A Statistics-Based Semantic Textual Entailment System
نویسندگان
چکیده
We present a Textual Entailment (TE) recognition system that uses semantic features based on the Universal Networking Language (UNL). The proposed TE system compares the UNL relations in both the text and the hypothesis to arrive at the two-way entailment decision. The system has been separately trained on each development corpus released as part of the Recognizing Textual Entailment (RTE) competitions RTE-1, RTE-2, RTE-3 and RTE-5 and tested on the respective RTE test sets.
منابع مشابه
FATE: a FrameNet-Annotated Corpus for Textual Entailment
Several studies indicate that the level of predicate-argument structure is relevant for modeling prevalent phenomena in current textual entailment corpora. Although large resources like FrameNet have recently become available, attempts to integrate this type of information into a system for textual entailment did not confirm the expected gain in performance. The reasons for this are not fully o...
متن کاملExpanded Dependency Structure based Textual Entailment Recognition System of NTTDATA for NTCIR10-RITE2
This paper describes NTT DATA’s recognizing textual entailment(RTE) systems for NTCIR10 RITE2. We participate in four Japanese tasks, BC Subtask, Unit Test, Exam BC and Exam Search[5]. Our approach uses a ratio with the same semantic relations between words. It is necessary to recognize two semantic viewpoints, which are the semantic relation and the meaning between words in a sentence, in orde...
متن کاملEffects of Using Simple Semantic Similarity on Textual Entailment Recognition
We applied various WordNet based similarity measures to the RTE (Recognizing Textual Entailment) task in order to compare the effects of them on Textual Entailment Recognition. Although the improvements over a baseline system are not big, many of them show positive effects.
متن کاملSAGAN: An approach to Semantic Textual Similarity based on Textual Entailment
In this paper we report the results obtained in the Semantic Textual Similarity (STS) task, with a system primarily developed for textual entailment. Our results are quite promising, getting a run ranked 39 in the official results with overall Pearson, and ranking 29 with the Mean metric.
متن کاملTALP at TAC 2008: A Semantic Approach to Recognizing Textual Entailment
This paper describes our experiments on Textual Entailment in the context of the Fourth Recognising Textual Entailment (RTE-4) Evaluation Challenge at TAC 2008 contest. Our system uses a Machine Learning approach with AdaBoost to deal with the RTE challenge. We perform a lexical, syntactic, and semantic analysis of the entailment pairs. From this information we compute a set of semantic-based d...
متن کامل