Download Computational Linguistics and Intelligent Text Processing: by Mari-Sanna Paukkeri, Jaakko Väyrynen, Antti Arppe (auth.), PDF
By Mari-Sanna Paukkeri, Jaakko Väyrynen, Antti Arppe (auth.), Alexander Gelbukh (eds.)
This two-volume set, together with LNCS 7181 and LNCS 7182, constitutes the completely refereed court cases of the thirteenth foreign convention on laptop Linguistics and clever Processing, held in New Delhi, India, in March 2012. the complete of ninety two complete papers have been conscientiously reviewed and chosen for inclusion within the complaints. The contents were ordered based on the subsequent topical sections: NLP method structure; lexical assets; morphology and syntax; observe feel disambiguation and named entity attractiveness; semantics and discourse; sentiment research, opinion mining, and feelings; traditional language new release; computing device translation and multilingualism; textual content categorization and clustering; details extraction and textual content mining; info retrieval and query answering; record summarization; and applications.
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Additional info for Computational Linguistics and Intelligent Text Processing: 13th International Conference, CICLing 2012, New Delhi, India, March 11-17, 2012, Proceedings, Part II
Be T a knowledge base, H the set of hypotheses given in T , ψ a document plan, and cost a function that assigns assumption costs to hypotheses. We call the process of selecting the most felicitous hypothesis na¨ıve abduction. If T, ψ, costs is a cost-based abduction problem, we try to ﬁnd h∗ ∈ H such that: ∀h ∈ H : match(h, ψ) − cost(h) ≤ match(h∗ , ψ) − cost(h∗ ) The function match(h, ψ) measures how coherently some hypothetical userinterest h integrates with a partial or complete document plan ψ.
C Springer-Verlag Berlin Heidelberg 2012 14 R. Klabunde, S. Reuße, and B. Schl¨ under game-theoretic terms). Based on this, we present our model of user-customized document generation which makes explicit reference to a user’s potential interests and allows user types to be deﬁned dynamically with reference to those interests. We will give an example of how this model is realized in a speciﬁc domain, and present the ﬁrst evaluation result how well the resulting documents have been accepted by readers.
It lists the four input factors determining the construction of a document plan. The message pool contains a set of atomic messages which the system will seek to combine by means of rhetorical relations. In this case, we assume that the data source provides some basic information (someM sg), accompanied by related messages (bgInf o, advice) which either relate additional information regarding someM sg, or dispense relevant advice. The text grammar then deﬁnes Abduction in Games for a Flexible Approach to Discourse Planning 19 Table 2.