Articles

Current text-based question answering (QA) systems usually contain a named en- tity recogniser (NER) as a core compo- nent. Named entity recognition has tra- ditionally been developed as a component for information extraction systems, and current techniques are focused on this end use. However, no formal assessment has been done …...

The current situation for Word Sense Dis- ambiguation (WSD) is somewhat stuck due to lack of training data. We present in this paper a novel disambiguation al- gorithm that improves previous systems based on acquisition of examples by incor- porating local context information. With a basic configuration, our method is …...

NLTK, the Natural Language Toolkit, is an open source project whose goals include providing students with software and lan- guage resources that will help them to learn basic NLP. Until now, the pro- gram modules in NLTK have covered such topics as tagging, chunking, and parsing, but have not incorporated …...

We present a set of experiments involving sentence classification, addressing issues of representation and feature selection, and we compare our findings with simi- lar results from work on the more general text classification task. The domain of our investigation is an email-based help-desk corpus. Our investigations compare the use of …...

Supervised word sense disambiguation has proven incredibly difficult. Despite significant effort, there has been little suc- cess at using contextual features to accu- rately assign the sense of a word. Instead, few systems are able to outperform the de- fault sense baseline of selecting the high- est ranked WordNet sense. …...

Efficient wide-coverage parsing is integral to large-scale NLP applications. Unfortu- nately, parsers for linguistically motivated formalisms, e.g. HPSG and TAG, are often too inefficient for these applications. This paper describes two modifications to the standard CKY chart parsing algo- rithm used in the Clark and Curran (2006) Combinatory Categorial Grammar …...

The driving vision for our work is to provide intelligent, automated assistance to users in understanding the status of their email conversations. Our approach is to create tools that enable the detec- tion and connection of speech acts across email messages. We thus require a mech- anism for tagging email …...

spaces and how that intimate relation- ship might be exploited when applied in computational linguistics. It is also about an approach to information retrieval, because the analysis of natural language, especially negation, is applied to problems in IR, and indeed illustrated throughout the book by simple examples using search engines. …...

An algorithm based on the Generalized Hebbian Algorithm is described that allows the singular value decomposition of a dataset to be learned based on single observation pairs presented seri- ally. The algorithm has minimal mem- ory requirements, and is therefore in- teresting in the natural language do- main, where very …...

Faced with the problem of annotation er- rors in part-of-speech (POS) annotated corpora, we develop a method for auto- matically correcting such errors. Build- ing on top of a successful error detection method, we first try correcting a corpus us- ing two off-the-shelf POS taggers, based on the idea that …...

Unsupervised paraphrase acquisition has been an active research field in recent years, but its effective coverage and per- formance have rarely been evaluated. We propose a generic paraphrase-based ap- proach for Relation Extraction (RE), aim- ing at a dual goal: obtaining an applicative evaluation scheme for paraphrase acquisi- tion and …...

This paper presents an approach to depen- dency parsing which can utilize any stan- dard machine learning (classification) al- gorithm. A decision list learner was used in this work. The training data provided in the form of a treebank is converted to a format in which each instance represents information …...

In this paper, we propose a three-step multilingual dependency parser, which generalizes an efficient parsing algorithm at first phase, a root parser and post- processor at the second and third stages. The main focus of our work is to provide an efficient parser that is practical to use with combining …...

Unlexicalized probabilistic context-free parsing is a general and flexible approach that sometimes reaches competitive re- sults in multilingual dependency parsing even if a minimum of language-specific information is supplied. Furthermore, in- tegrating parser results (good at long de- pendencies) and tagger results (good at short range dependencies, and more easily …...

We use SVM classifiers to predict the next action of a deterministic parser that builds labeled projective dependency graphs in an incremental fashion. Non-projective dependencies are captured indirectly by projectivizing the training data for the classifiers and applying an inverse trans- formation to the output of the parser. We present …...

We present a two-stage multilingual de- pendency parser and evaluate it on 13 diverse languages. The first stage is based on the unlabeled dependency pars- ing models described by McDonald and Pereira (2006) augmented with morpho- logical features for a subset of the lan- guages. The second stage takes the …...

This paper presents a deterministic pars- ing algorithm for projective dependency grammar. In a bottom-up way the al- gorithm finds the local optimum dynam- ically. A constraint procedure is made to use more structure information. The algorithm parses sentences in linear time and labeling is integrated with the parsing. This …...

Following (McDonald et al., 2005), we present an application of a maximum spanning tree algorithm for a directed graph to non-projective labeled depen- dency parsing. Using a variant of the voted perceptron (Collins, 2002; Collins and Roark, 2004; Crammer and Singer, 2003), we discriminatively trained our parser in an on-line …...

We describe our entry in the CoNLL-X shared task. The system consists of three phases: a probabilistic vine parser (Eisner and N. Smith, 2005) that pro- duces unlabeled dependency trees, a probabilistic relation-labeling model, and a discriminative mini- mum risk reranker (D. Smith and Eisner, 2006). The system is designed …...

Our approach to dependency parsing is based on the linear model of McDonald et al.(McDonald et al., 2005b). Instead of solving the linear model using the Max- imum Spanning Tree algorithm we pro- pose an incremental Integer Linear Pro- gramming formulation of the problem that allows us to enforce linguistic …...