We present a supervised machine learning algorithm for metonymy resolution, which exploits the similarity between examples of conventional metonymy. We show that syntactic head-mo...
The so-called Semantic Web vision will certainly benefit from automatic semantic annotation of words in documents. We present a method, called structural semantic interconnections ...
Supervised approaches to Word Sense Disambiguation (WSD) have been shown to outperform other approaches but are hampered by reliance on labeled training examples (the data acquisi...
Abstract. The Robust-WSD at CLEF 2009 aims at exploring the contribution of Word Sense Disambiguation to monolingual and multilingual Information Retrieval. The organizers of the t...
Eneko Agirre, Giorgio Maria Di Nunzio, Thomas Mand...
The present contribution aims at increasing our understanding of automatic speech recognition (ASR) errors involving frequent homophone or almost homophone words by confronting th...