Senseval 3
Related Papers
Augmenting ensemble classification for Word Sense Disambiguation with a kernel PCA model
The HKUST word sense disambiguation systems benefit from a new nonlinear Kernel Principal Component Analysis (KPCA) based disambigua- tion technique. We discuss and analyze results from the Senseval-3 English, Chinese, …...
Class-based collocations for Word Sense Disambiguation
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 …...
Complementarity of lexical and simple syntactic features: The SyntaLex approach to Senseval-3
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 …...
Explorations in disambiguation using XML text representation
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 …...
Finding optimal parameter settings for high performance word sense disambiguation
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 …...
Pattern abstraction and term similarity for Word Sense Disambiguation: IRST at Senseval-3
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 …...
Semantic role labeling with Boosting, SVMs, Maximum Entropy, SNOW, and Decision Lists
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 …...
SenseLearner: Minimally supervised Word Sense Disambiguation for all words in open text
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 …...
Senseval-3 task: Word Sense Disambiguation of WordNet glosses
The SENSEVAL-3 task to perform word-sense disambiguation of WordNet glosses was designed to encourage development of technology to make use of standard lexical resources. The task was based on the …...
Senseval-3: The Italian all-words task
This paper describes the Italian all-words sense disambiguation task for Senseval-3. The annotation procedure and criteria together with the encoding of multiwords are presented. 1...
Structural semantic interconnection: a knowledge-based approach to Word Sense Disambiguation
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...
Supervised Word Sense Disambiguation with Support Vector Machines and multiple knowledge sources
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 …...
The Basque lexical-sample task
In this paper we describe the Senseval 3 Basque lexical sample task. The task comprised 40 words (15 nouns, 15 verbs and 10 adjectives) selected from the Basque WordNet. 10 …...
The Duluth lexical sample systems in Senseval-3
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 …...
The MITRE logical form generation system
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 …...
The R2D2 team at Senseval-3
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 …...
UBB system at Senseval-3
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 …...
Partially Supervised Sense Disambiguation by Learning Sense Number from Tagged and Untagged Corpora
Supervised and semi-supervised sense dis- ambiguation methods will mis-tag the in- stances of a target word if the senses of these instances are not defined in sense in- ventories or …...
Two graph-based algorithms for state-of-the-art WSD
This paper explores the use of two graph algorithms for unsupervised induction and tagging of nominal word senses based on corpora. Our main contribution is the op- timization of the …...
Word Relatives in Context for Word Sense Disambiguation
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 …...
Learning Expressive Models for Word Sense Disambiguation
We present a novel approach to the word sense disambiguation problem which makes use of corpus-based evidence com- bined with background knowledge. Em- ploying an inductive logic programming algorithm, the …...
Automated Verb Sense Labelling Based on Linked Lexical Resources
We present a novel approach for creat- ing sense annotated corpora automatically. Our approach employs shallow syntactico- semantic patterns derived from linked lex- ical resources to automatically identify in- stances …...