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 introduce Wiktionary as an emerging lexical semantic resource that can be used as a substitute for expert-made resources in AI applications. We evaluate Wiktionary on the pervasive task of computing semantic relatedness for English and German by means of correlation with human rankings and solving word choice problems. For the first time, we apply a concept vector based measure to a set of different concept representations like Wiktionary pseudo glosses, the first paragraph of Wikipedia articles, English WordNet glosses, and GermaNet pseudo glosses. We show that: (i) Wiktionary is the best lexical semantic resource in the ranking task and performs comparably to other resources in the word choice task, and (ii) the concept vector based approach yields the best results on all datasets in both evaluations.

Abstract. In the project we describe, we have taken a basic core of about 5000 synsets in WordNet that are the most frequently used, and we have categorized these into sixteen broad categories, including, for example, time, space, scalar notions, composite entities, and event structure. core theories of commonsense knowledge, including those for the mentioned areas. These theories explicate the basic predicates in terms of which the most common word senses need to be defined or characterized. We are now encoding axioms that link the word senses to the core theories. This may be thought of as a kind of “advanced lexical decomposition”, where the “primitives ” into which words are “decomposed” are elements in coherently worked-out theories. In this paper we focus on our work on the 450 of these synsets that are concerned with events and their structure. 1

Abstract. Over the last few years there has been increased research in automated question-answering from text, including questions whose answer is implied, rather than explicitly stated, in the text. WordNet has played a central role in many such systems (e.g., 21 of the 26 teams in the recent PASCAL RTE3 challenge used WordNet), and thus WordNet is being increasingly stretched to play more semantic tasks in applications. As part of our current research, we are exploring some of the new demands which question-answering places on WordNet, and how it might be further extended to meet them. In this paper, we present some of these new requirements, and some of the extensions that we are currently making to WordNet in response.

Converting linear text documents into documents publishable in a hypertext environment is a complex task requiring methods for segmentation, reorganization, and linking. The HyTex project, funded by the German Research Foundation (DFG), aims at the development of conversion strategies based on text-grammatical features. One focus of our work is on topic-based linking strategies using lexical chains, which can be regarded as partial text representations and form the basis of calculating topic views, an example of which is shown in Figure 1. This paper discusses the development of our lexical chainer, called GLexi, as well as several experiments on two aspects: Firstly, the manual annotation of lexical chains in German corpora of specialized text; secondly, the construction of topic views. The principle of lexical chaining is based on the concept of lexical cohesion as described by Halliday and Hasan (1976). Morris and Hirst (1991) as well as Hirst and St-Onge (1998) developed a method of automatically calculating lexical chains by drawing on a thesaurus or word net. This method employs information on semantic relations between pairs of words as a connector, i.e. classical lexical semantic relations such as synonymy and hypernymy

with verb-noun relations – a case study of lexical acquisition

Finding words in an assistive communication device can be challenging and time-consuming for individuals with lexical access disorders like those caused by aphasia. These users have persistent difficulties accessing and retrieving words due to impaired semantic links in their mental lexicon. As a result, they can easily get lost in a vocabulary hierarchy or become confused and discouraged if extensive browsing of large word collections is required. We describe the design of the Visual Vocabulary for Aphasia (ViVA) which attempts to provide effective word finding by organizing words in a dynamic semantic network where links between words reflect word association measures, human judgments of semantic similarity, and past vocabulary usage. We present results from preliminary evaluation of ViVA and discuss the challenges inherent to evaluating adaptive assistive communication tools.

1. Program goal: incorporating content words into a model of entailment One of the most important properties of human languages is their ability to convey intricate meanings. The vastness and effectiveness of those meanings for everyday communication and reasoning transcends all known non-human languages, including other animal languages and artificial languages. For studying communication and reasoning in language, an indispensable empirical concept is entailment: the relation between premises and valid conclusions expressed as natural language sentences. Entailment relations may appear between sentences due to the presence of words and expressions like other, either…or, not or exactly five, whose meanings have been studied in logical frameworks since antiquity. However, entailments may also appear due to semantic properties of “non-logical ” words like parrot, hug, far or knowledge. Such content words constitute the bulk of the lexicon in all natural languages. Without considering them, it is simply impossible to understand entailment phenomena and human reasoning in general. However, while content words have played an important role in cognitive psychology and artificial intelligence, their meanings have turned out to be richer

In this paper, we introduce W 2 ANE, an Online Multimedia Language Assistant for individuals with aphasia, a language disorder that affects millions of people. W 2 ANE offers a rich online multimedia library (OMLA) supported by an adaptable and adaptive vocabulary scaffold (ViVA). The system, accessible over the Internet, provides a platform for applications such as looking up unknown words, constructing phrases for communication, practicing pronunciations, and accessing content. W 2 ANE also enables resource sharing and remote collaboration.

Providing sets of semantically related words in the lexical entries of an electronic dictionary should help language learners quickly understand the meaning of the target words. Relational information might also improve memorisation, by allowing the generation of structured vocabulary study lists. However, an open issue is which semantic relations are cognitively most salient, and should therefore be used for dictionary construction. In this paper, we present a concept description elicitation experiment conducted with German and Italian speakers. The analysis of the experimental data suggests that there is a small set of concept-class–dependent relation types that are stable across languages and robust enough to allow discrimination across broad concept domains. Our further research will focus on harvesting instantiations of these classes from corpora. 1