Articles

We describe a supervised learning approach to categorizing inter-noun relations, based on Support Vector Machines, that builds a differ- ent classifier for each of seven semantic rela- tions. Each model uses the same learning strategy, while a simple voting procedure based on five trained discriminators with vari- ous blends of …...

This paper describes a system for classify- ing semantic relations among nominals, as in SemEval task 4. This system uses a corpus of 2,500 compounds annotated with WordNet senses and covering 139 different semantic relations. Given a set of nomi- nal pairs for training, as provided in the Se- mEval …...

For our system we use the SMO implemen- tation of a support vector machine provided with the WEKA machine learning toolkit. As with all machine learning approaches, the most important step is to choose a set of features which reliably help to predict the label of the example. We used …...

The UC Berkeley team participated in the SemEval 2007 Task #4, with an approach that leverages the vast size of the Web in or- der to build lexically-specific features. The idea is to determine which verbs, preposi- tions, and conjunctions are used in sentences containing a target word pair, and …...

SenseClusters is a freely–available open– source system that served as the Univer- sity of Minnesota, Duluth entry in the SENSEVAL-4 sense induction task. For this task SenseClusters was configured to con- struct representations of the instances to be clustered using the centroid of word co- occurrence vectors that replace the …...

This paper describes a system designed to disambiguate person names in a set of Web pages. In our approach Web documents are represented as different sets of features or terms of different types (bag of words, URLs, names and numbers). We apply Ag- glomerative Vector Space clustering that uses the …...

In this article a multiclassifier approach for word sense disambiguation (WSD) prob- lems is presented, where a set of k-NN classifiers is used to predict the category (sense) of each word. In order to combine the predictions generated by the multiclas- sifier, Bayesian voting is applied. Through all the classification …...

We present a sequential Semantic Role La- beling system that describes the tagging problem as a Maximum Entropy Markov Model. The system uses full syntactic in- formation to select BIO-tokens from input data, and classifies them sequentially us- ing state-of-the-art features, with the addi- tion of Selectional Preference features. The …...

This paper describes the joint submission of two systems to the all-words WSD sub- task of SemEval-2007 task 17. The main goal of this work was to build a competitive unsupervised system by combining hetero- geneous algorithms. As a secondary goal, we explored the integration of unsupervised predictions into a …...

This paper describes a graph-based unsu- pervised system for induction and clas- sification. The system performs a two stage graph based clustering where a co- occurrence graph is first clustered to com- pute similarities against contexts. The con- text similarity matrix is pruned and the re- sulting associated graph is …...

This work describes the University of the Basque Country system (UBC-ALM) for lexical sample and all-words WSD subtasks of SemEval-2007 task 17, where it per- formed in the second and fifth positions re- spectively. The system is based on a com- bination of k-Nearest Neighbor classifiers, with each classifier learning …...

This paper presents an approach for web page clustering. The different underlying meanings of a name are discovered on the basis of the title of the web page, the body content, the common named entities across the documents and the sub-links. This in- formation is feeded into a K-Means cluster- …...

This paper presents a headline emotion clas- sification approach based on frequency and co-occurrence information collected from the World Wide Web. The content words of a headline (nouns, verbs, adverbs and adjec- tives) are extracted in order to form different bag of word pairs with the joy, disgust, fear, anger, …...

This paper describes the clustering-based approach to Word Sense Disambiguation that is followed by the TKB-UO system at SemEval-2007. The underlying disambigua- tion method only uses WordNet as external resource, and does not use training data. Re- sults obtained in both Coarse-grained En- glish all-words task (task 7) and English …...

Words in the context of a target word have long been used as features by su- pervised word-sense classifiers. Moham- mad and Hirst (2006a) proposed a way to determine the strength of association be- tween a sense or concept and co-occurring words—the distributional profile of a con- cept (DPC)—without the …...

Most of the previous works that disam- biguate personal names in Web search re- sults employ agglomerative clustering ap- proaches. However, these approaches tend to generate clusters that contain a single el- ement depending on a certain criterion of merging similar clusters. In contrast to such previous works, we have …...

In this paper, we describe our two SemEval- 2007 entries. Our first entry, for Task 5: Multilingual Chinese-English Lexical Sam- ple Task, is a supervised system that decides the most appropriate English translation of a Chinese target word. This system uses a combination of Na¨ıve Bayes, nearest neigh- bor cosine, …...

This article describes the implementation of Word Sense Disambiguation system that participated in the SemEval-2007 multilin- gual Chinese-English lexical sample task. We adopted a supervised learning approach with Maximum Entropy classifier. The fea- tures used were neighboring words and their part-of-speech, as well as single words in the context, and …...

We present two systems that pick the ten most appropriate substitutes for a marked word in a test sentence. The first system scores candidates based on how frequently their local contexts match that of the marked word. The second system, an enhancement to the first, incorporates cosine similarity us- ing …...

We describe experiments for the cross- document coreference task in SemEval 2007. Our cross-document coreference sys- tem uses an in-house agglomerative clus- tering implementation to group documents referring to the same entity. Clustering uses vector representations created by sum- marization and semantic tagging analysis components. We present evaluation results for …...