Input: Automatic extraction of subcategorization frames for Italian Subcategorization is a kind of knowledge which can be considered as crucial in several NLP tasks, such as Information Extraction or parsing, but the collection of very large resources including subcategorization representation is difficult and time-consuming. Various experiences show that the automatic extraction can be a practical and reliable solution for acquiring such a kind of knowledge. The aim of this paper is at investigating the relationships between subcategorization frame extraction and the nature ofdata from which the frames have to be extracted, e. g. how much the task can be influenced by the richness/poorness of the annotation. Therefore, we present some experiments that apply statistical subcategorization extraction methods, known in literature, on an Italian treebank that exploits a rich set of dependency relations that can be annotated at different degrees of specificity. Benefiting of the availability of relation sets that implement different granularity in the representation of relations, we evaluate our results with reference to previous works in a cross-linguistic perspective.

kind

knowledge

NLP tasks

Information Extraction

parsing

collection

resources

including

representation

time-consuming

experiences

automatic

extraction

solution

kind

knowledge

paper

relationships

frame

extraction

nature

ofdata

frames

extracted

task

influenced

richness

experiments

apply

statistical

extraction methods

literature

dependency relations

degrees

specificity

availability

relation

implement

representation

relations

evaluate

results

reference

cross-linguistic

perspective

resources

collection

representation

relations


Input: Benchmarking Textual Annotation Tools for the Semantic Web This paper investigates the state of the art in automatic textual annotation tools, and examines the extent to which they are ready for use in the real world. We define some benchmarking criteria for measuring the usability of annotation tools, and examine those factors which are particularly important for a real user to be able to determine which is the most suitable tool for their use. We discuss factors such as usability, accessibility, interoperability and scalability, and evaluate a set of annotation tools according to these factors. Finally, we draw some conclusions about the current state of research in annotation and make some suggestions for the future.

paper

automatic

tools

extent

real world

criteria

tools

factors

user

tool

factors

scalability

evaluate

tools

factors

conclusions

current

research

suggestions

paper

tools

factors

evaluate


Input: Building of a Speech Corpus Optimised for Unit Selection TTS Synthesis The paper deals with the process of designing a phonetically and prosodically rich speech corpus for unit selection speech synthesis. The attention is given mainly to the recording and verification stage of the process. In order to ensure as high quality and consistency of the recordings as possible, a special recording environment consisting of a recording session management and " pluggable " chain of checking modules was designed and utilised. Other stages, namely text collection (including) both phonetically and prosodically balanced sentence selection and a careful annotation on both orthographic and phonetic level are also mentioned.

paper

deals

process

designing

speech corpus

unit

selection

speech synthesis

recording

verification

process

order

high quality

consistency

recordings

recording

environment

recording

management

chain

modules

designed

text

collection

including

sentence

selection

level

mentioned

speech corpus

speech synthesis


Input: Communicating Unknown Words in Machine Translation A new approach to handle unknown words in machine translation is presented. The basic idea is to find definitions for the unknown words on the source language side and translate those definitions instead. Only monolingual resources are required, which generally offer a broader coverage than bilingual resources and are available for a large number of languages. In order to use this in a machine translation system definitions are extracted automatically from online dictionaries and encyclopedias. The translated definition is then inserted and clearly marked in the original hypothesis. This is shown to lead to significant improvements in (subjective) translation quality. clear if it will be a positive or a negative event. The first sentence is relatively understandable, but there is the possibility that the unknown word might negate the actual sentence. A background lexicon can ameliorate this situation, but it will not be possible to have a lexicon covering all words. The unknown word problem is especially severe for small portable translation systems, as here the vocabulary has to be limited to be able to put the translation system on the portable device.

approach

unknown words

machine translation

basic

definitions

unknown words

source language

side

translate

definitions

resources

required

coverage

resources

number

languages

order

machine translation system

definitions

extracted

dictionaries

encyclopedias

translated

definition

hypothesis

improvements

translation quality

event

sentence

possibility

unknown word

sentence

background

lexicon

situation

lexicon

words

unknown word

problem

translation systems

vocabulary

limited

translation system

device

definitions

unknown words

definitions

dictionaries


Input: Automatic Rich Annotation of Large Corpus of Conversational transcribed speech: the Chunking Task of the EPAC Project This paper describes the use of the CasSys platform in order to achieve the chunking of conversational speech transcripts by means of cascades of Unitex transducers. Our system is involved in the EPAC project of the French National agency of Research (ANR). The aim of this project is to develop robust methods for the annotation of audio/multimedia document collections which contains conversational speech sequences such as TV or radio programs. At first, this paper presents the EPAC project and the adaptation of a former chunking system (Romus) which was developed in the restricted framework of dedicated spoken man-machine dialogue. Then, it describes the problems that are arising due to 1) spontaneous speech disfluencies and 2) errors for the previous stages of processing (automatic speech recognition and POS tagging).

paper

platform

order

chunking

speech

transcripts

cascades

system

project

Research

project

develop

robust

methods

document

collections

speech

sequences

programs

paper

project

adaptation

chunking

system

developed

framework

man-machine

dialogue

problems

due

spontaneous speech

errors

processing

automatic speech recognition

POS tagging

chunking

speech

sequences

collections

paper

project


Input: Constructing a Corpus that Indicates Patterns of Modification between Draft and Final Translations by Human Translators In human translation, translators first make draft translations and then modify and edit them. In the case of experienced translators, this process involves the use of wide-ranging expert knowledge, which has mostly remained implicit so far. Describing the difference between draft and final translations, therefore, should contribute to making this knowledge explicit. If we could clarify the expert knowledge of translators, hopefully in a computationally tractable way, we would be able to contribute to the automatic notification of awkward translations to assist inexperienced translators, improving the quality of MT output, etc. Against this backdrop, we have started constructing a corpus that indicates patterns of modification between draft and final translations made by human translators. This paper reports on our progress to date.

translation

translators

translations

case

experienced

translators

process

expert

knowledge

difference

translations

knowledge

expert

knowledge

translators

automatic

translations

translators

improving

quality

output

constructing

corpus

patterns

modification

translations

translators

paper

reports

progress

date

patterns

modification


Input: Construction of an Idiom Corpus and its Application to Idiom Identification based on WSD Incorporating Idiom-Specific Features Some phrases can be interpreted either idiomatically (figuratively) or literally in context, and the precise identification of idioms is indispensable for full-fledged natural language processing (NLP). To this end, we have constructed an idiom corpus for Japanese. This paper reports on the corpus and the results of an idiom identification experiment using the corpus. The corpus targets 146 ambiguous idioms, and consists of 102, 846 sentences, each of which is annotated with a literal/idiom label. For idiom identification, we targeted 90 out of the 146 idioms and adopted a word sense disambiguation (WSD) method using both common WSD features and idiom-specific features. The corpus and the experiment are the largest of their kind, as far as we know. As a result, we found that a standard supervised WSD method works well for the idiom identification and achieved an accuracy of 89. 25% and 88. 86% with/without idiom-specific features and that the most effective idiom-specific feature is the one involving the adjacency of idiom constituents.

phrases

context

identification

idioms

natural language processing

constructed

idiom

corpus

Japanese

paper

reports

corpus

results

idiom

identification

experiment

corpus

corpus

targets

idioms

sentences

idiom

idiom

identification

targeted

idioms

word sense disambiguation

method

common

features

idiom-specific

features

corpus

experiment

kind

result

standard

method

idiom

identification

accuracy

idiom-specific

features

idiom-specific

feature

idiom

constituents

identification

idioms

corpus

experiment

sentences

corpus

word sense disambiguation

method

method

accuracy


Input: An Analysis of Active Learning Strategies for Sequence Labeling Tasks Active learning is well-suited to many problems in natural language processing, where unlabeleddata may be abundant but annotation is slow and expensive. This paper aims to shed light on the best active learning approaches for sequence labeling tasks such as information extraction and document segmentation. We survey previously used query selection strategies for sequence models, and propose several novel algorithms to address their shortcomings. We also conduct a large-scale empirical comparison using multiple corpora, which demonstrates that our proposed methods advance the state of the art.

Active learning

problems

natural language processing

unlabeleddata

paper

learning approaches

sequence

tasks

information extraction

document

survey

query

selection

strategies

sequence

models

propose

algorithms

shortcomings

large-scale

comparison

corpora

proposed

methods

advance

corpora

comparison


Input: Two-Phase Biomedical Named Entity Recognition Using A Hybrid Method Abstract. Biomedical named entity recognition (NER) is a difficult problem in biomedical information processing due to the widespread ambiguity of terms out of context and extensive lexical variations. This paper presents a two-phase biomedical NER consisting of term boundary detection and semantic labeling. By dividing the problem, we can adopt an effective model for each process. In our study, we use two exponential models, conditional random fields and maximum entropy, at each phase. Moreover, results by this machine learning based model are refined by rule-based postprocessing implemented using a finite state method. Experiments show it achieves the performance of F-score 71. 19% on the JNLPBA 2004 shared task of identifying 5 classes of biomedical NEs.

Abstract

named

entity

recognition

problem

information processing

due

ambiguity

terms

context

lexical

variations

paper

two-phase

term

boundary

detection

semantic

problem

model

process

study

models

conditional random fields

maximum entropy

phase

results

machine

based

model

rule-based

implemented

method

Experiments

performance

task

classes

ambiguity

terms

Experiments

performance


Input: Parser Evaluation and the BNC: Evaluating 4 constituency parsers with 3 metrics We evaluate discriminative parse reranking and parser self-training on a new English test set using four versions of the Charniak parser and a variety of parser evaluation metrics. The new test set consists of 1, 000 hand-corrected British National Corpus parse trees. We directly evaluate parser output using both the Parseval and the Leaf Ancestor metrics. We also convert the hand-corrected and parser output phrase structure trees to dependency trees using a state-of-the-art functional tag labeller and constituent-to-dependency conversion tool, and then calculate label accuracy, unlabelled attachment and labelled attachment scores over the dependency structures. We find that reranking leads to a performance improvement on the new test set (albeit a modest one). We find that self-training using BNCdata leads to significantly better results. However, it is not clear how effective self-training is when the training material comes from the North American News Corpus.

evaluate

parse

parser

self-training

English

test set

versions

parser

variety

parser

evaluation metrics

test set

hand-corrected

Corpus

parse trees

evaluate

parser

output

metrics

hand-corrected

parser

output

phrase structure trees

dependency trees

tag

constituent-to-dependency

conversion

tool

accuracy

attachment

attachment

dependency structures

performance improvement

test set

self-training

BNCdata

results

self-training

training

News

Corpus

evaluation metrics

evaluate

parse trees

test set

metrics

evaluate

self-training

results


Input: Thai Broadcast News Corpus Construction and Evaluation Large speech and text corpora are crucial to the development of a state-of-the-art speech recognition system. This paper reports on the construction and evaluation of the first Thai broadcast news speech and text corpora. Specifications and conventions used in the transcription process are described in the paper. The speech corpus contains about 17 hours of speechdata while the text corpus was transcribed from around 35 hours of television broadcast news. The characteristics of the corpus were analyzed and shown in the paper. The speech corpus was split according to the evaluation focus condition used in the DARPA Hub-4 evaluation. An 18k-word Thai speech recognition system was setup to test with this speech corpus as a preliminary experiment. Acoustic model adaptations were performed to improve the system performance. The best system yielded a word error rate of about 20% for clean and planned speech, and below 30% for the overall condition.

speech

text

corpora

development

speech recognition system

paper

reports

construction

evaluation

broadcast news

speech

text

corpora

Specifications

conventions

transcription

process

paper

speech corpus

speechdata

speechdata

text

corpus

broadcast news

characteristics

corpus

paper

speech corpus

evaluation

focus

evaluation

speech recognition system

setup

test

speech corpus

experiment

Acoustic model

adaptations

performed

improve

system

performance

system

yielded

word error rate

speech

corpora

speech recognition system

paper

process

speechdata

speech corpus

broadcast news

corpus

speech recognition system

test

system

word error rate


Input: Question Answering on Speech Transcriptions: the QAST evaluation in CLEF This paper reports on the QAST track of CLEF aiming to evaluate Question Answering on Speech Transcriptions. Accessing information in spoken documents provides additional challenges to those of text-based QA, needing to address the characteristics of spoken language, as well as errors in the case of automatic transcriptions of spontaneous speech. The framework and results of the pilot QAst evaluation held as part of CLEF 2007 is described, illustrating some of the additional challenges posed by QA in spoken documents relative to written ones. The current plans for future multiple-language and multiple-task QAst evaluations are described.

paper

reports

evaluate

Question Answering

Speech

information

documents

provides

challenges

text-based

characteristics

language

errors

case

automatic

transcriptions

spontaneous speech

framework

results

pilot

evaluation

part

challenges

documents

relative

current

multiple-language

multiple-task

evaluations

paper

Question Answering

errors

transcriptions


Input: Ontology Learning and Semantic Annotation: a Necessary Symbiosis " Semantic annotation of text requires the dynamic merging of linguistically structured information and a " world model ", usually represented as a domain-specific ontology. On the other hand, the process of engineering a domain ontology through semi-automatic ontology learning system requires the availability of a considerable amount of semantically annotated documents. Facing this bootstrapping paradox requires an incremental process of annotation-acquisition-annotation, whereby domain-specific knowledge is acquired from linguistically-annotated texts and then projected back onto texts for extra linguistic information to be annotated and further knowledge layers to be extracted. The presented methodology is a first step in the direction of a full " virtuous " circle where the semantic annotation platform and the evolving ontology interact in symbiosis. As a case study we have chosen the semantic annotation of product catalogues. We propose a hybrid approach, combining pattern matching techniques to exploit the regular structure of product descriptions in catalogues, and Natural Language Processing techniques which are resorted to analyze natural language descriptions. The semantic annotation involves the access to the ontology, semi-automatically bootstrapped with an ontology learning tool from annotated collections of catalogues.

Semantic

text

requires

structured

information

model

domain-specific

ontology

hand

process

domain

ontology

semi-automatic

ontology

system

requires

availability

amount

documents

bootstrapping

requires

process

annotation-acquisition-annotation

domain-specific

knowledge

texts

projected

texts

linguistic information

knowledge

layers

extracted

methodology

first step

direction

semantic

platform

ontology

case study

semantic

product

propose

approach

pattern

matching

techniques

structure

product

descriptions

Natural Language Processing techniques

natural language

descriptions

semantic

access

ontology

ontology

tool

collections

documents

system

knowledge

texts

structure

techniques

descriptions

Natural Language Processing techniques

collections

tool


Input: Anaphoric Annotation in the ARRAU Corpus Arrau is a new corpus annotated for anaphoric relations, with information about agreement and explicit representation of multiple antecedents for ambiguous anaphoric expressions and discourse antecedents for expressions which refer to abstract entities such as events, actions and plans. The corpus contains texts from different genres: task-oriented dialogues from the Trains-91 and Trains-93 corpus, narratives from the English Pear Stories corpus, newspaper articles from the Wall Street Journal portion of the Penn Treebank, and mixed text from the Gnome corpus.

corpus

relations

information

agreement

representation

expressions

discourse

expressions

abstract

entities

events

actions

corpus

texts

genres

task-oriented

dialogues

corpus

English

Pear

corpus

newspaper

Wall Street Journal

portion

Penn Treebank

text

corpus

corpus

relations

texts

corpus


Input: Improving Probabilistic Latent Semantic Analysis With Principal Component Analysis Probabilistic Latent Semantic Analysis (PLSA) models have been shown to provide a better model for capturing polysemy and synonymy than Latent Semantic Analysis (LSA). However, the parameters of a PLSA model are trained using the Expectation Maximization (EM) algorithm, and as a result, the trained model is dependent on the initialization values so that performance can be highly variable. In this paper we present a method for using LSA analysis to initialize a PLSA model. We also investigated the performance of our method for the tasks of text segmentation and retrieval on personal-size corpora, and present results demonstrating the efficacy of our proposed approach.

Probabilistic Latent Semantic Analysis

models

provide

model

Latent Semantic Analysis

parameters

model

trained

Expectation

Maximization

algorithm

result

trained

model

performance

variable

paper

method

analysis

model

performance

method

tasks

text

retrieval

personal-size

corpora

results

proposed

approach

algorithm

model

paper

method


Input: Improved Lexical Alignment By Combining Multiple Reified Alignments We describe a word alignment platform which ensures text pre-processing (to-kenization, POS-tagging, lemmatization, chunking, sentence alignment) as required by an accurate word alignment. The platform combines two different methods, producing distinct alignments. The basic word aligners are described in some details and are individually evaluated. The union of the individual alignments is subject to a filtering postprocessing phase. Two different filtering methods are also presented. The evaluation shows that the combined word alignment contains 10. 75% less errors than the best individual aligner.

word alignment

platform

text

pre-processing

POS-tagging

chunking

sentence

alignment

required

word alignment

platform

methods

alignments

basic

word

details

evaluated

individual

alignments

phase

methods

evaluation

word alignment

errors

individual

platform

pre-processing

word alignment

errors


Input: Toward A Task-Based Gold Standard For Evaluation Of NP Chunks And Technical Terms We propose a gold standard for evaluating two types of information extraction output--noun phrase (NP) chunks (Abney 1991; Ramshaw and Marcus 1995) and technical terms (Justeson and Katz 1995; Daille 2000; Jacquemin 2002). The gold standard is built around the notion that since different semantic and syntactic variants of terms are arguably correct, a fully satisfactory assessment of the quality of the output must include task-based evaluation. We conducted an experiment that assessed subjects' choice of index terms in an information access task. Subjects showed significant preference for index terms that are longer, as measured by number of words, and more complex, as measured by number of prepositions. These terms, which were identified by a human indexer, serve as the gold standard. The experimental protocol is a reliable and rigorous method for evaluating the quality of a set of terms. An important advantage of this task-based evaluation is that a set of index terms which is different than the gold standard can' win' by providing better information access than the gold standard itself does. And although the individual human subject experiments are time consuming, the experimental interface, test materials anddata analysis programs are completely re-usable.

propose

gold standard

evaluating

types

information extraction

output

noun phrase

chunks

terms

gold standard

notion

semantic

syntactic

variants

terms

assessment

quality

output

include

task-based

evaluation

experiment

choice

index

terms

information access

task

preference

index

terms

number

words

complex

number

prepositions

terms

gold standard

experimental

protocol

method

evaluating

quality

terms

advantage

task-based

evaluation

index

terms

gold standard

providing

information access

gold standard

individual

experiments

time

experimental

interface

test

anddata

analysis

programs

gold standard

information extraction

variants

terms

terms

gold standard

method

evaluating


Input: Acquiring Inference Rules With Temporal Constraints By Using Japanese Coordinated Sentences And Noun-Verb Co-Occurrences " This paper shows that inference rules with temporal constraints can be acquired by using verb-verb co-occurrences in Japanese coordinated sentences and verb-noun cooccurrences. For example, our unsuper-vised acquisition method could obtain the inference rule " If someone enforces a law, usually someone enacts the law at the same time as or before the enforcing of the law " since the verbs " enact " and " enforce " frequently co-occurred in coordinated sentences and the verbs also frequently co-occurred with the noun " law ". We also show that the accuracy of the acquisition is improved by using the occurrence frequency of a single verb, which we assume indicates how generic the meaning of the verb is.

paper

inference

rules

constraints

verb-verb

Japanese

sentences

verb-noun

example

acquisition

method

inference

rule

time

verbs

sentences

verbs

noun

accuracy

acquisition

improved

occurrence

frequency

verb

meaning

verb

verbs

sentences

frequency

verb


Input: Improved Affinity Graph Based Multi-Document Summarization This paper describes an affinity graph based approach to multi-document summarization. We incorporate a diffusion process to acquire semantic relationships between sentences, and then compute information richness of sentences by a graph rank algorithm on differentiated in-tra-document links and inter-document links between sentences. A greedy algorithm is employed to impose diversity penalty on sentences and the sentences with both high information richness and high information novelty are chosen into the summary. Experimental results on task 2 of DUC 2002 and task 2 of DUC 2004 demonstrate that the proposed approach outperforms existing state-of-the-art systems.

paper

based

approach

multi-document summarization

process

semantic

relationships

sentences

compute

information

richness

sentences

rank

algorithm

in-tra-document

links

inter-document

links

sentences

algorithm

diversity

sentences

sentences

information

richness

information

novelty

summary

Experimental

results

task

task

proposed

approach

systems

paper

approach

compute

algorithm

algorithm

sentences

sentences

summary


Input: A Hybrid Approach To Biomedical Named Entity Recognition And Semantic Role Labeling In this paper, we describe our hybrid approach to two key NLP technologies: biomedical named entity recognition (Bio-NER) and (Bio-SRL). In Bio-NER, our system successfully integrates linguistic features into the CRF framework. In addition, we employ web lexicons and template-based post-processing to further boost its performance. Through these broad linguistic features and the nature of CRF, our system outperforms state-of-the-art machine-learning-based systems, especially in the recognition of protein names (F = 78. 5%). In Bio-SRL, first, we construct a proposition bank on top of the popular biomedical GENIA treebank following the PropBank annotation scheme. We only annotate the predicate-argument structures (PAS' s) of thirty frequently used biomedical verbs (predicates) and their corresponding arguments. Second, we use our proposition bank to train a biomedical SRL system, which uses a maximum entropy (ME) machine-learning model. Thirdly, we automatically generate argument-type templates, which can be used to improve classification of biomedical argument roles. Our experimental results show that a newswire English SRL system that achieves an F-score of 86. 29% in the newswire English domain can maintain an F-score of 64. 64% when ported to the biomedical domain. By using our annotated biomedical corpus, we can increase that F-score by 22. 9%. Adding automatically generated template features further increases overall F-score by 0. 47% and adjunct (AM) F-score by 1. 57%, respectively.

paper

approach

technologies

named

entity

recognition

system

linguistic features

framework

addition

lexicons

template-based

post-processing

performance

linguistic features

nature

system

machine-learning-based systems

recognition

protein

names

construct

bank

scheme

predicate-argument structures

verbs

arguments

bank

train

system

maximum entropy

machine-learning

model

generate

argument-type

templates

improve

classification

argument

roles

experimental

results

English

system

English

domain

domain

corpus

increase

generated

template

features

increases

adjunct

paper

approach

linguistic features

framework

post-processing

performance

predicate-argument structures

verbs

bank

system

templates

classification


Input: Automatic Extraction Of Facts From Press Releases To Generate News Stories While complete understanding of arbitrary input text remains in the future, it is currently possible to construct natural language processing systems that provide a partial understanding of text with limited accuracy. Moreover, such systems can provide cost-effective solutions to commercially-significant business problems. This paper describes one such system: JASPER. JASPER is a fact extraction system recently developed and deployed by Carnegie Group for Reuters Ltd. JASPER uses a template-driven approach, partial understanding techniques, and heuristic procedures to extract certain key pieces of information from a limited range of text.. We believe that many significant business problems can be solved by fact extraction applications which involve locating and extracting specific, predefined types of information from a limited range of text The information extracted by such systems can be used in a variety of ways, such as filling in values in a database, generating summaries of the input text, serving as a part of the knowledge in an expert system, or feeding into another program which bases decisions on it. We expect to develop many such applications in the future using similar techniques.

understanding

input

text

construct

natural language processing systems

provide

partial

understanding

text

accuracy

systems

provide

cost-effective

solutions

problems

paper

system

extraction system

developed

template-driven

approach

partial

understanding

techniques

procedures

extract

information

text

problems

solved

extraction

applications

extracting

types

information

text

information

extracted

systems

variety

database

generating

summaries

input

text

part

knowledge

expert system

program

bases

decisions

develop

applications

techniques

techniques

extract

information

text

information

text

information

database

techniques

applications


Input: Exploiting Sophisticated Representations For Document Retrieval " The use of NLP techniques for document classification has not produced significant improvements in performance within the standard term weighting statistical assignment paradigm (Fagan 1987; Lewis, 1992b c; Buckley, 1993). This perplexing fact needs both an explanation and a solution if the power of recently developed NLP techniques are to be successfully applied in IR. A novel method for adding linguistic annotation to corpora is presented which involves using a statistical POS tagger in conjunction with unsupervised structure finding methods to derive notions of " noun group ", " verb group ", and so on which is inherently extensible to more sophisticated annotation, and does not require a pre-tagged corpus to fit. One of the distinguishing features of a more linguistically sophisticated representation of documents over a word set based representation of them is that linguistically sophisticated units are more frequently individually good predictors of document descriptors (keywords) than single words are. This leads us to consider the assignment of descriptors from individual phrases rather than from the weighted sum of a word set representation. We investigate how sets of individually high-precision rules can result in a low precision when used together, and develop some theory about these probably-correct rules. We then proceed to repeat results which show that standard statistical models are not particularly suitable for exploiting linguistically sophisticated representations, and show that a statistically fitted rule-based model provides significantly improved performance for sophisticated representations. It therefore shows that statistical systems can exploit sophisticated representations of documents, and lends some support to the use of more linguistically sophisticated representations for document classification. This paper reports on work done for the LRE project SlSTA, "

techniques

document classification

improvements

performance

standard

term

weighting

statistical

assignment

paradigm

Lewis

explanation

solution

developed

techniques

applied

method

corpora

statistical

conjunction

structure

methods

notions

noun

verb

require

corpus

features

representation

documents

word

based

representation

units

predictors

document

keywords

words

assignment

individual

phrases

sum

word

representation

high-precision

rules

result

precision

develop

theory

rules

results

standard

statistical models

representations

rule-based model

provides

improved

performance

representations

statistical

systems

representations

documents

support

representations

document classification

paper

reports

project

techniques

document classification

representation

documents

rules

precision

statistical models

rule-based model

representations

documents

representations

document classification

paper

project


Input: An Evaluation Of Lexicalization In Parsing In this paper, we evaluate a two-pass parsing strategy proposed for the so-called' lexicalized' grammar. In' lexicalized' grammars (Schabes, Abeille and Joshi, 1988), each elementary structure is systematically associated with a lexical item called A general two-pass parsing strategy for' lexicalized' grammars follows naturally. In the first stage, the parser selects a set of elementary structures associated with the lexical items in the input sentence, and in the second stage the sentence is parsed with respect to this set. We evaluate this strategy with respect to two characteristics. First, the amount of filtering on the entire grammar is evaluated: once the first pass is performed, the parser uses only a subset of the grammar. Second, we evaluate the use of non-local information: the structures selected during the first pass encode the morphological value (and therefore the position in the string) of their anchor; this enables the parser to use non-local information to guide its search. We take Lexicalized Tree Adjoining Grammars as an instance of lexicalized grammar. We illustrate the organization of the grammar. Then we show how a general Earley-type TAG parser (Schabes and Joshi, 1988) can take advantage of lexicalization. Empiricaldata show that the filtering of the grammar and the non-local information provided by the two-pass strategy improve the performance of the parser.

paper

evaluate

parsing

strategy

proposed

structure

lexical item

called

parsing

strategy

parser

structures

lexical items

input

sentence

sentence

respect

evaluate

strategy

respect

characteristics

amount

evaluated

performed

parser

evaluate

information

structures

string

parser

information

search

Tree

instance

organization

Earley-type

TAG

parser

advantage

Empiricaldata

information

provided

strategy

improve

performance

parser

structures

lexical items

information

parser

strategy

performance


Input: Context Dependent Modeling Of Phones In Continuous Speech Using Decision Trees In a continuous speech recognition system it is important to model the context dependent variations in the pronunciations of words. In this paper we present an automatic method for modeling phonological variation using decision trees. For each phone we construct a decision tree that specifies the acoustic realization of the phone as a function of the context in which it appears. Several thousand sentences from a natural language corpus spoken by several talkers are used to construct these decision trees. Experimental results on a 5000-word vocabulary natural language speech recognition task are presented.

continuous speech recognition system

model

context

variations

pronunciations

words

paper

automatic

method

modeling

variation

decision trees

phone

construct

decision tree

realization

phone

function

context

sentences

natural language

corpus

construct

decision trees

Experimental

results

word

vocabulary

natural language

speech recognition

task

variations

pronunciations

paper

method

decision trees

variation

phone

decision tree

sentences

corpus


Input: Experience With A Stack Decoder-Based HMM CSR And Back-Off N-Gram Language Models Stochastic language models are more useful than non-stochastic models because they contribute more information than a simple acceptance or rejection of a word sequence. Back-ofF N-gram language models [ll] are an effective class of word based stochastic language model. The first part of this paper describes our experiences using the back-off language models in our time-synchronous decoder CSR. A bigram back-off language model was chosen for the language model to be used in the informal ATIS CSR baseline evaluation test [13, 21]. The stack decoder [2, 8, 24] is a promising control structure for a speech understanding system because it can combine constraints from both the acoustic model and a long span language model (such as a natural language processor (NLP)) into a single integrated search [17]. A copy of the Lincoln time-synchronous HMM CSR has been converted to a stack decoder controlled search with stochastic language models. The second part of this paper describes our experiences with our prototype stack decoder CSR using no grammar, the word-pair grammar, and N-gram back-off language models.

language models

models

information

simple

word

sequence

gram language models

class

word

based

language model

part

paper

experiences

language models

time-synchronous

decoder

language model

language model

evaluation

test

decoder

control

structure

speech understanding system

constraints

acoustic model

span

language model

natural language processor

search

time-synchronous

decoder

search

language models

part

paper

experiences

prototype

decoder

word-pair

gram

language models

paper

experiences

language models

decoder

language model

test

decoder

speech understanding system

paper

decoder


Input: Hypothesizing Word Association From Untagged Text This paper reports a new method for suggesting word associations, based on a greedy algorithm that employs Chi-square statistics on joint frequencies of pairs of word groups compared against chance co-occurrence. The benefits of this new approach are: 1) we can consider even low frequency words and word pairs, and 2) word groups and word associations can be automatically generated. The method provided 87% accuracy in hypothesizing word associations for unobserved combinations of words in Japanese text.

paper

reports

method

word

associations

based

algorithm

statistics

frequencies

pairs

word

co-occurrence

benefits

approach

frequency

words

word pairs

word

word

associations

generated

method

provided

accuracy

word

associations

combinations

words

Japanese

text

paper

method

statistics

algorithm

method

accuracy


Input: Perceived Prosodic Boundaries And Their Phonetic Correlates This paper addresses two main questions: (a) Can listeners assign values of perceived boundary strength to the juncture between any two words? (b) If so, what is the relationship between these values and various (combinations of) suprasegmental features. Three speakers read a set of twenty utterances of varying length and complexity. A panel of nineteen listeners assigned boundary strength values to each of the 175 word boundaries in the material. Then the correlation was established between the variable strength of the perceived boundaries and three prosodie variables: melodic discontinuity, declination reset and pause. The results show that speakers may differ in their strategies of prosodie boundary marking and listeners agree in the perceptual weight they attribute to the prosodie cues.

paper

main

questions

listeners

boundary

strength

words

relationship

combinations

features

utterances

length

complexity

listeners

boundary

strength

word

boundaries

correlation

variable

strength

boundaries

variables

results

strategies

boundary

listeners

weight

cues

boundary

words

boundary

word


Input: WORDNET: A Lexical Database For English Work under this grant is intended to provide lexical resources for research on natural languages. The principal product is WordNet, a lexical database for English whose organization is inspired by current psycholinguistic theories of human lexical knowledge. Lexicalized concepts are organized by semantic relations for nouns, verbs, adjectives, and adverbs. The principal goal of the project is to upgrade WordNet and make it available to interested users. A secondary goal is to explore practical applications of WordNet; its possible use in the resolution of word senses in context (semantic disambiguation) is viewed as a necessary precursor for many other applications.

grant

provide

lexical resources

research

natural languages

product

lexical database

English

organization

current

theories

lexical knowledge

concepts

semantic relations

nouns

verbs

adjectives

adverbs

goal

project

users

goal

practical applications

resolution

word

context

semantic

disambiguation

applications

lexical resources

research


Input: Issues And Methodology For Template Design For Information Extraction The goal of Information Extraction tasks is to identify, categorize, classify, relate, and normalize specific information of interest found in free text, and to make that information available to a back-enddata base, data fusion, or other application. Adata structure referred to as a template (desiderata)

goal

Information Extraction

tasks

information

text

information

enddata

base

data

fusion

application

Adata

structure

template

information

text


Input: Experiments On Sentence Boundary Detection This paper explores the problem of identifying sentence boundaries in the transcriptions produced by automatic speech recognition systems. An experiment which determines the level of human performance for this task is described as well as a memory-based computational approach to the problem.

paper

problem

sentence

boundaries

transcriptions

speech recognition systems

experiment

level

performance

task

memory-based

computational

approach

problem

boundaries

transcriptions


Input: Multi-Perspective Question Answering Using The OpQA Corpus We investigate techniques to support the answering of opinion-based questions. We first present the OpQA corpus of opinion questions and answers. Using the corpus, we compare and contrast the properties of fact and opinion questions and answers. Based on the disparate characteristics of opinion vs. fact answers, we argue that traditional fact-based QA approaches may have difficulty in an MPQA setting without modification. As an initial step towards the development of MPQA systems, we investigate the use of machine learning and rule-based subjectivity and opinion source filters and show that they can be used to guide MPQA systems.

techniques

support

opinion-based

questions

corpus

opinion

questions

corpus

contrast

properties

opinion

questions

Based

characteristics

opinion

approaches

difficulty

modification

step

development

systems

machine learning

rule-based

opinion

source

systems

opinion

corpus

machine learning

systems


Input: Oleada: User-Centered TIPSTER Technology For Language Instruction TIPSTER is an AREA sponsored program that seeks to develop methods and tools that support analysts in their efforts to filter, process, and analyze ever increasing quantities of text-based information. To this end, government sponsors, contractors, and developers are working to design an architecture specification that makes it possible for natural language processing techniques and tools, from a variety sources, to be integrated, shared, and configured by end-users. The Computing Research Laboratory (CRL) is a longtime contributor to TIPSTER. A significant portion of CRL' s research involves work on a variety of natural language processing problems, human-computer interaction, and problems associated with getting technology into the hands of end-users. CRL is using TIPSTER technology to develop OLEADA, which is an integrated set of computer tools designed to support language learners, and instructors. Further, OLEADA has been developed using a task-oriented user-centered design methodology. This paper describes the methodology used to develop OLEADA and the current system' s capabilities.

AREA

program

develop

methods

tools

support

analysts

efforts

process

increasing

quantities

text-based

information

developers

design

architecture

specification

natural language processing techniques

tools

variety

sources

Computing

Research

Laboratory

portion

research

variety

natural language processing

problems

human-computer

interaction

problems

technology

hands

technology

develop

computer

tools

designed

support

language

developed

task-oriented

user-centered

design

methodology

paper

methodology

develop

current

system

capabilities

paper

methodology


Input: The Cornell TIPSTER Phase III Project The overall objective of the Cornell University TIPSTER Project was to improve end-user efficiency in information retrieval systems by reducing the amount of text that the user must process [1]. The project focuses on high precision IR, near-duplicate detection and context-dependent summarization. The two main foundations of the research are the latest version of the Smart system for information Retrieval and the Empire system for natural language processing. Smart is an implementation of the vector-space model of information retrieval (IR). Its earlier purpose was to provide a framework to conduct IR research but current developments will make the system easier to use by non-researcher. Empire is a research-oriented system that uses machine learning methods to quickly perform partial parsing of sentences. Cornell' s integrated approach uses both statistical and linguistic sources to first identify relationships among important terms in the query or in the text. The integrated system then uses the extracted relationships to (1) discard or reorder retrieved texts (for high-precision IR); (2) locate redundant information (for near-duplicate document detection); or (3) generate summaries. A more detailed technical description about the research can be found in the Cornell University technical paper [2].

objective

University

Project

improve

end-user

efficiency

information retrieval systems

amount

text

user

process

project

focuses

precision

detection

context-dependent

summarization

main

research

version

system

information Retrieval

system

natural language processing

implementation

vector-space

model

information retrieval

purpose

provide

framework

research

current

developments

system

non-researcher

research-oriented

system

machine

methods

perform

partial

parsing

sentences

approach

statistical

sources

relationships

terms

query

text

system

extracted

relationships

texts

high-precision

information

document

detection

generate

summaries

description

research

University

paper

Project

information retrieval systems

parsing

sentences

relationships

terms


Input: The Automatic Construction Of A Symbolic Parser Via Statistical Techniques We report on the development of a robust parsing device which aims to provide a partial explanation for child language acquisition and help in the construction of better natural language processing systems. The backbone of the new approach is the synthesis of statistical and symbolic approaches to natural language.

report

development

robust

parsing

device

provide

partial

explanation

language

acquisition

help

construction

natural language processing systems

backbone

approach

synthesis

statistical

approaches

natural language

device

natural language processing systems


Input: How Language Structures Concepts-An Outline For the past three decades, the mainstream of linguistics has focused its research agenda on the formal aspects of language, primarily syntax. By contrast, the more recent tradition of cognitive linguistics centers its research directly within the semantic stratum of language in order to observe how languages organize meaning and structure conception, and it examines the more formal stratum of language for its role in supporting these semantic functions.

mainstream

linguistics

focused

research

agenda

aspects

language

syntax

contrast

linguistics

centers

research

semantic

language

order

languages

structure

language

role

supporting

semantic

functions


Input: Corpus Based Statistical Generalization Tree In Rule Optimization A corpus-based statistical Generalization Tree model is described to achieve rule optimization for the information extraction task. First, the user creates specific rules for the target information from the sample articles through a training interface. Second, WordNet is applied to generalize noun entities in the specific rules. The degree of generalization is adjusted to fit the user' s needs by use of the statistical Generalization Tree model. Finally, the optimally generalized rules are applied to scan new information. The results of experiments demonstrate the applicability of our Generalization Tree method.

corpus-based

statistical

Generalization

Tree

model

rule

optimization

extraction task

user

rules

target

information

sample

training

interface

applied

noun

entities

rules

degree

generalization

user

statistical

Generalization

Tree

model

generalized

rules

applied

information

results

experiments

applicability

Generalization

Tree

method

model

optimization


Input: Automatic Lexicon Enhancement By Means Of Corpus Tagging Using specialised text corpus to automatically enhance a general lexicon is the aim of this study. Indeed, having lexicons which offer maximal cover on a specific topic is an important benefit in many applications of Automatic Speech and Natural Language Processing. The enhancement of these lexicons can be made automatic as big corpora of specialised texts are available. A syntactic tagging process, based on 3-class and 3-gram language models, allows us to automatically allocate possible syntactic categories to the Out-Of-Vocabulary (OOV) words which are found in the corpus processed. These OOV words generally occur several times in the corpus, and a number of these occurrences can be important. By taking into account all the occurrences of an OOV word in a given text as a whole, we propose here a method for automatically extracting a specialised lexicon from a text corpus which is representative of a specific topic.

text

corpus

lexicon

study

lexicons

topic

benefit

applications

Automatic

Speech

Natural Language Processing

enhancement

lexicons

automatic

corpora

texts

syntactic

tagging

process

based

class

gram language models

syntactic categories

Vocabulary

words

corpus

processed

words

times

corpus

number

occurrences

occurrences

word

text

propose

method

extracting

lexicon

text

corpus

representative

topic

corpus

lexicon

lexicons

applications

texts

corpora

lexicon

text


Input: Stochastic Phonological Grammars And Acceptability In foundational works of generative phonology it is claimed that subjects can reliably discriminate between possible but non-occurring words and words that could not be English. In this paper we examine the use of a probabilistic phonological parser for words to model experimentally-obtained judgements of the acceptability of a set of nonsense words. We compared various methods of scoring the goodness of the parse as a predictor of acceptability. We found that the probability of the worst part is not the best score of acceptability, indicating that classical generative phonology and Optimality Theory miss an important fact, as these approaches do not recognise a mechanism by which the frequency of well-formed parts may ameliorate the unacceptability of low-frequency parts. We argue that probabilistic generative grammars are demonstrably a more psychologically realistic model of phonological competence than standard generative phonology or Optimality Theory.

phonology

claimed

words

words

English

paper

parser

words

model

words

methods

parse

predictor

probability

part

phonology

Theory

approaches

mechanism

frequency

parts

low-frequency

parts

model

competence

standard

phonology

Theory

paper

parser

frequency

parts

model

competence


Input: Constraints And Defaults Of Zero Pronouns In Japanese Instruction Manuals In this paper, we propose a method for anaphora resolution of zero subjects in Japanese manual sentences based on both the nature of language expressions and the ontology of ordinary instruction manuals. In instruction manuals written in Japanese, zero subjects often introduce ambiguity into sentences. In order to resolve them, we consider the property of several types of expressions including some forms of verbal phrases and some conjunctives of clauses, and so on. As the result, we have a set of constraints and defaults for zero subject resolution. We examine the precision of the constraints and defaults with real manual sentences, and we have the result that they make a good estimate with precision of over 80%.

paper

propose

method

anaphora resolution

Japanese

manual

sentences

based

nature

language

expressions

ontology

instruction

manuals

instruction

manuals

Japanese

ambiguity

sentences

order

property

types

expressions

including

forms

verbal

phrases

clauses

result

constraints

defaults

resolution

precision

constraints

defaults

manual

sentences

result

precision

paper

method

expressions

anaphora resolution

ambiguity

sentences

property

expressions

forms

phrases


Input: Automatic Disambiguation Of Discourse Particles In spite of their important quantitative role, discourse particles have so far been neglected in automatic speech processing for two reasons: Firstly it is not clear what they may contribute to the aims of automatic speech processing, and secondly their functions seem to vary so much that it seems difficult to identify the information relevant to such aims. The approach presented here therefore attempts to provide automatic means to distinguishing the different readings of discourse particles and to filtering out the information which can be useful for speech understanding systems, employing positional information and their role within a dialogue model of the respective domain, two types of information which are especially easy to obtain. First results indicate that discourse particles can indeed be automatically disambiguated on the basis of the model proposed.

spite

role

discourse

particles

automatic

speech processing

reasons

Firstly

automatic

speech processing

functions

information

approach

provide

automatic

discourse

particles

information

speech understanding systems

information

role

dialogue

model

domain

types

information

results

discourse

particles

basis

model

proposed

speech processing

particles

information

speech understanding systems

model

domain

model

results


Input: Do Not Forget: Full Memory In Memory-Based Learning Of Word Pronunciation Memory-based learning, keeping full memory of learning material, appears a viable approach to learning NLP tasks, and is often superior in generalisation accuracy to eager learning approaches that abstract from learning material. Here we investigate three

Memory-based

memory

approach

NLP tasks

accuracy

learning approaches

abstract


Input: Experiments Using Stochastic Search For Text Planning Marcu has characterised an important and difficult problem in text planning: given a set of facts to convey and a set of rhetorical relations that can be used to link them together, how can one arrange this material so as to yield the best possible text? We describe experiments with a number of heuristic search methods for this task.

problem

text

relations

link

yield

text

experiments

number

search

methods

task


Input: Logical Structure And Discourse Anaphora Resolution Working within the Dynamic Quantifier Logic (DQL) framework (van den Berg 1992, 1996a, b), we claim in this paper that in every language the translation into a logical language will be such that the preference ordering of possible discourse referents for an anaphor in a sentence can be explained in terms of the scopal order of the expressions in the antecedent that introduce the discourse referents. Since the scope of terms is derived from arguments independent of any discourse theory, our account explains discourse anaphora resolution in terms of general principles of utterance semantics, from which the predictions of centering theory follow. When combined with the powerful discourse structural framework of the Linguistic Discourse Model (Polanyi (1985, 1986, 1988, 1996) Polanyi and Scha (1984), Scha and Polanyi (1988), Prfist, H., R. Scha and M. H. van den Berg, 1994; Polanyi, L. and M. H. van den Berg 1996; van den Berg, M. H. 1996b), we provide a unified account of discourse anaphora resolution.

Quantifier

Logic

framework

van

den

claim

paper

language

translation

language

preference

ordering

discourse

referents

sentence

terms

order

expressions

discourse

referents

scope

terms

arguments

discourse

theory

discourse

anaphora resolution

terms

principles

utterance

semantics

predictions

centering

theory

discourse

structural

framework

Discourse Model

van

den

van

den

van

den

provide

discourse

anaphora resolution

order

expressions


Input: Corpus-Based Anaphora Resolution Towards Antecedent Preference In this paper we propose a corpus-based approach to anaphora resolution combining a machine learning method and statistical information. First, a decision tree trained on an annotated corpus determines the coreference relation of a given anaphor and antecedent candidates and is utilized as a filter in order to reduce the number of potential candidates. In the second step, preference selection is achieved by taking into account the frequency information of coreferential and non-referential pairs tagged in the training corpus as well as distance features within the current discourse. Preliminary experiments concerning the resolution of Japanese pronouns in spoken-language dialogs result in a success rate of 80. 6%.

paper

propose

corpus-based approach

anaphora resolution

machine

method

statistical

information

decision tree

trained

corpus

relation

candidates

order

number

candidates

step

preference

selection

frequency

information

pairs

tagged

training corpus

distance

features

current

discourse

experiments

concerning

resolution

Japanese

spoken-language

dialogs

result

success

rate

paper

corpus-based approach

information

pairs

features

discourse

experiments

rate


Input: Disambiguating Toponyms In News This research is aimed at the problem of disambiguating toponyms (place names) in terms of a classification derived by merging information from two publicly available gazetteers. To establish the difficulty of the problem, we measured the degree of ambiguity, with respect to a gazetteer, for toponyms in news. We found that 67. 82% of the toponyms found in a corpus that were ambiguous in a gazetteer lacked a local discriminator in the text. Given the scarcity of human-annotated data, our method used unsuper-vised machine learning to develop disambiguation rules. Toponyms were automatically tagged with information about them found in a gazetteer. A toponym that was ambiguous in the gazetteer was automatically disambiguated based on preference heuristics. This automatically taggeddata was used to train a machine learner, which disambigu-ated toponyms in a human-annotated news corpus at 78. 5% accuracy.

research

problem

disambiguating

place names

terms

classification

information

gazetteers

difficulty

problem

degree

ambiguity

respect

gazetteer

news

corpus

gazetteer

lacked

text

scarcity

method

machine

develop

disambiguation

rules

tagged

information

gazetteer

gazetteer

based

preference

taggeddata

taggeddata

train

machine

news

corpus

accuracy

research

disambiguating

information

gazetteers

difficulty

problem

ambiguity

news

news

corpus


Input: Disambiguation Of Morphological Structure Using A PCFG German has a productive morphology and allows the creation of complex words which are often highly ambiguous. This paper reports on the development of a head-lexicalized PCFG for the disambiguation of German morphological analyses. The grammar is trained on unla-beleddata using the Inside-Outside algorithm. The parser achieves a precision of more than 68% on difficult test data, which is 23% more than the baseline obtained by randomly choosing one of the simplest analyses. Remarkable is the fact that precision drops to 52% without lexicalization.

morphology

creation

complex

words

paper

reports

development

disambiguation

morphological analyses

trained

beleddata

algorithm

parser

precision

test

analyses

precision

parser

precision


Input: " When Is An Embedded MT System " Good Enough " For Filtering? " This paper proposes an end-to-end process analysis template with replicable measures to evaluate the filtering performance of a Scan-OCR-MT system. Preliminary results

paper

proposes

process

analysis

template

evaluate

performance

OCR-MT system

results

paper

template


Input: Experiments On Unsupervised Learning For Extracting Relevant Fragments From Spoken Dialog Corpus In this paper are described experiments on unsupervised learning of the domain lexicon and relevant phrase fragments from a dialog corpus. Suggested approach is based on using domain independent words for chunking and using semantical predictional power of such words for clustering and automatic extraction phrase fragments relevant to dialog topics.

paper

experiments

learning

domain

lexicon

phrase

fragments

dialog

corpus

approach

based

domain

words

chunking

words

clustering

automatic

extraction

phrase

fragments

dialog

topics

dialog

corpus


Input: A Paraphrase-Based Exploration Of Cohesiveness Criteria This paper proposes an empirical approach to the development of a computational model for assessing texts according to cohesiveness. We argue that the NLG technologies for the generation of structural paraphrases can be used to efficiently create what we call a cohesion-variant parallel corpus, which would serve as a good resource for empirical acquisition of cohesiveness criteria. We also present our pilot case study, in which we took a particular type of paraphrasing that separates a relative clause from a sentence. We have so far created a cohesion-variant parallel corpus containing 499 cohesive instances and 841 incohesive instances. Based on this corpus, we conducted a preliminary experiment on cohesion evaluation, obtaining encouraging results.

paper

proposes

approach

development

computational model

texts

technologies

generation

structural

call

cohesion-variant

parallel corpus

resource

acquisition

criteria

pilot

case study

type

relative

clause

sentence

cohesion-variant

parallel corpus

instances

instances

Based

corpus

experiment

cohesion

evaluation

results

paper

approach

technologies

generation

instances

parallel corpus

experiment

results


Input: Tools And Resources For Tree Adjoining Grammars This paper presents a workbench for Tree Adjoining Grammars that we are currently developing. This workbench includes several tools and resources based on the markup language XML, used as a convenient language to format and exchange linguistic resources.

paper

Tree

developing

includes

tools

resources

based

markup

language

language

format

linguistic resources


Input: Processing Comparable Corpora With Bilingual Suffix Trees We introduce Bilingual Suffix Trees (BST), adata structure that is suitable for exploiting comparable corpora. We discuss algorithms that use BSTs in order to create parallel corpora and learn translations of unseen words from comparable corpora. Starting with a small bilingual dictionary that was derived automatically from a corpus of 5. 000 parallel sentences, we have automatically extracted a corpus of 33. 926 parallel phrases of size greater than 3, and learned 9 new word translations from a comparable corpus of 1. 3M words (100. 000 sentences).

Suffix

adata

structure

corpora

algorithms

order

parallel corpora

translations

words

corpora

dictionary

corpus

sentences

extracted

corpus

phrases

size

word

translations

corpus

words

sentences

sentences

corpus

phrases

corpus

words

corpus


Input: Bootstrapping A Multilingual Part-Of-Speech Tagger In One Person-Day This paper presents a method for bootstrapping a fine-grained, broad-coverage part-of-speech (POS) tagger in a new language using only one person-day ofdata acquisition effort. It requires only three resources, which are currently readily available in 60-100 world languages: (1) an online or hard-copy pocket-sized bilingual dictionary, (2) a basic library reference grammar, and (3) access to an existing monolingual text corpus in the language. The algorithm begins by inducing initial lexical POS distributions from English translations in a bilingual dictionary without POS tags. It handles irregular, regular and semi-regular morphology through a robust generative model using weighted Levenshtein alignments. Unsupervised induction ofgrammatical gender is performed via global modeling ofcontext-window feature agreement. Using a combination of these and other evidence sources, interactive training of context and lexical prior models are accomplished for fine-grained POS tag spaces. Experiments show high accuracy, fine-grained tag resolution with minimal new human effort.

paper

method

bootstrapping

broad-coverage

part-of-speech

language

ofdata

acquisition

effort

requires

resources

languages

dictionary

basic

library

reference

access

text

corpus

language

algorithm

lexical

distributions

English

translations

dictionary

POS tags

morphology

robust

generative model

alignments

induction

gender

performed

modeling

ofcontext-window

feature

agreement

combination

evidence

sources

training

context

lexical

models

POS tag

spaces

Experiments

accuracy

tag

resolution

effort

paper

method

alignments

generative model

Experiments

accuracy


Input: TREQ-AL: A Word Alignment System With Limited Language Resources We provide a rather informal presentation of a prototype system for word alignment based on our previous translation equivalence approach, discuss the problems encountered in the shared-task on word-aligning of a parallel Romanian-English text, present the preliminary evaluation results and suggest further ways of improving the alignment accuracy.

provide

presentation

prototype

system

word alignment

based

translation

equivalence

approach

problems

shared-task

word-aligning

English

text

evaluation results

improving

alignment

accuracy

system

word alignment


Input: Words And Pictures In The News We discuss the properties of a collection of news photos and captions, collected from the Associated Press and Reuters. Captions have a vocabulary dominated by proper names. We have implemented various text clustering algorithms to organize these items by topic, as well as an iconic matcher that identifies articles that share a picture. We have found that the special structure of captions allows us to extract some names of people actually portrayed in the image quite reliably, using a simple syntactic analysis. We have been able to build a directory of face images of individuals from this collection.

properties

collection

news

vocabulary

proper names

implemented

text

clustering

algorithms

items

topic

structure

extract

names

simple

syntactic analysis

individuals

collection

proper names

vocabulary

extract

names


Input: An Architecture For Word Learning Using Bidirectional Multimodal Structural Alignment " Learning of new words is assisted by contextual information. This context can come in several forms, including observations in non-linguistic semantic domains, as well as the linguistic context in which the new word was presented. We outline a general architecture for word learning, in which structural alignment coordinates this contextual information in order to restrict the possible interpretations of unknown words. We identify spatial relations as an applicable semantic domain, and describe a system-in-progress for implementing the general architecture using video sequences as our non-linguistic input. For example, when the complete system is presented with " The bird dove to the rock, " with a video sequence of a bird flying from a tree to a rock, and with the meanings for all the words except the preposition " to, " the system will register the unknown " to " with the corresponding aspect of the bird' s trajectory. "

Learning

words

contextual information

context

forms

including

observations

semantic

domains

context

word

outline

architecture

word

learning

structural

alignment

contextual information

order

interpretations

unknown words

relations

semantic

domain

system-in-progress

implementing

architecture

video

sequences

input

example

system

video

sequence

tree

words

preposition

system

aspect

word

context

interpretations

unknown words

sequences

input


Input: A Knowledge-Driven Approach To Text Meaning Processing " Our goal is to be able to answer questions about text that go beyond facts explicitly stated in the text, a task which inherently requires extracting a " deep " level of meaning from that text. Our approach treats meaning processing fundamentally as a modeling activity, in which a knowledge base of common-sense expectations guides interpretation of text, and text suggests which parts of the knowledge base might be relevant. In this paper, we describe our ongoing investigations to develop this approach into a usable method for meaning processing. "

goal

questions

text

text

task

requires

extracting

level

text

approach

processing

modeling

knowledge base

common-sense

expectations

interpretation

text

text

parts

knowledge base

paper

investigations

develop

approach

method

processing

interpretation

text

paper

approach

method

processing


Input: An Evolutionary Approach For Improving The Quality Of Automatic Summaries Automatic text extraction techniques have proved robust, but very often their summaries are not coherent. In this paper, we propose a new extraction method which uses local coherence as a means to improve the overall quality of automatic summaries. Two algorithms for sentence selection are proposed and evaluated on scientific documents. Evaluation showed that the method ameliorates the quality of summaries, noticeable improvements being obtained for longer summaries produced by an algorithm which selects sentences using an evolutionary algorithm.

Automatic

text

extraction

techniques

robust

summaries

paper

propose

extraction method

coherence

improve

quality

automatic

summaries

algorithms

sentence

selection

proposed

evaluated

documents

Evaluation

method

quality

summaries

improvements

summaries

algorithm

sentences

algorithm

paper

extraction method

algorithms

selection

method

quality


Input: Unsupervised Monolingual And Bilingual Word-Sense Disambiguation Of Medical Documents Using UMLS This paper describes techniques for unsupervised word sense disambiguation of English and German medical documents using UMLS. We present both monolingual techniques which rely only on the structure of UMLS, and bilingual techniques which also rely on the availability of parallel corpora. The best results are obtained using relations between terms given by UMLS, a method which achieves 74% precision, 66% coverage for English and 79% precision, 73% coverage for German on evaluation corpora and over 83% coverage over the whole corpus. The success of this technique for German shows that a lexical resource giving relations between concepts used to index an English document collection can be used for high quality disambiguation in another language.

paper

techniques

word sense disambiguation

English

documents

techniques

structure

techniques

availability

parallel corpora

results

relations

terms

method

precision

coverage

English

precision

coverage

evaluation

corpora

coverage

corpus

success

technique

lexical resource

relations

concepts

index

English

document

collection

high quality

disambiguation

language

paper

techniques

word sense disambiguation

documents

structure

techniques

parallel corpora

techniques

method

precision


Input: Discriminative Word Alignment via Alignment Matrix Modeling In this paper a new discriminative word alignment method is presented. This approach models directly the alignment matrix by a conditional random field (CRF) and so no restrictions to the alignments have to be made. Furthermore, it is easy to add features and so all available information can be used. Since the structure of the CRFs can get complex, the inference can only be done approximately and the standard algorithms had to be adapted. In addition, different methods to train the model have been developed. Using this approach the alignment quality could be improved by up to 23 percent for 3 different language pairs compared to a combination of both IBM4-alignments. Furthermore the word alignment was used to generate new phrase tables. These could improve the translation quality significantly.

paper

word alignment

method

approach

models

alignment

matrix

conditional random field

restrictions

alignments

features

information

structure

complex

inference

standard

algorithms

adapted

addition

methods

train

model

developed

approach

alignment

quality

improved

language pairs

combination

alignments

word alignment

generate

phrase

tables

improve

translation quality

paper

method

conditional random field

approach

methods

train


Input: Regularization and Search for Minimum Error Rate Training Minimum error rate training (MERT) is a widely used learning procedure for statistical machine translation models. We contrast three search strategies for MERT: Powell' s method, the variant of coordinate descent found in the Moses MERT utility, and a novel stochastic method. It is shown that the stochastic method obtains test set gains of + 0. 98 BLEU on MT03 and + 0. 61 BLEU on MT05. We also present a method for regularizing the MERT objective that achieves statistically significant gains when combined with both Powell' s method and coordinate descent.

Minimum error rate training

procedure

statistical machine translation

models

contrast

search strategies

method

variant

utility

method

method

test set

gains

method

objective

gains

method

Minimum error rate training

statistical machine translation

method

gains

method

gains


Input: Syntax-Driven Learning of Sub-Sentential Translation Equivalents and Translation Rules from Parsed Parallel Corpora We describe a multi-step process for automatically learning reliable sub-sentential syntactic phrases that are translation equivalents of each other and syntactic translation rules between two languages. The input to the process is a corpus of parallel sentences, word-aligned and annotated with phrase-structure parse trees. We first apply a newly developed algorithm for aligning parse-tree nodes between the two parallel trees. Next, we extract all aligned sub-sentential syntactic constituents from the parallel sentences, and create a syntax-based phrase-table. Finally, we treat the node alignments as tree decomposition points and extract from the corpus all possible synchronous parallel tree fragments. These are then converted into synchronous context-free rules. We describe the approach and analyze its application to Chinese-English paralleldata.

multi-step

process

syntactic

phrases

translation

syntactic

translation

rules

languages

input

process

corpus

sentences

word-aligned

phrase-structure

parse trees

apply

developed

algorithm

parse-tree

nodes

trees

extract

syntactic

constituents

sentences

syntax-based

phrase-table

node

alignments

tree

decomposition

extract

corpus

tree

fragments

context-free

rules

approach

application

Chinese-

English

paralleldata

sentences

corpus

constituents

sentences

fragments

corpus


Input: Determining Verb Phrase Referents In Dialogs " This paper discusses two problems central to the interpretation of utterances: determining the relationship between actions described in an utterance and events in the world, and inferring the " state of the world " from utterances. Knowledge of the language, knowledge about the general subject being discussed, and knowledge about the current situation are all necessary for this. The problem of determining an action referred to by a verb phrase is analogous to the problem of determining the object referred to by a noun phrase. This paper presents an approach to the problem of determining verb phrase referents in which knowledge about language, the subject area, and the dialog itself is combined to interpret such references. Presented and discussed are the kinds of knowledge necessary for interpreting references to actions, as well as algorithms for using that knowledge in interpreting dialog utterances about ongoing tasks and for drawing inferences about the task situation that are based on a given interpretation. "

paper

problems

interpretation

utterances

relationship

actions

utterance

events

utterances

Knowledge

language

knowledge

knowledge

current

situation

problem

action

verb

phrase

problem

object

noun phrase

paper

approach

problem

verb

phrase

referents

knowledge

language

area

dialog

references

kinds

knowledge

references

actions

algorithms

knowledge

dialog

utterances

tasks

inferences

task

situation

based

interpretation

interpretation

utterances

paper

approach


Input: An Algorithm For Generating Quantifier Scopings The syntactic structure of a sentence often manifests quite clearly the predicate-argument structure and relations of grammatical subordination. But scope dependencies are not so transparent. As a result, many systems for representing the semantics of sentences have ignored scoping or generated scopings with mechanisms that have often been inexplicit as to the range of scopings they choose among or profligate in the scopings they allow. This paper presents, along with proofs of some of its important properties, an algorithm that generates scoped semantic forms from unscoped expressions encoding predicate-argument structure. The algorithm is not profligate as are those based on permutation of quantifiers, and it can provide a solid foundation for computational solutions where completeness is sacrificed for efficiency and heuristic efficacy.

syntactic structure

sentence

predicate-argument structure

relations

scope

dependencies

result

systems

semantics

sentences

generated

mechanisms

paper

properties

algorithm

generates

semantic

forms

expressions

predicate-argument structure

algorithm

based

permutation

quantifiers

provide

computational

solutions

completeness

efficiency

syntactic structure

sentence

semantics

sentences

paper

algorithm

forms

expressions

algorithm

solutions


Input: The Problem Of Logical-Form Equivalence " This note discusses the problem of logical-form equivalence, a problem in natural language generation first noted in print by Douglas Appelt (1987). Appelt describes the problem, which we first came across in pursuing joint work on the GeneSys natural language generation system, as a problem following from the goal of eliminating the need for a strategic generation component to possess detailed grammatical knowledge. In a paper describing the GeneSys tactical generation component (Shieber 1988), I claimed that the problem was " AI-complete, " in the sense that its resolution would involve a solution to the general knowledge representation problem. Since the publication of these two papers, several researchers have claimed to have solved the problem of logical-form equivalence. In this paper, I review the problem and attempt to highlight certain salient aspects of it that have been lost in the pursuing of solutions, in order to reconcile the apparently contradictory claims of the problem' s intractability and its resolution. "

note

problem

logical-form

equivalence

problem

natural language generation

noted

problem

natural language generation system

problem

goal

generation

component

knowledge

paper

generation

component

claimed

problem

sense

resolution

solution

knowledge representation

problem

papers

researchers

claimed

solved

problem

logical-form

equivalence

paper

review

problem

aspects

solutions

order

claims

problem

resolution


Input: Integrating General-Purpose And Corpus-Based Verb Classification A long-standing debate in the computational linguistic community is about the generality of lexical taxonomies. Many linguists (Nirenburg 1995; Hirst 1995) stress that taxonomies that are not language neutral, at least at the intermediate and high level, have little hope of success. On the other hand, lexicon builders who have experience of designing taxonomies for real applications claim that in sublanguages there exist very domain-dependent similarity relations. Given our experience and results, we are inclined to take the second position, but we are indeed sensitive to the theoretical motivations of the first. The problem is that the similarity relations suggested by the thematic structures of wordscompositional

computational

community

generality

lexical

taxonomies

linguists

taxonomies

language

level

success

hand

lexicon

experience

designing

taxonomies

applications

claim

domain-dependent

similarity

relations

experience

results

motivations

problem

similarity

relations

structures

taxonomies

applications


Input: Models Of Translational Equivalence Among Words Parallel texts (bitexts) have properties that distinguish them from other kinds of paralleldata. First, most words translate to only one other word. Second, bitext correspondence is typically only partial many words in each text have no clear equivalent in the other text. This article presents methods for biasing statistical translation models to reflect these properties. Evaluation with respect to independent human judgments has confirmed that translation models biased in this fashion are significantly more accurate than a baseline knowledge-free model. This article also shows how a statistical translation model can take advantage of preexisting knowledge that might be available about particular language pairs. Even the simplest kinds of language-specific knowledge, such as the distinction between content words and function words, are shown to reliably boost translation model performance on some tasks. Statistical models that reflect knowledge about the model domain combine the best of both the rationalist and empiricist paradigms.

Parallel texts

properties

kinds

paralleldata

words

translate

word

correspondence

partial

words

text

text

methods

biasing

statistical translation models

properties

Evaluation

respect

judgments

translation models

biased

fashion

knowledge-free

model

statistical translation model

advantage

knowledge

language pairs

kinds

language-specific

knowledge

distinction

content words

function words

translation model

performance

tasks

Statistical models

knowledge

model

domain

paradigms

properties

Parallel texts

statistical translation models

properties


Input: Human Variation And Lexical Choice Much natural language processing research implicitly assumes that word meanings are fixed in a language community, but in fact there is good evidence that different people probably associate slightly different meanings with words. We summarize some evidence for this claim from the literature and from an ongoing research project, and discuss its implications for natural language generation, especially for lexical choice, that is, choosing appropriate words for a generated text.

natural language processing

research

word meanings

language

community

evidence

words

evidence

claim

literature

research project

implications

natural language generation

lexical choice

words

generated

text


Input: Statistical Machine Translation With Scarce Resources Using Morpho-Syntactic Information In statistical machine translation, correspondences between the words in the source and the target language are learned from parallel corpora, and often little or no linguistic knowledge is used to structure the underlying models. In particular, existing statistical systems for machine translation often treat different inflected forms of the same lemma as if they were independent of one another. The bilingual trainingdata can be better exploited by explicitly taking into account the interdependencies of related inflected forms. We propose the construction of hierarchical lexicon models on the basis ofequivalence classes ofwords. In addition, we introduce sentence-level restructuring transformations which aim at the assimilation ofword order in related sentences. We have systematically investigated the amount ofbilingual trainingdata required to maintain an acceptable quality ofmachine translation. The combination ofthe suggested methods for improving translation quality in frameworks with scarce resources has been successfully tested: We were able to reduce the amount of bilingual trainingdata to less than 10% of the original corpus, while losing only1. 6% in translation quality. The improvement of the translation results is demonstrated on two German-English corpora taken from the Verbmobil task and the Nespole! task.

statistical machine translation

correspondences

words

source

target language

parallel corpora

linguistic knowledge

structure

models

statistical

systems

machine translation

forms

lemma

trainingdata

trainingdata

forms

propose

construction

lexicon

models

basis

classes

addition

sentence-level

transformations

order

sentences

amount

trainingdata

trainingdata

required

quality

translation

combination

methods

improving

translation quality

frameworks

resources

tested

amount

trainingdata

trainingdata

corpus

translation quality

improvement

translation results

English

corpora

task

task

correspondences

parallel corpora

systems

machine translation

forms

lemma


Input: " Comments On " Incremental Construction And Maintenance Of Minimal Finite-State Automata, " By Rafael C. Carrasco And Mikel L. Forcada " " In a recent article, Carrasco and Forcada (June 2002) presented two algorithms: one for incremental addition of strings to the language of a minimal, deterministic, cyclic automaton, and one for incremental removal of strings from the automaton. The first algorithm is a generalization of the " algorithm for unsorted data " the second of the two incremental algorithms for construction of minimal, deterministic, acyclic automata presented in Daciuk (2000). We show that the other algorithm in the older article the " algorithm for sorted data " can be generalized in a similar way. The new algorithm is faster than the algorithm for addition ofstrings presented in Carrasco and Forcada' s article, as it handles each state only once. "

algorithms

addition

strings

language

removal

strings

algorithm

generalization

algorithm

algorithms

construction

algorithm

algorithm

generalized

algorithm

algorithm

addition


Input: Semantic negotiation in dialogue: the mechanisms of alignment A key problem for models of dialogue is to explain how semantic co-ordination in dialogue is achieved and sustained. This paper presents findings from a series of Maze Task experiments which are not readily explained by the primary co-ordination mechanisms of existing models. It demonstrates that alignment in dialogue is not simply an outcome of successful interaction, but a communicative resource exploited by interlocutors in converging on a semantic model. We argue this suggests mechanisms of co-ordination in dialogue which are of relevance for a general account of how semantic co-ordination is achieved.

problem

models

dialogue

semantic

dialogue

paper

findings

series

Task

experiments

mechanisms

models

alignment

dialogue

outcome

interaction

resource

interlocutors

semantic

model

mechanisms

dialogue

relevance

semantic


Input: A Structure-sharing Parser for Lexicalized Grammars In wide-coverage lexicalized grammars many of the elementary structures have substructures in common. This means that in conventional parsing algorithms some of the computation associated with different structures is duplicated. In this paper we describe a precompilation technique for such grammars which allows some of this computation to be shared. In our approach the elementary structures of the grammar are transformed into finite state automata which can be merged and minimised using standard algorithms, and then parsed using an automaton-based parser. We present algorithms for constructing automata from elementary structures, merging and minimising them, and string recognition and parse recovery with the resulting grammar.

wide-coverage

structures

substructures

common

parsing

algorithms

computation

structures

paper

technique

computation

approach

structures

standard

algorithms

parser

algorithms

constructing

structures

string

recognition

parse

recovery

resulting

substructures

structures

paper

technique


Input: Automatic Construction of Frame Representations for Spontaneous Speech in Unrestricted Domains This paper presents a system which automatically generates shallow semantic frame structures for conversational speech in unrestricted domains. We argue that such shallow semantic representations can indeed be generated with a minimum amount of linguistic knowledge engineering and without having to explicitly construct a semantic knowledge base. The system is designed to be robust to deal with the problems of speech dysfluencies, ungrammaticalities, and imperfect speech recognition. Initial results on speech transcripts are promising in that correct mappings could be identified in 21% of the clauses of a test set (resp. 44% of this test set where ungrammatical or verb-less clauses were removed).

paper

system

generates

semantic

frame

structures

speech

domains

semantic representations

generated

minimum

amount

linguistic knowledge

engineering

construct

semantic knowledge

base

system

designed

robust

deal

problems

speech

speech recognition

results

speech

transcripts

mappings

clauses

test set

test set

verb-less

clauses

paper

system


Input: The GREC Challenge 2008: Overview and Evaluation Results The grec Task at reg' 08 required participating systems to select coreference chains to the main subject of short encyclopaedic texts collected from Wikipedia. Three teams submitted a total of 6 systems, and we additionally created four baseline systems. Systems were tested automatically using a range of existing intrinsic metrics. We also evaluated systems extrinsically by applying coreference resolution tools to the outputs and measuring the success of the tools. In addition, systems were tested in a reading/comprehension experiment involving human subjects. This report describes the grec Task and the evaluation methods, gives brief descriptions of the participating systems, and presents the evaluation results.

Task

required

systems

chains

main

texts

systems

baseline systems

Systems

tested

metrics

evaluated

systems

applying

coreference resolution

tools

outputs

success

tools

addition

systems

tested

comprehension

experiment

report

Task

evaluation methods

descriptions

systems

evaluation results

report

evaluation methods


Input: A Hybrid Feature Set based Maximum Entropy Hindi Named Entity Recognition We describe our effort in developing a Named Entity Recognition (NER) system for Hindi using Maximum Entropy (Max-Ent) approach. We developed a NER annotated corpora for the purpose. We have tried to identify the most relevant features for Hindi NER task to enable us to develop an efficient NER from the limited corpora developed. Apart from the orthographic and collocation features, we have experimented on the efficiency of using gazetteer lists as features. We also worked on semi-automatic induction of context patterns and experimented with using these as features of the MaxEnt method. We have evaluated the performance of the system against a blind test set having 4 classes-Person, Organization, Location and Date. Our system achieved a f-value of 81. 52%.

effort

developing

Named

Entity

Recognition

system

Maximum Entropy

approach

developed

corpora

purpose

features

task

develop

corpora

developed

collocation

features

experimented

efficiency

gazetteer

lists

features

semi-automatic

induction

context

patterns

experimented

features

method

evaluated

performance

system

test set

classes

Organization

Location

Date

system

Maximum Entropy

system


Input: SVM Classification Of FrameNet Semantic Roles A Support Vector Machines (SVM) classifier of FrameNet semantic roles was implemented based on a set of new and previously used syntactic and semantic features. At Senseval 3, the system achieved a precision of 0. 807 for the restricted test and 0. 898 for the non-restricted test.

Support

Vector

classifier

semantic roles

implemented

based

syntactic

semantic features

system

precision

test

test

system

precision


Input: Acknowledgment Use With Synthesized And Recorded Prompts " Acknowledgments, e. g., " yeah " and " uh-huh, " are ubiquitous in human conversation but are rarer in human-computer interaction. What interface factors might contribute to this difference? Using a simple spoken-language interface that responded to acknowledgments, we compared subjects' use of acknowledgments when the interface used recorded speech with that seen when the interface used synthesized speech. Contrary to our hypothesis, we saw a drop in the numbers of subjects using acknowledgments: subjects appeared to interpret the recorded-voice interface as signalling a more limited interface. These results were consistent for both Mexican Spanish and American English versions of the interface. "

conversation

human-computer

interaction

interface

factors

difference

simple

spoken-language

interface

interface

recorded

speech

interface

speech

hypothesis

numbers

interface

interface

results

English

versions

interface

speech

interface

speech

interface


Input: Speech Recognition Models Of The Interdependence Among Syntax, Prosody, And Segmental Acoustics This paper describes results from several dozen experimental systems, and draws conclusions about the ability of speech recognition models to represent the relationship among syntax, prosody, and segmental acoustics. Prosody-dependent allophone modeling can reduce the word error rate (WER) of a speech recognizer, but only if both the language model and the acoustic model encode explicit dependence on prosody. Word error rate is improved mainly because the observed prosody is linguistically unlikely to co-occur with any incorrect word string. Additional improvements, in both perplexity and WER, can be obtained using a semi-factored language model, in which the relationship between prosody and the word sequence is at least partly mediated by syntactic tags. Careful analysis of the relationship between prosody and syntax indicates that syntactic phrase boundaries are the most important cue for prosodic phrase boundary recognition, while part of speech is the most important cue for locating pitch accents, but that neither of these cues is entirely sufficient for either classification task. Experiments to port this system from Radio News to the Switchboard corpus are currently under way, but preliminary results suggest that the prosody of Switchboard is profoundly different from the prosody of Radio News.

paper

results

experimental

systems

conclusions

ability

speech recognition

models

relationship

syntax

prosody

Prosody-dependent

modeling

word error rate

speech

language model

acoustic model

dependence

prosody

Word error rate

improved

prosody

word

string

improvements

perplexity

language model

relationship

prosody

word

sequence

syntactic

tags

analysis

relationship

prosody

syntax

syntactic

phrase

boundaries

cue

phrase

boundary

recognition

part of speech

cue

pitch

cues

classification task

Experiments

port

system

News

corpus

results

prosody

prosody

News

prosody

string

prosody

sequence

boundaries

recognition

Experiments

results


Input: Parsing Word-Aligned Parallel Corpora In A Grammar Induction Context We present an Earley-style dynamic programming algorithm for parsing sentence pairs from a parallel corpus simultaneously, building up two phrase structure trees and a correspondence mapping between the nodes. The intended use of the algorithm is in bootstrapping grammars for less studied languages by using implicit grammatical information in parallel corpora. Therefore, we presuppose a given (statistical) word alignment underlying in the synchronous parsing task; this leads to a significant reduction of the parsing complexity. The theoretical complexity results are corroborated by a quantitative evaluation in which we ran an implementation of the algorithm on a suite of test sentences from the Europarl parallel corpus.

dynamic programming algorithm

parsing

sentence

pairs

parallel corpus

building

phrase structure trees

correspondence

mapping

nodes

algorithm

bootstrapping

studied

languages

information

parallel corpora

statistical

word alignment

parsing

task

reduction

parsing

complexity

complexity

results

evaluation

implementation

algorithm

suite

test

sentences

parallel corpus

dynamic programming algorithm

parsing

algorithm

sentences