Articles

This paper describes a system for word sense disambiguation that participated in the Swedish Lexical Sample task of SENSEVAL-2. The system LIU-WSD is based on letting different contextual features cast votes on preferred senses according to a ranking scheme....

Our system for the SENSEVAL-2 all words task uses automatically acquired selectional prefer- ences to sense tag subject and object head nouns, along with the associated verbal pred- icates. The selectional preferences comprise probability distributions over WordN et nouns, and these distributions are conditioned on WordNet verb classes. The conditional …...

In this paper we describe the systems we developed for the English (lexical and all- words) and Basque tasks. They were all supervised systems based on Yarowsky's Decision Lists. We used Semcor for training in the English all-words task. We defined different feature sets for each language. For Basque, in …...

The major goal in ITC-irst's participation at SENSEVAL-2 was to test the role of domain in- formation in word sense disambiguation. The underlying working hypothesis is that domain labels, such as MEDICINE, ARCHITECTURE and SPORT provide a natural way to establish se- mantic relations among word senses, which can be …...

CL Research's word-sense disambiguation (WSD) system is part of the DIMAP dictionary software, designed to use any full dictionary as the basis for unsupervised disambiguation. Official SENSEV AL-2 results were generated using WordNet, and separately using the New Oxford Dictionary of English (NODE). The disambiguation functionality exploits whatever information is …...

This paper describes our use of Prolog Word Experts (PWEs) in the SENSEVAL-2 competi- tion. We explain how we specify our PWEs as sequences of transformation rules and how they can be trained on sense tagged corpus data. We give a semantics of PWEs by translating them into first order …...

This paper describes the Sprakdata-ML system as used in the SENSEV AL-2 exercise. The main focus of the paper is devoted to the process of feature extraction, preparation and organization of the test and training data....

We propose a translation selection system based on the vector space model. When each translation candidate of a word is given as a pair of expressions containing the word and its translation, selecting the transla- tion of the word can be considered equivalent to selecting the expression having the most …...

The Stanford-CS224N system is an ensemble of sim- ple classifiers. The first-tier systems are heteroge- neous, consisting primarily of naive-Bayes variants, but also including vector space, memory-based, and other classifier types. These simple classifiers are combined by a second-tier classifier, which variously uses majority voting, weighted voting, or a maxi- …...

This paper describes IITl, IIT2, and IIT3, three versions of a semantic tagging system basing its sense discriminations on WordNet examples. The system uses WordNet relations aggressively, both in identifying examples of words with similar lexical constraints and matching those examples to the context. 1...

We have participated in the SENSEVAL-2 En- glish tasks (all words and lexical sample) with an unsupervised system based on mutual infor- mation measured over a large corpus (277 mil- lion words) and some additional heuristics. A supervised extension of the system was also pre- sented to the lexical sample …...

The approach presented in this paper for Word Sense Disambiguation (WSD) is based on a combination of different views of the context. Semantic Classification Trees (SCT) are employed over a short and a multi-level view of context, including rough semantic features, while a similarity measure is used in some particular …...

We describe the University of Maryland's su- pervised sense tagger, which participated in the SENSEVAL-2 lexical sample evaluations for En- glish, Spanish, and Swedish; we also present un- official results for Basque. We designed a highly modular combination of language-independent feature extraction and supervised learning us- ing support vector machines …...

This paper describes two distinct attempts at the SENSEVAL-2 Japanese translation task. The first im- plementation is based on lexical similarity and builds on the results of Baldwin (2001b; 2001a), whereas the second is based on structural similarity via the medium of parse trees and includes a basic model of …...

In this paper we describe the organisation and results of the SENSEVAL-2 exercise for Swedish. We present some of the experiences we gained by participating as developers and organisers in the exercise. We particularly focus on the choice of the lexical and corpus material, the annotation process, the scoring scheme, …...

This paper reports an overview of SENSEVAL-2 Japanese translation task. In this task, word senses are defined according to translation dis- tinction. A translation Memory .· (TM) was constructed, which contains, for each Japanese head word, a list of typical Japanese expressions and their English translations. For each target word …...

In this paper we describe the structure, organisa- tion and results of the SENSEVAL exercise for Span- ish. We present several design decisions we taked for the exercise, we describe the creation of the gold- standard data and finally, we present the results of the evaluation. Twelve systems from five …...

This paper reports an overview of the SENSEVAL-2 Japanese dictionary task. It was a lexical sample task, and word senses are de- fined according to a Japanese dictionary, the Iwanami Kokugo Jiten. The Iwanami Kokugo Jiten and a training corpus were distributed to all participants. The number of target words …...

In this paper we give an overall description of the Italian lexical sample task for SENSEV AL-2, together with some general reflections about on the one hand the overall task of lexical-semantic annotation and on the other about the adequacy of existing lexical-semantic reference resources....

This paper describes the all-word sense disam- biguation task provided by Estonian team at SENSEVAL-2. About 10,000 words are manually disambiguated according to Estonian WordN et word senses. Language-specific problems and lexicon features are discussed. 1...