Articles

We present a simple, two-steps supervised strategy for the identification and classifica- tion of thematic roles in natural language texts. We employ no external source of in- formation but automatic parse trees of the in- put sentences. We use a few attribute-value features and tree kernel functions applied to specialized …...

We describe about the system description of the PSNUS team for the SemEval-2007 Web People Search Task. The system is based on the clustering of the web pages by us- ing a variety of features extracted and gen- erated from the data provided. This system achieves Fα=0.5 = 0.75 and …...

This article introduces an unsupervised word sense disambiguation algorithm that is in- spired by the lexical attraction models of Yuret (1998). It is based on the assump- tion that the meanings of the words that form a sentence can be best assigned by con- structing an interpretation of the whole …...

This paper describes an exponential family model of word sense which captures both occurrences and co-occurrences of words and senses in a joint probability distribution. This statistical framework lends itself to the task of word sense disambiguation. We eval- uate the performance of the model in its par- ticipation on …...

In this paper, we described the PNNL Word Sense Disambiguation system as ap- plied to the English all-word task in Se- mEval 2007. We use a supervised learning approach, employing a large number of features and using Information Gain for di- mension reduction. The rich feature set combined with a …...

This paper presents the word sense disam- biguation system of Peking University which was designed for the SemEval-2007 competition. The system participated in the Web track of task 11 “English Lexical Sample Task via English-Chinese Parallel Text”. The system is a hybrid model by combining two supervised learning algo- rithms …...

Optimal ensembling (OE) is a word sense disambiguation (WSD) method using word-specific training factors (average pos- itive vs negative training per sense, posex and negex) to predict best system (classifier algorithm / applicable feature set) for given target word. Our official entry (OE1) in Senseval-4 Task 17 (coarse-grained En- glish …...

We participated in the SemEval-2007 coarse-grained English all-words task and fine-grained English all-words task. We used a supervised learning approach with SVM as the learning algorithm. The knowledge sources used include local col- locations, parts-of-speech, and surrounding words. We gathered training examples from English-Chinese parallel corpora, SEMCOR, and DSO corpus. …...

We participated in SemEval-1 English coarse-grained all-words task (task 7), En- glish fine-grained all-words task (task 17, subtask 3) and English coarse-grained lex- ical sample task (task 17, subtask 1). The same method with different labeled data is used for the tasks; SemCor is the labeled corpus used to train …...

In this paper, we outline our approach to interpreting semantic relations in nominal pairs in SemEval-2007 task #4: Classifica- tion of Semantic Relations between Nomi- nals. We build on two baseline approaches to interpreting noun compounds: sense col- location, and constituent similarity. These are consolidated into an overall system in …...

This paper describes a maxent-based prepo- sition sense disambiguation system entry to the preposition sense disambiguation task of the SemEval 2007. This system uses a wide variety of semantic and syntactic fea- tures to perform the disambiguation task and achieves a precision of 69.3% over the test data. 1...

In this paper we describe the MELB-MKB system, as entered in the SemEval-2007 lex- ical substitution task. The core of our sys- tem was the “Relatives in Context” unsuper- vised approach, which ranked the candidate substitutes by web-lookup of the word se- quences built combining the target context and each …...

We describe our contribution to the SemEval task on Frame-Semantic Structure Extrac- tion. Unlike most previous systems de- scribed in literature, ours is based on depen- dency syntax. We also describe a fully auto- matic method to add words to the FrameNet lexical database, which gives an improve- ment in …...

This document describes the Word Sense Disam- biguation system used by Language Computer Cor- poration at English Coarse Grained All Word Task at SemEval 2007. The system is based on two su- pervised machine learning algorithms: Maximum Entropy and Support Vector Machines. These algo- rithms were trained on a corpus …...

This paper explores a hybrid approach to temporal information extraction within the TimeML framework. Particularly, we focus on our initial efforts to apply machine learning techniques to identify temporal relations as defined in a constrained manner by the TempEval-2007 task. We explored several machine learning models and human rules to …...

This document provides a description of the Language Computer Corporation (LCC) SRN System that participated in the SemE- val 2007 Semantic Relation between Nominals task. The system combines the outputs of different binary and multi-class classifiers build using machine learning al- gorithms like Decision Trees, Semantic Scattering, Iterative Semantic Specializa- …...

Data sparsity is one of the main factors that make word sense disambiguation (WSD) difficult. To overcome this problem we need to find effective ways to use resources other than sense labeled data. In this paper I de- scribe a WSD system that uses a statistical language model based on …...

This paper presents an Extended WordNet based word sense disambiguation system using a major modification to the Lesk al- gorithm. The algorithm tries to disambigu- ate nouns, verbs and adjectives. The algo- rithm relies on the POS-sense tagged syn- set glosses provided by the Extended WordNet. The basic unit of …...

This paper presents an approach to person name disambiguation using K-means clus- tering on rich-feature-enhanced document vectors, augmented with additional web- extracted snippets surrounding the polyse- mous names to facilitate term bridging. This yields a significant F-measure improvement on the shared task training data set. The pa- per also illustrates …...

In this paper we describe a person clus- tering system for web pages and report the results we have obtained on the test set of the Semeval 2007 Web Person Search task. Deciding which particular person a name refers to within a text document depends mainly on the capac- ity …...