition

signal processing

database

pre-processing

scenarios

experiments

video

compute

events

database