Articles

Much NLP research on Multi-Word Ex- pressions (MWEs) focuses on the discov- ery of new expressions, as opposed to the identification in texts of known expres- sions. However, MWE identification is not trivial because many expressions al- low variation in form and differ in the range of variations they allow. …...

Word sense disambiguation is typically phrased as the task of labeling a word in context with the best-fitting sense from a sense inventory such as WordNet. While questions have often been raised over the choice of sense inventory, computational linguists have readily accepted the best- fitting sense methodology despite the …...

The sense of a preposition is related to the semantics of its dominating prepositional phrase. Knowing the sense of a prepo- sition could help to correctly classify the semantic role of the dominating preposi- tional phrase and vice versa. In this pa- per, we propose a joint probabilistic model for …...

In this paper we propose a novel statistical language model to capture long-range se- mantic dependencies. Specifically, we ap- ply the concept of semantic composition to the problem of constructing predictive his- tory representations for upcoming words. We also examine the influence of the un- derlying semantic space on the …...

Experiments are reported that investi- gate the effect of various source docu- ment representations on the accuracy of the sentence extraction phase of a multi- document summarisation task. A novel representation is introduced based on generic relation extraction (GRE), which aims to build systems for relation iden- tification and characterisation …...

This paper shows that discriminative reranking with an averaged perceptron model yields substantial improvements in realization quality with CCG. The paper confirms the utility of including language model log probabilities as features in the model, which prior work on discrimina- tive training with log linear models for HPSG realization had …...

We report in this paper a way of doing Word Sense Disambiguation (WSD) that has its ori- gin in multilingual MT and that is cognizant of the fact that parallel corpora, wordnets and sense annotated corpora are scarce re- sources. With respect to these resources, lan- guages show different levels …...

We investigate the possibility of exploit- ing character-based dependency for Chi- nese information processing. As Chinese text is made up of character sequences rather than word sequences, word in Chi- nese is not so natural a concept as in En- glish, nor is word easy to be defined with- out …...

As the arm of NLP technologies extends beyond a small core of languages, tech- niques for working with instances of lan- guage data across hundreds to thousands of languages may require revisiting and re- calibrating the tried and true methods that are used. Of the NLP techniques that has been …...

-like text summarisation requires a means of producing novel summary sen- tences. In order to improve the grammati- cality of the generated sentence, we model a global (sentence) level syntactic struc- ture. We couch statistical sentence genera- tion as a spanning tree problem in order to search for the best …...

In this paper, we present a feature-based me- thod to align documents with similar content across two sets of bilingual comparable cor- pora from daily news texts. We evaluate the contribution of each individual feature and investigate the incorporation of these diverse statistical and heuristic features for the task of …...

Concept taxonomies offer a powerful means for organizing knowledge, but this organiza- tion must allow for many overlapping and fine-grained perspectives if a general-purpose taxonomy is to reflect concepts as they are actually employed and reasoned about in ev- eryday usage. We present here a means of bootstrapping finely-discriminating tax- …...

This paper presents a new, exemplar-based model of thematic fit. In contrast to pre- vious models, it does not approximate thematic fit as argument plausibility or ‘fit with verb selectional preferences’, but directly as semantic role plausibility for a verb-argument pair, through similarity- based generalization from previously seen verb-argument pairs. …...

A corpus-based technique is described to improve the efficiency of wide-coverage high-accuracy parsers. By keeping track of the derivation steps which lead to the best parse for a very large collection of sentences, the parser learns which parse steps can be filtered without significant loss in parsing accuracy, but with …...

We introduce an alternative approach to ex- tracting word pair associations from corpora, based purely on surface distances in the text. We contrast it with the prevailing window- based co-occurrence model and show it to be more statistically robust and to disclose a broader selection of significant associative re- lationships …...

In this paper, we address the problem of min- ing transliterations of Named Entities (NEs) from large comparable corpora. We leverage the empirical fact that multilingual news ar- ticles with similar news content are rich in Named Entity Transliteration Equivalents (NETEs). Our mining algorithm, MINT, uses a cross-language document similarity …...

ion Benjamin Van Durme, Phillip Michalak and Lenhart K. Schubert Department of Computer Science University of Rochester Rochester, NY 14627, USA Abstract Existing work in the extraction of com- monsense knowledge from text has been primarily restricted to factoids that serve as statements about what may possibly ob- tain in …...

This paper presents a chunking-based dis- criminative approach to full parsing. We convert the task of full parsing into a series of chunking tasks and apply a conditional random field (CRF) model to each level of chunking. The probability of an en- tire parse tree is computed as the product …...

We discuss text summarization in terms of maximum coverage problem and its vari- ant. We explore some decoding algorithms including the ones never used in this sum- marization formulation, such as a greedy algorithm with performance guarantee, a randomized algorithm, and a branch-and- bound method. On the basis of the …...

Latent conditional models have become popular recently in both natural language processing and vision processing commu- nities. However, establishing an effective and efficient inference method on latent conditional models remains a question. In this paper, we describe the latent-dynamic inference (LDI), which is able to produce the optimal label sequence …...