In this paper we present two original methods for recognizing textual inference.First one is a modified resolution method such that some linguistic considerations are introduced in the unification of two atoms. The approach is possible due to the recent methods of transforming texts in logic formulas. Second one is based on semantic relations in text, as presented in WordNet. Some similarities ...

This paper describes the THK system that participated in the BC subtask, MC subtask, ExamBC subtask and UnitTest in NTCIR-10 RITE-2. Our system learns plausible transformations of pairs of Text t1 and Hypothesis t2 only from semantic labels of the pairs using a discriminative probabilistic model combined with the framework of Natural Logic. The model is trained so as to prefer alignments and th...

We define a novel textual entailment task that requires inference over multiple premise sentences. We present a new dataset for this task that minimizes trivial lexical inferences, emphasizes knowledge of everyday events, and presents a more challenging setting for textual entailment. We evaluate several strong neural baselines and analyze how the multiple premise task differs from standard tex...

In this paper, we describe our submission to the Shared Task #1. We tried to follow the underlying idea of the task, that is, evaluating the gap of full-fledged recognizing textual entailment systems with respect to compositional distributional semantic models (CDSMs) applied to this task. We thus submitted two runs: 1) a system obtained with a machine learning approach based on the feature spa...

This paper introduces BIUTEE1, an opensource system for recognizing textual entailment. Its main advantages are its ability to utilize various types of knowledge resources, and its extensibility by which new knowledge resources and inference components can be easily integrated. These abilities make BIUTEE an appealing RTE system for two research communities: (1) researchers of end applications,...

This paper proposes a methodology for the creation of specialized data sets for Textual Entailment, made of monothematic Text-Hypothesis pairs (i.e. pairs in which only one linguistic phenomenon relevant to the entailment relation is highlighted and isolated). The annotation procedure assumes that humans have knowledge about the linguistic phenomena relevant to inference, and a classification o...

We participated in Japanese tasks of RITE-2 in NTCIR10 (team id: “KYOTO”). Our proposed method regards predicate-argument structure as a basic unit of handling the meaning of text/hypothesis, and performs the matching between text and hypothesis. Our system first performs predicate-argument structure analysis to both a text and a hypothesis. Then, we perform the matching between text and hypoth...

The aim of the current work is to see how well existing techniques for textual entailment work when applied to Arabic, and to propose extensions which deal with the specific problems posed by the language. Arabic has a number of characteristics, described below, which make it particularly challenging to determine the relations between sentences. In particular, the lack of diacritics means that ...

Manning [1] presents an extended critique of a paper by Zaenen, Karttunen and Crouch [2] (henceforth ZKC), which was a commentary on the PASCAL Recognizing Textual Entailment (RTE) challenge [3]. The ZKC paper was entitled “Local textual inference: can it be defined or circumscribed?” and argued that the PASCAL data should have been better defined and circumscribed so as to distinguish between ...

In this paper, we present a system for automatically answering opendomain, multiple choice reading comprehension questions about short English narrative texts. The system is based on state-of-the-art text similarity measures, textual entailment metrics and coreference resolution and does not make use of any additional domain specific background knowledge. Each answer option is scored with a com...