Nowadays automatic systems for detecting and measuring textual similarity are being developed, in order to apply them to different tasks in the field of Natural Language Processing (NLP). Currently, these systems use surface linguistic features or statistical information. Nowadays, few researchers use deep linguistic information. In this work, we present an algorithm for detecting and measuring...

This paper reports the methods used by the EHIME team for textual entailment recognition in NTCIR-10, RITE-2. We participated in the Japanese BC subtask and Japanese MC subtask. We used Markov logic to infer textual entailment relations. In our Markov logic network, words and hyponyms are used as features.

In this paper we develop discourse approach to text interpretation. Being concerned with the study of language in use (written texts of all kinds and spoken data, from conversation to highly institutionalized forms of talk), discourse analysis has built a significant foundation for applied linguistics, including ELT. Discourse approach to text interpretation implies three-dimensional semiotic a...

New applications like office information systems need interfaces to data bases which integrate classical data manipulation with management and retrieval of textual (“unformatted”) data. The relational data model is widely accepted as a high level interface to classical (“formatted”) data management. It turns out, however, to be inconvenient for handling even simple data structures as commonly u...

This paper describes the SimBow system submitted at SemEval2017-Task3, for the question-question similarity subtask B. The proposed approach is a supervised combination of different unsupervised textual similarities. These textual similarities rely on the introduction of a relation matrix in the classical cosine similarity between bag-of-words, so as to get a softcosine that takes into account ...

The proofs below are given in textual form and contain the minimum amount of detail both with respect to the logic and reasoning involved, and with respect to the properties and definitions pertaining to sets, relations and mappings. Note that most applications of logical reasoning steps (i.e., the introduction and elimination rules discussed in Parts I and II of the book) are left implicit, bu...

Knowledge graph embedding represents entities and relations in knowledge graph as low-dimensional, continuous vectors, and thus enables knowledge graph compatible with machine learning models. Though there have been a variety of models for knowledge graph embedding, most methods merely concentrate on the fact triples, while supplementary textual descriptions of entities and relations have not b...