No abstract

This paper presents an approach for detecting semantic relations in noun phrases. A learning algorithm, called semantic scattering, is used to automatically label complex nominals, genitives and adjectival noun phrases with the corresponding semantic relation.

Exhaustive extraction of semantic information from text is one of the formidable goals of state-of-the-art NLP systems. In this paper, we take a step closer to this objective. We combine the semantic information provided by different resources and extract new semantic knowledge to improve the performance of a recognizing textual entailment system.

Few attempts have been made to investigate the utility of temporal reasoning within machine learning frameworks for temporal relation classification between events in news articles. This paper presents three settings where temporal reasoning aids machine learned classifiers of temporal relations: (1) expansion of the dataset used for learning; (2) detection of inconsistencies among the automatically identified relations; and (3) selection among multiple temporal relations. Feature engineering is another effort in our work to improve classification accuracy.

This paper reports on LCC's participation at the Third PASCAL Recognizing Textual Entailment Challenge. First, we summarize our semantic logical-based approach which proved successful in the previous two challenges. Then we highlight this year's innovations which contributed to an overall accuracy of 72.25% for the RTE 3 test data. The novelties include new resources, such as eXtended WordNet KB which provides a large number of world knowledge axioms, event and temporal information provided by the TARSQI toolkit, logic form representations of events, negation, coreference and context, and new improvements of lexical chain axiom generation. Finally, the system's performance and error analysis are discussed.