scholarly journals UoW: NLP techniques developed at the University of Wolverhampton for Semantic Similarity and Textual Entailment

2014 ◽  
Author(s):  
Rohit Gupta ◽  
Hanna Bechara ◽  
Ismail El Maarouf ◽  
Constantin Orasan
Keyword(s):  
Semantic Similarity ◽  
Textual Entailment ◽  
The University

A Lexico-Syntactic-Semantic Approach to Recognizing Textual Entailment

2020 ◽  
pp. 187-227 ◽  
Author(s):  
Rohini Basak ◽  
Sudip Kumar Naskar ◽  
Alexander Gelbukh
Keyword(s):  
Detailed Analysis ◽  
Semantic Similarity ◽  
Dependency Graph ◽  
Experimental Results ◽  
Classification Task ◽  
Semantic Approach ◽  
Textual Entailment ◽  
Lexical Similarity ◽  
Matching Rules ◽  
Recognizing Textual Entailment

Given two textual fragments, called a text and a hypothesis, respectively, recognizing textual entailment (RTE) is a task of automatically deciding whether the meaning of the second fragment (hypothesis) logically follows from the meaning of the first fragment (text). The chapter presents a method for RTE based on lexical similarity, dependency relations, and semantic similarity. In this method, called LSS-RTE, each of the two fragments is converted to a dependency graph, and the two obtained graph structures are compared using dependency triple matching rules, which have been compiled after a thorough and detailed analysis of various RTE development datasets. Experimental results show 60.5%, 64.4%, 62.8%, and 61.5% accuracy on the well-known RTE1, RTE2, RTE3, and RTE4 datasets, respectively, for the two-way classification task and 54.3% accuracy for three-way classification task on the RTE4 dataset.

scholarly journals Similarity Evaluation Based on Contextual Modelling

2019 ◽  
Vol 4 (1) ◽  
pp. 47-51
Author(s):  
Mohamed H. Haggag ◽  
◽  
Marwa M. A. ELFattah ◽  
Ahmed Mohammed Ahmed ◽  
◽  
...  
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Semantic Similarity ◽  
Language Processing ◽  
Contextual Information ◽  
Research Area ◽  
Web Page ◽  
Related Research ◽  
Textual Entailment ◽  
Similarity Problem

Measuring Text similarity problem still one of opened fields for research area in natural language processing and text related research such as text mining, Web page retrieval, information retrieval and textual entailment. Several measures have been developed for measuring similarity between two texts: such as Wu and Palmer, Leacock and Chodorow measure and others . But these measures do not take into consideration the contextual information of the text .This paper introduces new model for measuring semantic similarity between two text segments. This model is based on building new contextual structure for extracting semantic similarity. This approach can contribute in solving many NLP problems such as te xt entailment and information retrieval fields.

Proceedings of The Modern Language Association of America

PMLA ◽  
1935 ◽  
Vol 50 (4) ◽  
pp. 1343-1343
Keyword(s):  
Modern Language Association ◽  
Modern Language ◽  
Local Committee ◽  
Tuesday Morning ◽  
University Of Cincinnati ◽  
The University ◽  
Plaza Hotel

The fifty-second meeting of the Modern Language Associationof America was held, on the invitation of the University of Cincinnati, at Cincinnati, Ohio, Monday, Tuesday, and Wednesday, December 30 and 31, 1935, and January 1, 1936. The Association headquarters were in the Netherland Plaza Hotel, where all meetings were held except those of Tuesday morning and afternoon. These took place at the University of Cincinnati. Registration cards at headquarters were signed by about 900, though a considerably larger number of members were in attendance. The Local Committee estimated the attendance at not less than 1400. This Committee consisted of Professor Frank W. Chandler, Chairman; Professor Edwin H. Zeydel; Professor Phillip Ogden; Mr. John J. Rowe (for the Directors); and Mr. Joseph S. Graydon (for the Alumni).

Spectrophotometric reductions with an IBM 1620

1966 ◽  
Vol 24 ◽  
pp. 116-117
Author(s):  
P.-I. Eriksson
Keyword(s):  
Astronomical Data ◽  
Spectrophotometric Data ◽  
Electronic Computers ◽  
The University ◽  
Near Future

Nowadays more and more of the reductions of astronomical data are made with electronic computers. As we in Uppsala have an IBM 1620 at the University, we have taken it to our help with reductions of spectrophotometric data. Here I will briefly explain how we use it now and how we want to use it in the near future.

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