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In dieser Arbeit wurde untersucht, ob es möglich ist, domänenspezifische Informationsbedürfnisse, die während der Konversation entstehen, vorherzusagen. Als Domäne wurde der Bereich „Kochen“ verwendet. Dazu wurden tendenziell naturalistische, simulierte Kochexperimente mit 45 Personen durchgeführt. Die dabei erhobenen annotierten Sprachdaten dienten als Input für einen Random Forest-Klassifikator, der Word Embeddings als Features zur Klassifikation verwendet. Es konnten Klassifikationsgenauigkeiten bis zu 44% erzielt werden. Die Ergebnisse zeigten, dass es prinzipiell möglich ist, Informationsbedürfnisse in einer bestimmten Domäne automatisiert vorherzusagen., This thesis examined whether it is feasible to detect domain specific information needs during a conversation. For this purpose, a rather naturalistic and simulated approach was used in the cooking domain. Cooking experiments with 45 persons have been conducted. The speech data of these conversations was annotated and used as input for a random forest classifier employing word embeddings as features. Finally, accuracies up to 44% were achieved which leads to the conclusion that it is generally possible to detect domain specific information needs.

As stated in Perinan-Pascual and Arcas Tunez (2010, p. 2671) FunGramKB´s lexical and grammatical levels are grounded in sound linguistic theories, allowing the system to capture syntactic-semantic generalizations which are able to provide both explanations and predictions of language phenomena. The linguistic foundation of FunGramKB is inspired on Role and Reference Grammar (RRG, Van Valin, 2005) and the Lexical Constructional Model (LCM, Ruiz de Mendoza & Mairal Uson, 2008; Mairal Uson & Ruiz de Mendoza, 2008). The close interrelation between these grammatical theories and FungramKB is patent in the design of ARTEMIS (Automatically Representing Text Meaning via an Interlingua-Based System”), an NLP system whose objective is the simulation of natural language understanding. In doing so, ARTEMIS becomes a development of the syntax-to-semantics linking algorithm proposed in RRG which involves a parsing process that starts with a natural language sentence, extracts its morphosyntactic features and provides a representation of these in terms of the so-called layered structure of the clause (LSC) in RRG. Because of its centrality, there is a vital need for a full development of ARTEMIS. This paper aims at contributing to this elaboration by proposing the set of rules and operations necessary for the treatment of Yes/No interrogative sentences within such an NLP system.