Reference Resolution Supporting Lexical Disambiguation

2010 ◽  
Author(s):  
Marjorie McShane ◽  
Stephen Beale ◽  
Sergei Nirenburg
Keyword(s):  
Reference Resolution ◽  
Lexical Disambiguation

scholarly journals Managing Free Text for Secondary Use of Health Data

2014 ◽  
Vol 23 (01) ◽  
pp. 167-169 ◽  
Author(s):  
N. Griffon ◽  
J. Charlet ◽  
S. J. Darmoni ◽  
Keyword(s):  
Language Processing ◽  
Meaningful Use ◽  
Health Data ◽  
Structured Data ◽  
The Other ◽  
Free Text ◽  
Reference Resolution ◽  
Comprehensive Review ◽  
Secondary Use ◽  
Corpus Creation

Summary Objective: To summarize the best papers in the field of Knowledge Representation and Management (KRM). Methods: A comprehensive review of medical informatics literature was performed to select some of the most interesting papers of KRM and natural language processing (NLP) published in 2013. Results: Four articles were selected, one focuses on Electronic Health Record (EHR) interoperability for clinical pathway personalization based on structured data. The other three focus on NLP (corpus creation, de-identification, and co-reference resolution) and highlight the increase in NLP tools performances. Conclusion: NLP tools are close to being seriously concurrent to humans in some annotation tasks. Their use could increase drastically the amount of data usable for meaningful use of EHR.

scholarly journals What influences the referential properties of reflexives and pronouns in Finnish

2006 ◽  
Vol 44 (1) ◽  
pp. 155-169
Author(s):  
Elsi Kaiser ◽  
Jeffrey T. Runner ◽  
Rachel S. Sussman ◽  
Michael K. Tanenhaus
Keyword(s):  
Factor Model ◽  
Binding Theory ◽  
Sources Of Information ◽  
Reference Resolution ◽  
Multiple Factors ◽  
Complementary Distribution ◽  
Morphological Differences ◽  
Multiple Factor ◽  
Perceiver Effects

According to standard Binding Theory, pronouns and reflexives are in (nearly) complementary distribution. However, representational NPs (e.g. 'picture of her/herself') allow both. It has been suggested that in English, reflexives in representational NPs (RNPs) have a preference for 'sources of information' and that pronouns prefer 'perceivers of information.' We conducted two experiments investigating the effects of structural and non-structural (source/perceiver) factors on the interpretation of two kinds of RNP structures in a typologically different language, namely Finnish. Our results reveal source/perceiver effects for postnominal but not for prenominal RNPs in Finnish, with a difference in the degree of sensitivity that pronouns and reflexives exhibit to the source/perceiver manipulation, and our results also suggest that morphological differences in Finnish reflexives correspond to interpretation differences. As a whole, these results support a multiple-factor model of reference resolution, which assumes that multiple factors can play a role in reference resolution and that the relative contributions of these factors can be different for different anaphoric forms (Kaiser 2003b, Kaiser & Trueswell in press).  

scholarly journals Corpus annotation and reference resolution

1997 ◽  
Author(s):  
A. McEnery ◽  
I. Tanaka ◽  
S. Botley
Keyword(s):  
Corpus Annotation ◽  
Reference Resolution

Part-of-Speech Tagging

2017 ◽  
Author(s):  
Dan Tufiș ◽  
Radu Ion
Keyword(s):  
Language Processing ◽  
Machine Learning Techniques ◽  
Computing Power ◽  
Data Sparseness ◽  
Part Of Speech Tagging ◽  
Web Environment ◽  
Part Of Speech ◽  
Learning Techniques ◽  
Lexical Disambiguation ◽  
Speech Tagging

One of the fundamental tasks in natural-language processing is the morpho-lexical disambiguation of words occurring in text. Over the last twenty years or so, approaches to part-of-speech tagging based on machine learning techniques have been developed or ported to provide high-accuracy morpho-lexical annotation for an increasing number of languages. Due to recent increases in computing power, together with improvements in tagging technology and the extension of language typologies, part-of-speech tags have become significantly more complex. The need to address multilinguality more directly in the web environment has created a demand for interoperable, harmonized morpho-lexical descriptions across languages. Given the large number of morpho-lexical descriptors for a morphologically complex language, one has to consider ways to avoid the data sparseness threat in standard statistical tagging, yet ensure that full lexicon information is available for each word form in the output. The chapter overviews the current major approaches to part-of-speech tagging.

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