Articles

In this paper we describe our participation in the SemEval 2007 Web People Search task. Our main aim in participating was to adapt language modeling tools for the task, and to experiment with various document represen- tations. Our main finding is that single pass clustering, using title, snippet and body …...

One of the main challenges in the applica- tions (i.e.: text summarization, question an- swering, information retrieval, etc.) of Natural Language Processing is to deter- mine which of the several senses of a word is used in a given context. The problem is phrased as “Word Sense Disambiguation (WSD)” in …...

This paper describes our system for the task of extracting frame semantic structures in SemEval–2007. The system architecture uses two types of learning models in each part of the task: Support Vector Machines (SVM) and Maximum Entropy (ME). De- signed as a pipeline of classifiers, the seman- tic parsing system …...

Although researchers have shown increas- ing interest in extracting/classifying seman- tic relations, most previous studies have ba- sically relied on lexical patterns between terms. This paper proposes a novel way to accomplish the task: a system that captures a physical size of an entity. Experimental results revealed that our proposed …...

In this paper we present a semantic archi- tecture that was employed for processing two different SemEval 2007 tasks: Task 4 (Classification of Semantic Relations be- tween Nominals) and Task 8 (Metonymy Resolution). The architecture uses multi- ple forms of syntactic, lexical, and semantic information to inform a classification-based approach …...

This paper describes the University of Syd- ney’s WSD and Lexical Substitution sys- tems for SemEval-2007. These systems are principally based on evaluating the substi- tutability of potential synonyms in the con- text of the target word. Substitutability is measured using Pointwise Mutual Informa- tion as obtained from the Web1T …...

This paper reports on the participation of University of Wolverhampton and University of Alicante at the SemEval-2007 TempEval evaluation exercise. TempEval consisted of three tasks involving the identification of event-time and event-event temporal relations. We participated in all three tasks with TICTAC (Syntactico- Semantic Temporal Annotation Cluster), a system comprising …...

We describe two systems participating of the English Lexical Sample task in SemEval- 2007. The systems make use of Inductive Logic Programming for supervised learning in two different ways: (a) to build Word Sense Disambiguation (WSD) models from a rich set of background knowledge sources; and (b) to build interesting …...

We describe the Sheffield system used in TempEval-2007. Our system takes a machine-learning (ML) based approach, treating temporal relation assignment as a simple classification task and using features easily derived from the TempEval data, i.e. which do not require ‘deeper’ NLP analy- sis. We aimed to explore three questions: (1) …...

For the Affective Text task at SemEval- 2007, University Paris 7’s system first evaluates emotion and valence on all words of a news headline (using enriched versions of SentiWordNet and a subset of WordNet- Affect). We use a parser to find the head word, considering that it has a major …...

This short paper presents a system developed at the Université Paris 13 for the Semeval 2007 Metonymy Resolution Task (task #08, location name track; see Markert and Nissim, 2007). The system makes use of plain word forms only. In this paper, we evaluate the accuracy of this minimalist approach, compare …...

This paper is an outcome of ongoing re- search and presents an unsupervised method for automatic word sense induction (WSI) and disambiguation (WSD). The induction algorithm is based on modeling the co- occurrences of two or more words using hypergraphs. WSI takes place by detect- ing high-density components in the …...

In this paper we are reporting the re- sults obtained participating in the “Eval- uating Word Sense Induction and Dis- crimination Systems” task of Semeval 2007. Our totally unsupervised system performed an automatic self-term expan- sion process by mean of co-ocurrence terms and, thereafter, it executed the unsupervised KStar clustering …...

This paper describes the WSD system devel- oped for our participation to the SemEval-1. It combines various methods by means of a fuzzy Borda voting. The fuzzy Borda vote- counting scheme is one of the best known methods in the field of collective decision making. In our system the different …...

This paper describes the University of North Texas SUBFINDER system. The system is able to provide the most likely set of sub- stitutes for a word in a given context, by combining several techniques and knowl- edge sources. SUBFINDER has successfully participated in the best and out of ten (oot) …...

We describe the SUPERSENSELEARNER system that participated in the English all- words disambiguation task. The system re- lies on automatically-learned semantic mod- els using collocational features coupled with features extracted from the annotations of coarse-grained semantic categories gener- ated by an HMM tagger. 1...

We describe a system, UNN-WePS for identifying individuals from web pages us- ing data from Semeval Task 13. Our sys- tem is based on using co-presence of per- son names to form seed clusters. These are then extended with pages that are deemed conceptually similar based on a lexical chaining …...

This paper describes a supervised, knowledge-intensive approach to the auto- matic identification of semantic relations between nominals in English sentences. The system employs different sets of new and previously used lexical, syntactic, and semantic features extracted from various knowledge sources. At SemEval 2007 the system achieved an F-measure of 72.4% …...

In this paper we describe an unsuper- vised WordNet-based Word Sense Disam- biguation system, which participated (as UMND1) in the SemEval-2007 Coarse- grained English Lexical Sample task. The system disambiguates a target word by using WordNet-based measures of semantic relat- edness to find the sense of the word that is …...

This paper presents a method for auto- matic classification of semantic relations between nominals using Sequential Minimal Optimization. We participated in the four categories of SEMEVAL task 4 (A: No Query, No Wordnet; B: Word- Net, No Query; C: Query, No WordNet; D: WordNet and Query) and for all train- …...