Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Text : now in 2D! A framework for lexical expansion with contextual similarity

Biemann C., Riedl M.

Journal of Language Modelling

2013

Vol. 1, No. 1

55--95

A new metaphor of two-dimensional text for data-driven semantic modeling of natural language is proposed, which provides an entirely new angle on the representation of text: not only syntagmatic relations are annotated in the text, but also paradigmatic relations are made explicit by generating lexical expansions. We operationalize distributional similarity in a general framework for large corpora, and describe a new method to generate similar terms in context. Our evaluation shows that distributional similarity is able to produce high-quality lexical resources in an unsupervised and knowledge-free way, and that our highly scalable similarity measure yields better stores in a WordNet-based evaluation than previous measures for very large corpora. Evaluating on a lexical substitution task, we find that our contextualization method improves over a non-contextualized baseline across all parts of speech, and we show how the metaphor can be applied successfully to part-of-speech tagging. A number of ways to extend and improve the contextualization method within our Framework are discussed. As opposed to comparable approaches, our framework defines a model of lexical expansions in context that can generate the expansions as opposed to ranking a given list, and thus does not require existing lexical-semantic resources.

Mapping speech streams to conceptual structures

Melz R., Biemann C., Böhm K., Heyer G., Schmidt F.

Archives of Control Sciences

2005

Vol. 15, no. 3

359--370

We describe a software system that processes textual data and spoken input streams of natural language and arranges the information in a meaningful way on the screen: concepts as nodes, relations as edges. For spoken input, the software simulates conceptual awareness. A naturally spoken speech stream is converted into a word stream (speech-to-text), the most significant concepts are extracted and associated to related concepts, which have not been mentioned by the speaker(s) yet. The result is displayed on a screen as a conceptual structure.