Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. We introduce the reader to the motivations for solving the ambiguity of words and provide a description of the task. We overview supervised, unsupervised, and knowledge-based approaches. The assessment of WSD systems is discussed in the context of the Senseval/Semeval campaigns, aiming at the objective evaluation of systems participating in several different disambiguation tasks. Finally, applications, open problems, and future directions are discussed.

We present a pilot study of word-sense annotation using multiple annotators, relatively polysemous words, and a heterogenous corpus. Annotators selected senses for words in context, using an annotation interface that presented WordNet senses. Interannotator agreement (IA) results show that annotators agree well or not, depending primarily on the individual words and their general usage properties. Our focus is on identifying systematic differences across words and annotators that can account for IA variation. We identify three lexical use factors: semantic specificity of the context, sense concreteness, and similarity of senses. We discuss systematic differences in sense selection across annotators, and present the use of association rules to mine the data for systematic differences across annotators. 1

It has recently been proposed that term senses can be automatically ranked by how strongly they possess a given opinion-related property, by applying PageRank, the well known random-walk algorithm lying at the basis of the Google search engine, to a graph in which nodes are represented by eXtended WordNet synsets and links are represented by the binary relation si ◮ sk (“the gloss of synset si contains a term belonging to synset sk”). In other words, these properties are seen as “flowing ” through this graph, from the definiendum (i.e., the synset being defined) to the definiens (i.e., a synset which occurs in the gloss of the definiendum), with PageRank controlling the “hydraulics ” of this flow. In this paper we contend that two other random-walk algorithms may be equally adequate to this task, and provide an intuitive justification to support this claim. The first is a random-walk algorithm different from PageRank which we apply to the “inverse ” graph, i.e., with properties flowing from the definiens to the definiendum. The second algorithm is a bidirectional randomwalk algorithm, which assumes that properties may flow from the definiens to the definiendum and viceversa. We report results which significantly improve on the ones obtained by simple PageRank. 1.

Opinion mining has been receiving increasing attention recently, and various approaches have been suggested for mining sentiment information, such as mining attitudes or opinions about a topic or product etc. However, as far as we know, little work has been reported on citation

Abstract. The work described in this paper was originally motivated by the construction of a lexical semantic knowledge base for analysis of Ideational Metafunction of language in Systemic Functional Grammar and the Generalized Upper Model ontology. The work involves mapping FrameNet Frames with Ideational Meanings and instantiating WordNet Verb as the meaning evoking linguistic elements. As the work evolved, the developed method has allowed the assignment of sense-tagged WordNet verb to FrameNet Lexical Units of each Frame. The task is achieved by linking FrameNet Frames with SUMO (Suggested Upper Merged Ontology) concepts. We describe our method of mapping which reuses and integrates linkages between WordNet, FrameNet and SUMO. The generated verb list is furthered examined with WordNet::Similarity, a semantic similarity and relatedness measuring system. 1