Articles

Temporal relation resolution involves extraction of temporal information explicitly or implicitly embedded in a language. This information is of- ten inferred from a variety of interactive gram- matical and lexical cues, especially in Chinese. For this purpose, inter-clause relations (tempo- ral or otherwise) in a multiple-clause sentence play an important …...

QA-by-Dossier-with-Constraints is a new ap- proach to Question Answering whereby candi- date answers’ confidences are adjusted by asking auxiliary questions whose answers con- strain the original answers. These constraints emerge naturally from the domain of interest, and enable application of real-world knowledge to QA. We show that our approach signifi- …...

This paper presents a multi-layered Question An- swering (Q.A.) architecture suitable for enhanc- ing current Q.A. capabilities with the possibility of processing complex questions. That is, questions whose answer needs to be gathered from pieces of factual information scattered in different docu- ments. Specifically, we have designed a layer ori- …...

We explore the descriptive power, in terms of syn- tactic phenomena, of a formalism that extends Tree- Adjoining Grammar (TAG) by adding a fourth level of hierarchical decomposition to the three levels TAG already employs. While extending the descrip- tive power minimally, the additional level of decom- position allows us …...

We discuss existing approaches to train LR parsers, which have been used for statistical resolution of structural ambiguity. These approaches are non- optimal, in the sense that a collection of probability distributions cannot be obtained. In particular, some probability distributions expressible in terms of a context-free grammar cannot be expressed …...

We present a geometric view on bilingual lexicon extraction from comparable corpora, which allows to re-interpret the methods proposed so far and iden- tify unresolved problems. This motivates three new methods that aim at solving these problems. Empir- ical evaluation shows the strengths and weaknesses of these methods, as well …...

We investigate a number of simple methods for improving the word-alignment accuracy of IBM Model 1. We demonstrate reduction in alignment error rate of approximately 30% resulting from (1) giving extra weight to the probability of alignment to the null word, (2) smoothing probability esti- mates for rare words, and …...

In this paper we present the RWTH FSA toolkit – an efficient implementation of algorithms for creating and manipulating weighted finite-state automata. The toolkit has been designed using the principle of on-demand computation and offers a large range of widely used algorithms. To prove the superior efficiency of the toolkit, …...

Aligning words from sentences which are mutual translations is an important problem in different set- tings, such as bilingual terminology extraction, Ma- chine Translation, or projection of linguistic fea- tures. Here, we view word alignment as matrix fac- torisation. In order to produce proper alignments, we show that factors must …...

The paper describes a particular approach to multi- engine machine translation (MEMT), where we make use of voted language models to selectively combine translation outputs from multiple off-the- shelf MT systems. Experiments are done using large corpora from three distinct domains. The study found that the use of voted language …...

Exploiting unannotated natural language data is hard largely because unsupervised parameter estimation is hard. We describe deterministic annealing (Rose et al., 1990) as an appealing alternative to the Expectation- Maximization algorithm (Dempster et al., 1977). Seek- ing to avoid search error, DA begins by globally maxi- mizing an easy concave …...

We present a generative model for the unsupervised learning of dependency structures. We also describe the multiplicative combination of this dependency model with a model of linear constituency. The product model outperforms both components on their respective evalu- ation metrics, giving the best published figures for un- supervised dependency parsing …...

This paper discusses the use of statistical word alignment over multiple parallel texts for the identi- fication of string spans that cannot be constituents in one of the languages. This information is ex- ploited in monolingual PCFG grammar induction for that language, within an augmented version of the inside-outside algorithm. …...

This paper presents a Chinese word segmen- tation system which can adapt to different domains and standards. We first present a sta- tistical framework where domain-specific words are identified in a unified approach to word segmentation based on linear models. We explore several features and describe how to create training …...

In this paper, we propose a multi-criteria- based active learning approach and effec- tively apply it to named entity recognition. Active learning targets to minimize the human annotation efforts by selecting ex- amples for labeling. To maximize the con- tribution of the selected examples, we consider the multiple criteria: informative- …...

We compare two approaches for describing and gen- erating bodies of rules used for natural language parsing. In today’s parsers rule bodies do not ex- ist a priori but are generated on the fly, usually with methods based on n-grams, which are one particu- lar way of inducing probabilistic regular …...

Parsing systems which rely on hand-coded linguis- tic descriptions can only perform adequately in as far as these descriptions are correct and complete. The paper describes an error mining technique to discover problems in hand-coded linguistic descrip- tions for parsing such as grammars and lexicons. By analysing parse results for …...

A crucial step toward the goal of au- tomatic extraction of propositional in- formation from natural language text is the identification of semantic relations between constituents in sentences. We examine the problem of distinguishing among seven relation types that can oc- cur between the entities “treatment” and “disease” in bioscience …...

We extend previous work on tree kernels to estimate the similarity between the dependency trees of sen- tences. Using this kernel within a Support Vector Machine, we detect and classify relations between entities in the Automatic Content Extraction (ACE) corpus of news articles. We examine the utility of different features …...

Discovering the significant relations embedded in documents would be very useful not only for infor- mation retrieval but also for question answering and summarization. Prior methods for relation discov- ery, however, needed large annotated corpora which cost a great deal of time and effort. We propose an unsupervised method for …...