The vast majority of work on word senses has relied on predefined sense inventories and an annotation schema where each word instance is tagged with the best fitting sense.

The responses from both experiments correlate with the overlap of paraphrases from the English lexical substitution task which bodes well for the use of substitutes as a proxy for word sense .

WSsim is a word sense annotation task using WordNet senses.5 Unlike previous word sense annotation projects, we asked annotators to provide judgments on the applicability of every WordNet sense of the target lemma with the instruction: 6

In traditional word sense annotation, such bias could be introduced directly through annotation guidelines or indirectly, through tools that make it easier to assign fewer senses.

The vast majority of work on word sense tagging has assumed that predefined word senses from a dictionary are an adequate proxy for the task, although of course there are issues with this enterprise both in terms of cognitive validity (Hanks, 2000; Kilgarriff, 1997; Kilgarriff, 2006) and adequacy for computational linguistics applications (Kilgarriff, 2006).

Furthermore, given a predefined list of senses, annotation efforts and computational approaches to word sense disambiguation (WS D) have usually assumed that one best fitting sense should be selected for each usage.

In the first one, referred to as WSsim ( Word Sense Similarity), annotators give graded ratings on the applicability of WordNet senses.

Manual word sense assignment is difficult for human annotators (Krishnamurthy and Nicholls, 2000).

Reported inter-annotator agreement (ITA) for fine-grained word sense assignment tasks has ranged between 69% (Kilgarriff and Rosenzweig, 2000) for a lexical sample using the HECTOR dictionary and 78.6% using WordNet (Landes et al., 1998) in all-words annotation.

The task was proposed following a background of discussions in the WSD community as to the adequacy of predefined word senses .

Undirected edges in the graph denote translations between words: an edge e E 5 between (ml, [1) and (mg, [2) represents the belief that ml and ’LU2 share at least one word sense .

TRANS GRAPH searched for paths in the graph between two vertices and estimated the probability that the path maintains the same word sense along all edges in the path, even when the edges come from different dictionaries.

One formula estimates the probability that two multilingual dictionary entries represent the same word sense , based on the proportion of overlapping translations for the two entries.

ing word senses across multiple, independently-authored dictionaries.

However, if A, B, and C are on a circuit that starts at A, passes through B and C and returns to A, there is a high probability that all nodes on that circuit share a common word sense , given certain restrictions that we enumerate later.

Each clique in the graph represents a set of vertices that share a common word sense .

When two cliques intersect in two or more vertices, the intersecting vertices share the word sense of both cliques.

This paper proposes to solve the bottleneck of finding training data for word sense disambiguation (WSD) in the domain of web queries, where a complete set of ambiguous word senses are unknown.

In this paper, we present a combination of active learning and semi-supervised learning method to treat the case when positive examples, which have an expected word sense in web search result, are only given.

When retrieving texts from Web archive, we often suffer from word sense ambiguity and WSD system is indispensable.

Because target words are often proper nouns, their word senses are rarely listed in handcrafted lexicon.

In selecting pseudo negative dataset, we predict word sense of each unlabeled example using the