Results 221 to 230 of about 571 (258)

Human-AI co-research on design and evaluation of Embodied Conversational Agent in rehabilitation contexts. [PDF]

Front Robot AI
Lekova A, Tsvetkova P, Stefanov T.
europepmc   +1 more source

Explanatory argumentation in natural language for correct and incorrect medical diagnoses. [PDF]

J Biomed Semantics
Molinet B, Marro S, Cabrio E, Villata S.
europepmc   +1 more source

Unsupervised literature mining approaches for extracting relationships pertaining to habitats and reproductive conditions of plant species. [PDF]

Front Artif Intell
Gabud R, Lapitan P, Mariano V, Mendoza E, Pampolina N, Clariño MAA, Batista-Navarro R.   +6 more
europepmc   +1 more source

Paraphrase Substitution for Recognizing Textual Entailment [PDF]

, 2007
We describe a method for recognizing textual entailment that uses the length of the longest common subsequence (LCS) between two texts as its decision criterion. Rather than requiring strict word matching in the common subsequences, we perform a flexible match using automatically generated paraphrases.
Bosma, W.E., Callison-Burch, C.
core   +6 more sources

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Recognizing Textual Entailment Based on WordNet

2008 Second International Symposium on Intelligent Information Technology Application, 2008
Textual entailment recognition (RTE) is one of the fundamental problems in many natural language processing applications. This paper proposes a new method for lexical entailment measure which is based on exploiting the information in the WordNet glosses.
Trevor Martin
exaly   +3 more sources

An Empirical Study of Recognizing Textual Entailment in Japanese Text

Lecture Notes in Computer Science, 2012
Recognizing Textual Entailment (RTE) is a fundamental task in Natural Language Understanding. The task is to decide whether the meaning of a text can be inferred from the meaning of the other one. In this paper, we conduct an empirical study of the RTE task for Japanese, adopting a machine-learning-based approach.
Le Minh Nguyen, Akira Shimazu, Shimazu Akira   +2 more
exaly   +2 more sources

Recognizing Textual Entailment in Vietnamese Text: An Experimental Study

2015 Seventh International Conference on Knowledge and Systems Engineering (KSE), 2015
This paper proposes a model which utilizes Support Vector Machines (SVMs) - a machine learning approach for recognizing textual entailment in Vietnamese text, including three steps: (1) feature extraction, (2) training and (3) judgement by voting. In the first step, many features (e.g., Euclidean distance, Cosine, if-idf, etc) were extracted to train ...
Minh-Tien Nguyen, Quang-Thuy Ha, Tri-Thanh Nguyen   +2 more
exaly   +2 more sources

Recognizing Textual Entailment Using Weighted Dependency Relations

Lecture Notes in Computer Science, 2023
Sudip Kumar Naskar, Asif Ekbal, Naskar Sudip Kumar   +2 more
exaly   +2 more sources

Recognizing Textual Entailment

2013
In the last few years, a number of NLP researchers have developed and participated in the task of Recognizing Textual Entailment (RTE). This task encapsulates Natural Language Understanding capabilities within a very simple interface: recognizing when the meaning of a text snippet is contained in the meaning of a second piece of text.
Dagan, I, Roth, D, Sommons, M, ZANZOTTO, FABIO MASSIMO   +3 more
openaire   +2 more sources