A clustering based Approach for Unsupervised Word Sense Disambiguation

We present our results on four general domain datasets for English and a Spanish dataset (Màrquez et al. Alternatively, some researchers have applied Knowledge-Based WSD to

For example, we can see in the held-out columns in Table 1 the (X’,Y’) counts obtained for the feature type prev N wf and the target word art in the Senseval-2 training data for the

The method is general, in the sense that it is not tied to any particular knowledge base, but in this work we have applied it to the Multilingual Central Repository (MCR, (Atserias

In this paper we propose a new graph-based method that uses the knowledge in a LKB (based on WordNet) in order to perform un- supervised Word Sense Disambiguation.. Our algorithm

Two kinds of systems have been defined during the long history of WSD: principled systems that define which knowledge types are useful for WSD, and robust systems that use

In order to connect both resources we used a knowledge-based Word Sense Disambiguation algo- rithm for assigning appropriate WordNet synsets to the FrameNet lexical

This technique roughly follows these steps: (i) select a set of monosemous words that are related to the different senses of the target word, (ii) query the Internet to obtain

The original Lesk algorithm (Lesk, 1986) performs WSD by calculating the relative word overlap be- tween the context of usage of a target word, and the dictionary definition of each