Articles

In a discourse, events are narrated one by one linearly. But the temporal sequence of events does not always match the linear sequence of narration. One of such cases involves the mixed occurrences of durative and punctual events, as illustrated by “Mia took aspirin and slept, for she was ill. …...

AbstractTree Adjoining Grammars (TAG) are known not to be powerful enough to deal with scrambling in free word order languages. The TAG-variants proposed so far in order to account for scrambling are not entirely satisfying. Therefore, an alternative extension of TAG is introduced based on the notion of node sharing. …...

This paper gives an overview of the evaluation campaign results of the IWSLT041 workshop, which is organized by the C-STAR2 consortium to investigate novel speech transla- tion technologies and their evaluation. The objectives of this workshop is to provide a framework for the applicability vali- dation of existing machine translation …...

This paper describes the component models and combination model built as a joint effort be- tween Swarthmore College, Hong Kong PolyU, and HKUST. Though other models described elsewhere contributed to the final combination model, this pa- per focuses solely on the joint contributions to the ”Swat-HK” effort. 1...

The R2D2 systems for the English All-Words and Lexical Sample tasks at SENSEVAL-3 are based on several supervised and unsupervised methods com- bined by means of a voting procedure. Main goal was to take advantage of training data when avail- able, and getting maximum coverage with the help of methods …...

The DLSI-UA team is currently working on sev- eral word sense disambiguation approaches, both supervised and unsupervised. These approaches are based on different ways to use both annotated and unannotated data, and several resources generated from or exploiting WordNet (Miller et al., 1993), WordNet Domains, EuroWordNet (EWN) and addi- tional …...

This paper describes the National Research Coun- cil (NRC) Word Sense Disambiguation (WSD) sys- tem, as applied to the English Lexical Sample (ELS) task in Senseval-3. The NRC system approach- es WSD as a classical supervised machine learn- ing problem, using familiar tools such as the Weka machine learning software …...

This paper describes the four entries from the Uni- versity of Utah in the semantic role labeling task of SENSEVAL-3. All the entries took a statisti- cal machine learning approach, using the subset of the FrameNet corpus provided by SENSEVAL-3 as training data. Our approach was to develop a model …...

ion and Term Similarity for Word Sense Disambiguation: IRST at Senseval-3 Carlo Strapparava and Alfio Gliozzo and Claudio Giuliano ITC-irst, Istituto per la Ricerca Scientifica e Tecnologica, I-38050 Trento, ITALY {strappa, gliozzo, giuliano}@itc.it Abstract This paper summarizes IRST’s participation in Senseval-3. We participated both in the English all- words task …...

This paper presents the Swarthmore College word- sense disambiguation system which was designed for the 2004 SENSEVAL3 competition. Our system participated in five tasks: the lexical sample tasks in Basque, Catalan, Italian, Romanian, and Spanish. For each task, a suite of supervised algorithms were combined using voting to form the …...

It is known that whenever a system’s actions depend on the meaning of the text being pro- cessed, disambiguation is beneficial or even nec- essary. The contest Senseval is an international frame where the research in this important field is validated in an hierarchical manner. In this paper we present …...

The task of word sense disambiguation is to assign a sense label to a word in a passage. We report our algorithms and experiments for the two tasks that we participated in viz. the task of WSD of Word- Net glosses and the task of WSD of English lexical sample. …...

The paper describes RLSC-LIN and RLSC- COMB systems which participated in the Senseval-3 English lexical sample task. These systems are based on Regularized Least-Squares Classification (RLSC) learning method. We describe the reasons of choosing this method, how we applied it to word sense disambigua- tion, what results we obtained on …...

This article describes the implementation of I2R word sense disambiguation system (I2R − WSD) that participated in one senseval3 task: Chinese lex- ical sample task. Our core algorithm is a supervised Naive Bayes classifier. This classifier utilizes an op- timal feature set, which is determined by maximiz- ing the cross …...

Traditionally, word sense disambiguation (WSD) involves a different context model for each individual word. This paper presents a new approach to WSD using weakly supervised learning. Statistical models are not trained for the contexts of each individual word, but for the similarities between context pairs at category level. The insight …...

In this paper we describe the SSI algorithm, a structural pattern matching algorithm for WSD. The algorithm has been applied to the gloss disambiguation task of Senseval-3. 1...

Two systems from the University of Minnesota, Duluth participated in various SENSEVAL-3 lexi- cal sample tasks. The supervised learning system is based on lexical features and bagged decision trees. It participated in lexical sample tasks for the English, Spanish, Catalan, Basque, Romanian and MultiLingual English-Hindi data. The unsuper- vised system …...

This paper describes the NMSU-Pitt-UNCA word-sense disambiguation system participat- ing in the Senseval-3 English lexical sample task. The focus of the work is on using seman- tic class-based collocations to augment tradi- tional word-based collocations. Three separate sources of word relatedness are used for these collocations: 1) WordNet hypernym relations; …...

We have participated in both English all words 3.task and English lexical sample task of SENSEVAL- Our system disambiguates senses of a target word in a context by selecting a substituent among WordNet relatives of the target word, such as syn- onyms, hypernyms, meronyms and so on. The deci- sion …...

This paper describes the HKPolyU-HKUST sys- tems which were entered into the Semantic Role La- beling task in Senseval-3. Results show that these systems, which are based upon common machine learning algorithms, all manage to achieve good performances on the non-restricted Semantic Role Labeling task. 1...