Articles

Logic Forms, particular powerful logic representations presented in Moldovan and Rus (2001), are simple yet highly ef- fective. In this paper, the structure of Logic Forms and their generation from input text are described. The results of an evaluation comparing the Logic Forms generated by hand with those generated automatically …...

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 of word bigrams or Part of Speech tags as features. The systems rely on bagged decision trees, and focus on …...

This paper introduces SENSELEARNER – a mini- mally supervised sense tagger that attempts to dis- ambiguate all content words in a text using the senses from WordNet. SENSELEARNER partici- pated in the SENSEVAL-3 English all words task, and achieved an average accuracy of 64.6%. 1...

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 first (or predominant) sense heuristic assumes the availability of hand- tagged data. Whilst there are hand-tagged corpora available for some languages, …...

In SENSEVAL-3, CL Research participated in four tasks: English all-words, English lexical sample, disambiguation of WordNet glosses, and automatic labeling of semantic roles. This participation was performed within the development of CL Research’s Knowledge Management System, which massively tags texts with syntactic, semantic, and discourse characterizations and attributes. This System …...

We participated in the SENSEVAL-3 English lexi- cal sample task and multilinguallexical sample task. We adopted a supervised learning approach with Support Vector Machines, using only the official training data provided. No other external resources were used. The knowledge sources used were part- of-speech of neighboring words, single words in …...

In this paper, we describe our experiments on statistical word sense disambiguation (WSD) using two systems based on different ap- proaches: Na¨ıve Bayes on word tokens and Max- imum Entropy on local syntactic and seman- tic features. In the first approach, we consider a context window and a sub-window within …...

As a task in SensEval-3, Automatic Labeling of Semantic Roles is to identify frame elements within a sentence and tag them with appropriate semantic roles given a sentence, a target word and its frame. We apply Maximum Entropy classification with feature sets of syntactic patterns from parse trees and officially …...

This article describes the four systems sent by the author to the SENSEVAL-3 contest, the English lexical sample task. The best recognition rate ob- tained by one of these systems was 72.9% (fine grain score) . 1...

This paper describes the architecture and re- sults of the University of Jaén system pre- sented at the SENSEVAL-3 for the English- lexical-sample and English-All-Words tasks. The system is based on a neural network ap- proach. We have used the Learning Vector Quantization, which is a supervised learning algorithm based …...

This paper describes a hybrid system for WSD, pre- sented to the English all-words and lexical-sample tasks, that relies on two different unsupervised ap- proaches. The first one selects the senses according to mutual information proximity between a context word a variant of the sense. The second heuristic analyzes the …...

The Naïve Bayes classification proves to be a good performing tool in word sense disambiguation, although it has not yet been applied to the Romanian language. The aim of this paper is to present our WSD system, based on the NBC algorithm, that performed quite well in Senseval 3. 1...

For Sinequa’s second participation to the Senseval evaluation, two systems using contextual semantic have been proposed. Based on different approaches, they both share the same data preprocessing and enrichment. The first system is a combined approach using semantic classification trees and information retrieval techniques. For the second system, the words …...

This paper describes the system MC-WSD pre- sented for the English Lexical Sample task. The system is based on a multicomponent architecture. It consists of one classifier with two components. One is trained on the data provided for the task. The second is trained on this data and, additionally, on …...

The CIAOSENSO WSD system is based on Con- ceptual Density, WordNet Domains and frequences of WordNet senses. This paper describes the upv- unige-CIAOSENSO WSD system, we participated in the english all-word task with, and its versions used for the english lexical sample and the Word- Net gloss disambiguation tasks. In …...

This paper describes our method based on Support Vector Machines for automatically assigning seman- tic roles to constituents of English sentences. This method employs four different feature sets, one of which being first reported herein. The combination of features as well as the extended training data we considered have produced …...

In this paper, we describe MITRE’s contribution to the logical form generation track of Senseval-3. We begin with a description of the context of MITRE’s work, followed by a description of the MITRE sys- tem and its results. We conclude with a commentary on the form and structure of this …...

We describe a system for semantic role assignment built as part of the Senseval III task, based on an off-the-shelf parser and Maxent and Memory-Based learners. We focus on generalisation using several similarity measures to increase the amount of train- ing data available and on the use of EM-based clus- …...

This paper describes the exemplar based ap- proach presented by UNED at Senseval-3. In- stead of representing contexts as bags of terms and defining a similarity measure between con- texts, we propose to represent terms as bags of contexts and define a similarity measure be- tween terms. Thus, words, lemmas …...

This paper describes a system developed for the transformation of English sentences into a first order logical form representation. The method- ology is centered on the use of a dependency grammar based parser. We demonstrate the suit- ability of applying a dependency parser based solution to the given task and …...