Articles

Standard approaches to Chinese word seg- mentation treat the problem as a tagging task, assigning labels to the characters in the sequence indicating whether the char- acter marks a word boundary. Discrimina- tively trained models based on local char- acter features are used to make the tagging decisions, with Viterbi …...

We present an unsupervised, nonparamet- ric Bayesian approach to coreference reso- lution which models both global entity iden- tity across a corpus as well as the sequen- tial anaphoric structure within each docu- ment. While most existing coreference work is driven by pairwise decisions, our model is fully generative, producing …...

We propose a new language learning model that learns a syntactic-semantic grammar from a small number of natural language strings annotated with their semantics, along with basic assumptions about natural lan- guage syntax. We show that the search space for grammar induction is a complete gram- mar lattice, which guarantees …...

Task-solving in dialogue depends on the lin- guistic alignment of the interlocutors, which Pickering & Garrod (2004) have suggested to be based on mechanistic repetition ef- fects. In this paper, we seek confirmation of this hypothesis by looking at repetition in corpora, and whether repetition is cor- related with task …...

This paper presents a comparative study of five parameter estimation algorithms on four NLP tasks. Three of the five algorithms are well-known in the computational linguistics community: Maximum Entropy (ME) estima- tion with L2 regularization, the Averaged Perceptron (AP), and Boosting. We also in- vestigate ME estimation with L1 regularization …...

We examine the effect of contextual and acoustic cues in the disambiguation of three discourse-pragmatic functions of the word okay. Results of a perception study show that contextual cues are stronger predictors of discourse function than acoustic cues. However, acoustic features capturing the pitch excursion at the right edge of …...

We present an implemented system for the resolution of it, this, and that in tran- scribed multi-party dialog. The system han- dles NP-anaphoric as well as discourse- deictic anaphors, i.e. pronouns with VP an- tecedents. Selectional preferences for NP or VP antecedents are determined on the basis of corpus counts. …...

This paper describes a method for automat- ically learning effective dialogue strategies, generated from a library of dialogue content, using reinforcement learning from user feed- back. This library includes greetings, so- cial dialogue, chit-chat, jokes and relation- ship building, as well as the more usual clar- ification and verification components …...

In this paper, we propose a novel discrim- inative language model, which can be ap- plied quite generally. Compared to the well known N-gram language models, dis- criminative language models can achieve more accurate discrimination because they can employ overlapping features and non- local information. However, discriminative language models have …...

A standard form of analysis for linguis- tic typology is the universal implication. These implications state facts about the range of extant languages, such as “if ob- jects come after verbs, then adjectives come after nouns.” Such implications are typi- cally discovered by painstaking hand anal- ysis over a small …...

Human categorization is neither a binary nor a context-free process. Rather, some con- cepts are better examples of a category than others, while the criteria for category mem- bership may be satisfied to different degrees by different concepts in different contexts. In light of these empirical facts, WordNet’s static category …...

When a word sense disambiguation (WSD) system is trained on one domain but ap- plied to a different domain, a drop in ac- curacy is frequently observed. This high- lights the importance of domain adaptation for word sense disambiguation. In this pa- per, we first show that an active learning …...

Recent research presents conflicting evi- dence on whether word sense disambigua- tion (WSD) systems can help to improve the performance of statistical machine transla- tion (MT) systems. In this paper, we suc- cessfully integrate a state-of-the-art WSD system into a state-of-the-art hierarchical phrase-based MT system, Hiero. We show for the …...

We present a novel approach to the word sense disambiguation problem which makes use of corpus-based evidence com- bined with background knowledge. Em- ploying an inductive logic programming algorithm, the approach generates expres- sive disambiguation rules which exploit several knowledge sources and can also model relations between them. The ap- …...

Extracting tree transducer rules for syntac- tic MT systems can be hindered by word alignment errors that violate syntactic corre- spondences. We propose a novel model for unsupervised word alignment which explic- itly takes into account target language con- stituent structure, while retaining the robust- ness and efficiency of the …...

Statistical machine translation systems are usually trained on large amounts of bilin- gual text and monolingual text in the tar- get language. In this paper we explore the use of transductive semi-supervised meth- ods for the effective use of monolingual data from the source language in order to im- prove …...

We present a global discriminative statistical word order model for machine translation. Our model combines syntactic movement and surface movement information, and is discriminatively trained to choose among possible word orders. We show that com- bining discriminative training with features to detect these two different kinds of move- ment phenomena …...

We present a general framework to incor- porate prior knowledge such as heuristics or linguistic features in statistical generative word alignment models. Prior knowledge plays a role of probabilistic soft constraints between bilingual word pairs that shall be used to guide word alignment model train- ing. We investigate knowledge that …...

s. These extracted elements serve as the input to an algorithm that scores the relevance of citations with respect to structured representations of information needs, in accordance with the principles of evidence- based medicine. Starting with an initial list of citations retrieved by PubMed, our system can bring relevant abstracts …...

In this paper, we describe our work on a ran- dom walks-based approach to disambiguat- ing people in web search results, and the im- plementation of a system that supports such approach, which we used to participate at Semeval’07 Web People Search task. 1...