SENSEVAL 2

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 …...

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 …...

We describe the Dutch word sense disambigua- tion data submitted to SENSEVAL-2, and give preliminary results on the data using a WSD system based on memory-based learning and statistical keyword …...

This paper describes the sixteen Duluth entries in the SENSEVAL-2 comparative exercise among word sense disambiguation systems. There were eight pairs of Duluth systems entered in the Spanish and English …...

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 …...

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 …...

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 …...

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 …...

SENSEV AL-2: The Second International Workshop on Evaluating Word Sense Disambiguation Systems was held on July 5-6, 2001. This paper gives an overview of SENSEV AL-2, discussing the evaluation exercise, …...

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 …...

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- …...

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 …...

SENSEVAL-2 was held in Spring, 2001. It con- sisted of several tasks in various languages. In this paper, we describe our system used for one of these tasks: the Japanese …...

This paper demonstrates the substantial empirical success of classifier combination for the word sense disambiguation task. It investigates more than 10 classifier combination methods, including second order classifier stacking, over …...

A central problem of word sense disam- biguation (WSD) is the lack of manually sense-tagged data required for supervised learning. In this paper, we evaluate an ap- proach to automatically …...

In this paper, we improve an unsuper- vised learning method using the Expectation- Maximization (EM) algorithm proposed by Nigam et al. for text classification problems in order to apply it …...

In this paper we show that an unsupervised method for ranking word senses automatically can be used to iden- tify infrequently occurring senses. We demonstrate this using a ranking of …...

This paper describes the SyntaLex entries in the English Lexical Sample Task of SENSEVAL-3. There are four entries in all, where each of the dif- ferent entries corresponds to use …...

Although syntactic features offer more specific information about the context surrounding a target word in a Word Sense Disambiguation (WSD) task, in general, they have not distinguished themselves much above …...

In word sense disambiguation (WSD), the heuristic of choosing the most common sense is extremely powerful because the distribution of the senses of a word is often skewed. The problem …...

Supervised learning methods for WSD yield better performance than unsupervised methods. Yet the availability of clean training data for the former is still a severe challenge. In this paper, we …...

This paper systematically compares unsuper- vised word sense discrimination techniques that cluster instances of a target word that oc- cur in raw text using both vector and similarity spaces. The …...

. This paper shows that our WSD system using rich linguistic features achieved high accuracy in the classification of English SENSEVAL2 verbs for both fine-grained (64.6%) and coarse-grained (73.7%) senses. …...

This paper investigates whether multi- semantic-role (MSR) based selectional preferences can be used to improve the performance of supervised verb sense dis- ambiguation. Unlike conventional se- lectional preferences which are …...

Word Sense Disambiguation remains one of the most complex problems facing com- putational linguists to date. In this pa- per we present a system that combines evidence from a monolingual …...