We participated in the SENSEVAL-3 English lexical sample task and multilingual lexical 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 partof-speech of neighboring words, single words in the surrounding context, local collocations, and synta...

Strict compositionality in morphological theory is problematic for explaining how language-users comprehend phenomena like the partial yet non-decomposable forms in phonaesthemes and in blends like edutainment. An alternative account, based on discriminative learning, proposes that language-users associate linguistic cues (e.g., short segment or letter strings) with multiple simultaneous possib...

We present WASP-Bench: a novel approach to Word Sense Disambiguation, also providing a semi-automatic environment for a lexicographer to compose dictionary entries based on corpus evidence. For WSD, involving lexicographers tackles the twin obstacles to high accuracy: paucity of training data and insufficiently explicit dictionaries. For lexicographers, the computational environment fills the n...

Given a target word wi to be disambiguated, we define a class of local contexts for wi such that the sense of wi is univocally determined. We call such local contexts sense discriminative and represent them with sense discriminative (SD) patterns of lexico-syntactic features. We describe an algorithm for the automatic acquisition of minimal SD patterns based on training data in SemCor. We have ...

A basic task in machine translation is to choose the right translation for source words with several possible translations in the target language. In this paper we treat word translation as a word sense disambiguation problem and train memory-based classifiers on words with alternative translations. The training data was automatically labeled with the corresponding translations by word-aligning...

This paper reports on word sense disambiguation of Korean nouns with information retrieval technique. First, context vectors are constructed using contextual words in training data. Then, the words in the context vector are weighted with local density. Each sense of a target word is represented as ‘Static Sense Vector’ in word space, which is the centroid of the context vectors. Contextual nois...

Wikipedia pages typically contain inter-language links to the corresponding pages in other languages. These links, however, are often incomplete. This paper describes a set of experiments in which the viability of discovering such missing inter-language links for ambiguous nouns by means of a cross-lingual Word Sense Disambiguation approach is investigated. The input for the inter-language link...

Sense tagging, the automatic assignment of the appropriate sense from some lexicon to each of the words in a text, is a specialised instance of the general problem of semantic tagging by category or type. We discuss which recent word sense disambiguation algorithms are appropriate for sense tagging. It is our belief that sense tagging can be carried out effectively by combining several simple, ...

This paper describes the system MC-WSD presented 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 an external training set extracted from the Wordnet glosses. The goal of the additional component i...

Current word sense disambiguation (WSD) systems based on supervised learning are still limited in that they do not work well for all words in a language. One of the main reasons is the lack of sufficient training data. In this paper, we investigate the use of unlabeled training data for WSD, in the framework of semi-supervised learning. Four semisupervised learning algorithms are evaluated on 2...