The authors present a computational model that builds a holographic lexicon representing both word meaning and word order from unsupervised experience with natural language. The model uses simple convolution and superposition mechanisms (cf. B. B. Murdock, 1982) to learn distributed holographic representations for words. The structure of the resulting lexicon can account for empirical data from classic experiments studying semantic typicality, categorization, priming, and semantic constraint in sentence completions. Furthermore, order information can be retrieved from the holographic representations, allowing the model to account for limited word transitions without the need for built-in transition rules. The model demonstrates that a broad range of psychological data can be accounted for directly from the structure of lexical representations learned in this way, without the need for complexity to be built into either the processing mechanisms or the representations. The holographic representations are an appropriate knowledge representation to be used by higher order models of language comprehension, relieving the complexity required at the higher level.

Objective: In this paper, we investigate the use of lexical knowledge for determining consistency in biomedical terminologies. We focus on adjectival modification as a way of assessing the systematic use of linguistic phenomena to represent similar lexical or semantic features in the constituent terms of a vocabulary.

In this paper we report on the use of feature structures to represent the linguistic information of a corpus. This approach has been adopted in TyPTex, a project which aims at providing a generic architecture for corpora profiling. After a brief overview of the Typtex project, we show that corpora exploration requires manipulating linguistic features in order to obtain a required level of linguistic information or changing the set of features to get a new point of view on the data. We show that feature structures formalism can help the building and management of linguistic features with Meta-Rules based on unification. Finally, we provide an example of marking which uses a mixed approach between projection of information from a static lexicon and contextual marking via Meta-Rules. Results tend to show that the use of feature structures can improve the coverage and reliability of the marking.

Abstract. WordNet is an electronic lexical database structured around psychological and linguistic principles. As such, it should play a part in any e ort to integrate cognitive factors into knowledge based systems. Yet, some of its basic assumptions have been attacked, and the suggestion made that a major restructuring would make it more cognitively transparent. We investigate these allegations from a psycholinguistic perspective and conclude that WordNet is in fact rigorous in terms of the cognitive principles it embodies. What is lacking is a methodology for translating the explicit and implicit knowledge in WordNet into a usable, formal ontologies. We show some ways in which WordNet should be extended to facilitate this process. We agree that WordNet is not in itself ready for use as a formal ontology, but we argue that it is an invaluable tool for describing the conceptualized structure of our world, and should be used as a fundamental resource. 1

In this paper we propose a small set of lexical conceptual relations which allow to encode adjectives in computational relational lexica in a principled and integrated way. Our main motivation comes from the fact that adjectives and certain classes of verbs, related in a way or another with adjectives, do not have a satisfactory representation in this kind of lexica. This is due to a great extent to the heterogeneity of their semantic and syntactic properties. We sustain that such properties are mostly derived from the relations holding between adjectives and other POS. Accordingly, our proposal is mainly concerned with the specification of appropriate cross-POS relations to encode adjectives in lexica of the type considered here. 1

Some documents provoke emotions in people viewing them. Will it be possible to describe emotions consistently and use this information in retrieval systems? We tested collective (statistically aggregated) emotion indexing using images as examples. Considering psychological results, basic emotions are anger, disgust, fear, happiness, and sadness. This study follows an approach developed by Lee and Neal (2007) for music emotion retrieval and applies scroll bars for tagging basic emotions and their intensities. A sample comprising 763 persons tagged emotions caused by images (retrieved from www.Flickr.com) applying scroll bars and (linguistic) tags. Using SPSS, we performed descriptive statistics and correlation analysis. For more than half of the images, the test persons have clear emotion favorites. There are prototypical images for given emotions. The document-specific consistency of tagging using a scroll bar is, for some images, very high. Most of the (most commonly used) linguistic tags are on the basic level (in the sense of Rosch’s basic level theory). The distributions of the linguistic tags in our examples follow an inverse power-law. Hence, it seems possible to apply collective image emotion tagging to image information systems and to present a new search option for basic emotions. This article is one of the first steps in the research area of emotional information retrieval (EmIR).