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Hits 1 – 4 of 4
1
Learning semantic types and relations from text ...
Hovy, Dirk
. - : University of Southern California Digital Library (USC.DL), 2015
Abstract:
NLP applications such as Question Answering (QA), Information Extraction (IE), or Machine Translation (MT) are incorporating increasing amounts of semantic information. A fundamental building block of semantic information is the relation between a predicate and its arguments, e.g. eat(John,burger). In order to reason at higher levels of abstraction, it is useful to group relation instances according to the types of their predicates and the types of their arguments. For example, while eat(Mary,burger) and devour(John,tofu) are two distinct relation instances, they share the underlying predicate and argument types INGEST(PERSON,FOOD). A central question is: where do the types and relations come from? ❧ The subfield of NLP concerned with this is relation extraction, which comprises two main tasks: ❧ 1. identifying and extracting relation instances from text ❧ 2. determining the types of their predicates and arguments ❧ The first task is difficult for several reasons. Relations can express their predicate ...
Keyword:
computational linguistics
;
Computer Science
;
information extraction
;
NLP
;
relation extraction
;
unsupervised learning
URL:
https://dx.doi.org/10.25549/usctheses-c3-300459
https://digitallibrary.usc.edu/asset-management/2A3BF1LGLIUT
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2
Tree-adjoining machine translation ...
DeNeefe, Steve
. - : University of Southern California Digital Library (USC.DL), 2015
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3
Weighted tree automata and transducers for syntactic natural language processing ...
May, Jonathan David Louis
. - : University of Southern California Digital Library (USC.DL), 2015
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4
Semantically-enriched parsing for natural language understanding ...
Tratz, Stephen Charles
. - : University of Southern California Digital Library (USC.DL), 2015
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