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A Minimally Supervised Word Sense Disambiguation Algorithm Using Syntactic Dependencies and Semantic Generalizations
Natural language is inherently ambiguous. For example, the word "bank" can mean a financial institution or a river shore. Finding the correct meaning of a word in a particular context is a task known as word sense disambiguation (WSD), which is essential for many natural language processing applications such as machine translation, information retrieval, and others. While most current WSD methods try to disambiguate a small number of words for which enough annotated examples are available, the method proposed in this thesis attempts to address all words in unrestricted text. The method is based on constraints imposed by syntactic dependencies and concept generalizations drawn from an external dictionary. The method was tested on standard benchmarks as used during the SENSEVAL-2 and SENSEVAL-3 WSD international evaluation exercises, and was found to be competitive
Distinguishing Word Senses in Untagged Text
This paper describes an experimental comparison of three unsupervised
learning algorithms that distinguish the sense of an ambiguous word in untagged
text. The methods described in this paper, McQuitty's similarity analysis,
Ward's minimum-variance method, and the EM algorithm, assign each instance of
an ambiguous word to a known sense definition based solely on the values of
automatically identifiable features in text. These methods and feature sets are
found to be more successful in disambiguating nouns rather than adjectives or
verbs. Overall, the most accurate of these procedures is McQuitty's similarity
analysis in combination with a high dimensional feature set.Comment: 11 pages, latex, uses aclap.st
Morphological Disambiguation by Voting Constraints
We present a constraint-based morphological disambiguation system in which
individual constraints vote on matching morphological parses, and
disambiguation of all the tokens in a sentence is performed at the end by
selecting parses that receive the highest votes. This constraint application
paradigm makes the outcome of the disambiguation independent of the rule
sequence, and hence relieves the rule developer from worrying about potentially
conflicting rule sequencing. Our results for disambiguating Turkish indicate
that using about 500 constraint rules and some additional simple statistics, we
can attain a recall of 95-96% and a precision of 94-95% with about 1.01 parses
per token. Our system is implemented in Prolog and we are currently
investigating an efficient implementation based on finite state transducers.Comment: 8 pages, Latex source. To appear in Proceedings of ACL/EACL'97
Compressed postscript also available as
ftp://ftp.cs.bilkent.edu.tr/pub/ko/acl97.ps.
NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings
Current approaches for service composition (assemblies of atomic services)
require developers to use: (a) domain-specific semantics to formalize services
that restrict the vocabulary for their descriptions, and (b) translation
mechanisms for service retrieval to convert unstructured user requests to
strongly-typed semantic representations. In our work, we argue that effort to
developing service descriptions, request translations, and matching mechanisms
could be reduced using unrestricted natural language; allowing both: (1)
end-users to intuitively express their needs using natural language, and (2)
service developers to develop services without relying on syntactic/semantic
description languages. Although there are some natural language-based service
composition approaches, they restrict service retrieval to syntactic/semantic
matching. With recent developments in Machine learning and Natural Language
Processing, we motivate the use of Sentence Embeddings by leveraging richer
semantic representations of sentences for service description, matching and
retrieval. Experimental results show that service composition development
effort may be reduced by more than 44\% while keeping a high precision/recall
when matching high-level user requests with low-level service method
invocations.Comment: This paper will appear on SCC'19 (IEEE International Conference on
Services Computing) on July 1
Extending, trimming and fusing WordNet for technical documents
This paper describes a tool for the automatic
extension and trimming of a multilingual
WordNet database for cross-lingual retrieval
and multilingual ontology building in
intranets and domain-specific document
collections. Hierarchies, built from
automatically extracted terms and combined
with the WordNet relations, are trimmed
with a disambiguation method based on the
document salience of the words in the
glosses. The disambiguation is tested in a
cross-lingual retrieval task, showing
considerable improvement (7%-11%). The
condensed hierarchies can be used as
browse-interfaces to the documents
complementary to retrieval
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