ambiguity detection
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Author(s):  
Hongyu Liang ◽  
Lei Zhang ◽  
Xiaoli Ding ◽  
Zhong Lu ◽  
Xin Li ◽  
...  
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10.29007/nxnc ◽  
2019 ◽  
Author(s):  
Shanel Reyes-Palacios ◽  
Edwin Aldana-Bobadilla ◽  
Ivan Lopez-Arevalo ◽  
Alejandro Molina-Villegas

Modern Text Mining techniques seek for extract information in useful formats such as georeferences in digital documents. Automatic recognition of location names in texts is usually solved through Named Entity Recognition (NER) systems. Most current NER are based on Machine Learning and have very high accuracy in detection of location entities in digital documents, especially if the texts are in English due to the lack of available an- notated corpora in other languages. However, recent studies are dealing with the challenge of taking the output labels of a NER system and then gather, from a gazetteer, their exact unambiguous geographical coordinates. This is challenging mainly because toponyms use to be very ambiguous, so research in disambiguation methods is relevant. In this paper we describe some of the main ideas towards a method to associate locations with geographical data removing possible confusion between entities with the same name. So far, we have already accomplished Geographic NER and coordinates retrieval but the main research is still in course. We largely discuss about the state of the art around Geoparsing; we explain how our Geographic Entity Recognition module works and finally we describe the research proposal focusing in ambiguity detection.


2019 ◽  
Vol 168 (1) ◽  
pp. 79-88
Author(s):  
Roman Urban ◽  
Hubert Anisimowicz

Author(s):  
Ariane Bazan ◽  
Ramesh Kushwaha ◽  
E. Samuel Winer ◽  
J. Michael Snodgrass ◽  
Linda A. W. Brakel ◽  
...  
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2018 ◽  
Vol 7 (2.29) ◽  
pp. 501
Author(s):  
Khin Hayman Oo ◽  
Azlin Nordin ◽  
Amelia Ritahani Ismail ◽  
Suriani Sulaiman

Ambiguity is the major problem in Software Requirements Specification (SRS) documents because most of the SRS documents are written in natural language and natural language is generally ambiguous. There are various types of techniques that have been used to detect ambiguity in SRS documents. Based on an analysis of the existing work, the ambiguity detection techniques can be categorized into three approaches: (1) manual approach, (2) semi-automatic approach using natural language processing, (3) semi-automatic approach using machine learning. Among them, one of the semi-automatic approaches that uses the Naïve Bayes (NB) text classification technique obtained high accuracy and performed effectively in detecting ambiguities in SRS.  


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