Articles

We present several statistical models of syntactic constituent order for sentence realization. We compare several models, including simple joint models inspired by existing statistical parsing models, and several novel conditional models. The conditional models leverage a large set of linguistic features without manual feature selection. We apply and evaluate the …...

Past work of generating referring expressions mainly utilized attributes of objects and bi- nary relations between objects. However, such an approach does not work well when there is no distinctive attribute among objects. To overcome this limitation, this paper proposes a method utilizing the perceptual groups of ob- jects and …...

The identification of the key concepts in a set of documents is a useful source of information for several information access applications. We are interested in its application to multi-document summarization, both for the automatic genera- tion of summaries and for interactive summa- rization systems. In this paper, we study …...

This paper presents a versatile system intended to acquire paraphrastic phrases from a representative corpus. In order to decrease the time spent on the elaboration of resources for NLP system (for example Information Extraction, IE hereafter), we suggest to use a knowledge acquisition module that helps extracting new information despite …...

We are developing an automatic method to compile an encyclopedic corpus from the Web. In our previous work, paragraph-style descriptions for a term are extracted from Web pages and organized based on domains. However, these descriptions are independent and do not comprise a condensed text as in hand-crafted encyclopedias. To …...

The decoding problem in Statistical Ma- chine Translation (SMT) is a computation- ally hard combinatorial optimization prob- lem. In this paper, we propose a new al- gorithmic framework for solving the decod- ing problem and demonstrate its utility. In the new algorithmic framework, the decod- ing problem can be solved …...

We propose a path-based transfer model for machine translation. The model is trained with a word-aligned parallel corpus where the source language sentences are parsed. The training algorithm extracts a set of transfer rules and their probabilities from the training corpus. A rule translates a path in the source language …...

New words such as names, technical terms, etc appear frequently. As such, the bilingual lexicon of a machine translation system has to be constantly updated with these new word translations. Comparable corpora such as news documents of the same period from dif- ferent news agencies are readily available. In this …...

The identification of authorship falls into the category of style classification, an interesting sub-field of text categorization that deals with properties of the form of linguistic expression as opposed to the content of a text. Various fea- ture sets and classification methods have been proposed in the literature, geared towards …...

Terminological variation is an integral part of the linguistic ability to realise a concept in many ways, but it is typically considered an obstacle to automatic term recognition (ATR) and term management. We present a method that integrates term variation in a hybrid ATR approach, in which term candidates are …...

Part of speech taggers based on Hidden Markov Models rely on a series of hypothe- ses which make certain errors inevitable. The idea developed in this paper consists in allowing a limited, controlled ambiguity in the output of the tagger in order to avoid a number of errors. The ambiguity …...

Chinese word segmentation is a difficult, im- portant and widely-studied sequence modeling problem. This paper demonstrates the abil- ity of linear-chain conditional random fields (CRFs) to perform robust and accurate Chi- nese word segmentation by providing a prin- cipled framework that easily supports the in- tegration of domain knowledge in …...

We present a new HMM tagger that exploits context on both sides of a word to be tagged, and evaluate it in both the unsupervised and supervised case. Along the way, we present the first comprehensive comparison of unsupervised methods for part-of-speech tagging, noting that published results to date have …...

The tedious task of responding to a backlog of email is one which is familiar to many researchers. As a subset of email management, we address the problem of constructing a summary of email discussions. Specifically, we examine ongoing discussions which will ultimately culminate in a consensus in a decision-making …...

In this paper, we introduce a large-scale test collec- tion for multiple document summarization, the Text Summarization Challenge 3 (TSC3) corpus. We detail the corpus construction and evaluation mea- sures. The significant feature of the corpus is that it annotates not only the important sentences in a doc- ument set, …...

Current rule induction techniques based on hard matching (i.e., strict slot-by-slot matching) tend to fare poorly in extracting information from natural language texts, which often exhibit great variations. The reason is that hard matching techniques result in relatively high precision but low recall. To tackle this problem, we take advantage …...

Spelling recognition is an approach to enhance a speech recognizer’s ability to cope with incorrectly recognized words and out-of-vocabulary words. This paper presents a general framework for Thai speech recognition enhanced with spelling recognition. In order to implement Thai spelling recognition, Thai alphabets and their spelling methods are analyzed. Based …...

This paper presents a constraint-based multi- agent strategy to coreference resolution of general noun phrases in unrestricted English text. For a given anaphor and all the preceding referring expressions as the antecedent candidates, a common constraint agent is first presented to filter out invalid antecedent candidates using various kinds of …...

The paper aims at a deeper understanding of sev- eral well-known algorithms and proposes ways to optimize them. It describes and discusses factors and strategies of factor interaction used in the algo- rithms. The factors used in the algorithms and the algorithms themselves are evaluated on a German corpus annotated …...

Comparisons of automatic evaluation metrics for machine translation are usually conducted on corpus level using correlation statistics such as Pearson’s product moment correlation coefficient or Spearman’s rank order correlation coefficient between human scores and automatic scores. However, such comparisons rely on human judgments of translation qualities such as adequacy and …...