2005 ASemApproachToRecogTextualEntailment
- (Tatu and Moldovan, 2005) ⇒ Marta Tatu, and Dan Moldovan. (2005). “A Semantic Approach to Recognizing Textual Entailment.” In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing.
Subject Headings: Textual Entailment Algorithm, Language Computer Corporation
Notes
Cited By
~61 http://scholar.google.com/scholar?cites=13911562536230057908
Quotes
Abstract
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 systems.
Semantic relations
For this study, we adopt a revised version of the semantic relation set proposed by (Moldovan et al., 2004). Table 2 enumerates the semantic relations that we consider.
Table 2: The set of semantic relations
POSSESSION (POS) MAKE-PRODUCE (MAK)
RECIPIENT (REC) THEME-PATIENT (THM) KINSHIP (KIN) INSTRUMENT (INS) FREQUENCY (FRQ) RESULT (RSL) PROPERTY-ATTRIBUTE (PAH) LOCATION-SPACE (LOC) INFLUENCE (IFL) STIMULUS (STI) AGENT (AGT) PURPOSE (PRP) ASSOCIATED WITH (OTH) EXTENT (EXT) TEMPORAL (TMP) SOURCE-FROM (SRC) MEASURE (MEA) PREDICATE (PRD) DEPICTION (DPC) TOPIC (TPC) SYNONYMY-NAME (SYN) CAUSALITY (CSL) PART-WHOLE (PW) MANNER (MNR) ANTONYMY (ANT) JUSTIFICATION (JST) HYPERNYMY (ISA) MEANS (MNS) PROBABILITY OF EXISTENCE (PRB) GOAL (GOL) ENTAIL (ENT) ACCOMPANIMENT (ACC) POSSIBILITY (PSB) BELIEF (BLF) CAUSE (CAU) EXPERIENCER (EXP) CERTAINTY (CRT) MEANING (MNG)
…
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