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Distributional models of word use constitute an indispensable tool in corpus based lexicological research for discovering paradigmatic relations and syntagmatic patterns (Belica et al. 2010). Recently, word embeddings (Mikolov et al. 2013) have revived the field by allowing to construct and analyze distributional models on very large corpora. This is accomplished by reducing the very high dimensionality of word cooccurrence contexts, the size of the vocabulary, to few dimensions, such as 100-200. However, word use and meaning can vary widely along dimensions such as domain, register, and time, and word embeddings tend to represent only the most prevalent meaning. In this paper we thus construct domain specific word embeddings to allow for systematically analyzing variations in word use. Moreover, we also demonstrate how to reconstruct domain specific co-occurrence contexts from the dense word embeddings.
In the context of the HyTex project, our goal is to convert a corpus into a hypertext, basing conversion strategies on annotations which explicitly mark up the text-grammatical structures and relations between text segments. Domain-specific knowledge is represented in the form of a knowledge net, using topic maps. We use XML as an interchange format. In this paper, we focus on a declarative rule language designed to express conversion strategies in terms of text-grammatical structures and hypertext results. The strategies can be formulated in a concise formal syntax which is independend of the markup, and which can be transformed automatically into executable program code.