SemCor

. This paper presents a method for the resolution of lexical ambiguity of nouns and its automatic evaluation over the Brown Corpus. The method relies on the use oil' the …...

We analyze the results of a semantic an- notation task performed by novice taggers as part of the WordNet SemCor project (Landes et al., in press). Each polysemous content word …...

It is widely accepted that tagging text with se- mantic information would improve the quality of lexical learning in corpus-based NLP methods. However available on-line taxonomies are rather entangled and …...

A number of previous experiments on the role of lexical ambiguity, in Information Retrieval are re- produced on the'IR-Semcor test collection (derived from Semcor), where both queries and documents are …...

In this paper we describe the systems we developed for the English (lexical and all- words) and Basque tasks. They were all supervised systems based on Yarowsky's Decision Lists. We …...

We present here the main ideas of the algo- rithm employed in the SMUls and SMU aw sys- tems. These systems have participated in the SENSEVAL-2 competition attaining the best …...

In this paper we show that an unsupervised method for ranking word senses automatically can be used to iden- tify infrequently occurring senses. We demonstrate this using a ranking of …...

In this paper we illustrate and evaluate an approach to the creation of high quality linguistically annotated resources based on the exploitation of aligned parallel corpora. This approach is based …...

The CIAOSENSO WSD system is based on Con- ceptual Density, WordNet Domains and frequences of WordNet senses. This paper describes the upv- unige-CIAOSENSO WSD system, we participated in the english …...

In word sense disambiguation (WSD), the heuristic of choosing the most common sense is extremely powerful because the distribution of the senses of a word is often skewed. The first …...

Distributions of the senses of words are often highly skewed. This fact is exploited by word sense disambiguation (WSD) sys- tems which back off to the predominant sense of a …...

Supervised word sense disambiguation has proven incredibly difficult. Despite significant effort, there has been little suc- cess at using contextual features to accu- rately assign the sense of a word. …...

This document describes the Word Sense Disam- biguation system used by Language Computer Cor- poration at English Coarse Grained All Word Task at SemEval 2007. The system is based on …...

In spite of decades of research on word sense disambiguation (WSD), all-words general purpose WSD has remained a dis- tant goal. Many supervised WSD systems have been built, but the …...

We present a novel approach for creat- ing sense annotated corpora automatically. Our approach employs shallow syntactico- semantic patterns derived from linked lex- ical resources to automatically identify in- stances …...

AbstractLSTM-based language models have been shown effective in Word Sense Disambiguation (WSD). In particular, the technique proposed by Yuan et al. (2016) returned state-of-the-art performance in several benchmarks, but neither …...

AbstractContextual embeddings are proved to be overwhelmingly effective to the task of Word Sense Disambiguation (WSD) compared with other sense representation techniques. However, these embeddings fail to embed sense knowledge …...

AbstractWord sense disambiguation (WSD) is a long-standing problem in natural language processing. One significant challenge in supervised all-words WSD is to classify among senses for a majority of words that …...

Many studies were recently done for investi- gating the properties of contextual language models but surprisingly, only a few of them consider the properties of these models in terms of …...