Articles

We describe a probabilistic approach to content se- lection for meeting summarization. We use skip- chain Conditional Random Fields (CRF) to model non-local pragmatic dependencies between paired utterances such as QUESTION-ANSWER that typi- cally appear together in summaries, and show that these models outperform linear-chain CRFs and Bayesian models in …...

This paper proposes an unsupervised lexicon building method for the detec- tion of polar clauses, which convey pos- itive or negative aspects in a specific domain. The lexical entries to be ac- quired are called polar atoms, the min- imum human-understandable syntactic structures that specify the polarity of clauses. As …...

Supervised and semi-supervised sense dis- ambiguation methods will mis-tag the in- stances of a target word if the senses of these instances are not defined in sense in- ventories or there are no tagged instances for these senses in training data. Here we used a model order identification method to …...

Ranking documents or sentences accord- ing to both topic and sentiment relevance should serve a critical function in helping users when topics and sentiment polari- ties of the targeted text are not explicitly given, as is often the case on the web. In this paper, we propose several sentiment information …...

Combining fine-grained opinion informa- tion to produce opinion summaries is im- portant for sentiment analysis applica- tions. Toward that end, we tackle the problem of source coreference resolution – linking together source mentions that re- fer to the same entity. The partially super- vised nature of the problem leads us …...

We investigate whether one can determine from the transcripts of U.S. Congressional floor debates whether the speeches repre- sent support of or opposition to proposed legislation. To address this problem, we exploit the fact that these speeches occur as part of a discussion; this allows us to use sources of …...

This paper presents a corpus-based ac- count of structural priming in human sen- tence processing, focusing on the role that syntactic representations play in such an account. We estimate the strength of struc- tural priming effects from a corpus of spontaneous spoken dialogue, annotated syntactically with Combinatory Catego- rial Grammar …...

We study unsupervised methods for learn- ing refinements of the nonterminals in a treebank. Following Matsuzaki et al. (2005) and Prescher (2005), we may for example split NP without supervision into NP[0] and NP[1], which behave differently. We first propose to learn a PCFG that adds such features to nonterminals …...

In this work we learn clusters of contex- tual annotations for non-terminals in the Penn Treebank. Perhaps the best way to think about this problem is to contrast our work with that of Klein and Man- ning (2003). That research used tree- transformations to create various gram- mars with different …...

How can proteins fold so quickly into their unique native structures? We show here that there is a natural analogy between parsing and the protein folding problem, and demonstrate that CKY can find the na- tive structures of a simplified lattice model of proteins with high accuracy. 1...

In this paper we propose a machine- learning approach to paragraph boundary identification which utilizes linguistically motivated features. We investigate the re- lation between paragraph boundaries and discourse cues, pronominalization and in- formation structure. We test our algorithm on German data and report improvements over three baselines including a reimple- …...

In this paper we describe a coreference resolution method that employs a classi- fication and a clusterization phase. In a novel way, the clusterization is produced as a graph cutting algorithm, in which nodes of the graph correspond to the men- tions of the text, whereas the edges of the …...

This paper presents a method of automat- ically constructing information extraction patterns on predicate-argument structures (PASs) obtained by full parsing from a smaller training corpus. Because PASs represent generalized structures for syn- tactical variants, patterns on PASs are ex- pected to be more generalized than those on surface words. In …...

The increasing flow of information be- tween languages has led to a rise in the fre- quency of non-native or loan words, where terms of one language appear transliter- ated in another. Dealing with such out of vocabulary words is essential for suc- cessful cross-lingual information retrieval. For example, techniques …...

In this paper we investigate unsuper- vised name transliteration using compara- ble corpora, corpora where texts in the two languages deal in some of the same top- ics — and therefore share references to named entities — but are not translations of each other. We present two distinct methods for …...

For transliterating foreign words into Chi- nese, the pronunciation of a source word is spelled out with Kanji characters. Be- cause Kanji comprises ideograms, an indi- vidual pronunciation may be represented by more than one character. However, because different Kanji characters convey different meanings and impressions, char- acters must be …...

This paper proposes a statistical, tree- to-tree model for producing translations. Two main contributions are as follows: (1) a method for the extraction of syn- tactic structures with alignment informa- tion from a parallel corpus of translations, and (2) use of a discriminative, feature- based model for prediction of these …...

We develop admissible A* search heuris- tics for synchronous parsing with Inver- sion Transduction Grammar, and present results both for bitext alignment and for machine translation decoding. We also combine the dynamic programming hook trick with A* search for decoding. These techniques make it possible to find opti- mal alignments …...

In this paper we describe a novel dis- tributed language model for N-best list re-ranking. The model is based on the client/server paradigm where each server hosts a portion of the data and provides information to the client. This model al- lows for using an arbitrarily large corpus in a …...

In this paper, we present a weakly super- vised learning approach for spoken lan- guage understanding in domain-specific dialogue systems. We model the task of spoken language understanding as a suc- cessive classification problem. The first classifier (topic classifier) is used to iden- tify the topic of an input utterance. …...