Lexical Disambiguation using Simulated Annealing

  title={Lexical Disambiguation using Simulated Annealing},
  author={James R. Cowie and Joe A. Guthrie and Louise Guthrie},
The resolution of lexical ambiguity is important for most natural language processing tasks, and a range of computational techniques have been proposed for its solution. None of these has yet proven effective on a large scale. In this paper, we describe a method for lexical disambiguation of text using the definitions in a machine-readable dictionary together with the technique of simulated annealing. The method operates on complete sentences and attempts to select the optimal combinations of… 

Figures from this paper

Linguistic knowledge and word sense disambiguation
Combining Weak Knowledge Sources for Sense Disambiguation
An implemented sense tagger which uses a machine readable dictionary to provide both a set of senses and associated forms of information on which to base disambiguation decisions, and is based on an architecture which makes use of different sources of lexical knowledge in two ways and optimises their combination using a learning algorithm.
The grammar of sense: Using part-of-speech tags as a first step in semantic disambiguation
This paper describes two experiments: one exploring the amount of information relevant to sense disambiguation contained in the part-of-speech field of entries in a Machine Readable Dictionary (MRD);
Opinion Extraction From Online Blogs And Public Reviews
This work focuses on the development of lexicon based improved term weighting method for polarity classification at sentence level by adapting a domain dependent polarity lexicon from set of labeled user reviews and domain independent lexicon, and proposes a unified learning framework based on information theory concepts that can assign the terms with correct polarity scores.
The Grammar of Sense: Is word-sense tagging much more than part-of-speech tagging?
This squib claims that Large-scale Automatic Sense Tagging of text (LAST) can be done at a high-level of accuracy and with far less complexity and computational effort than has been believed until
Word Sense Disambiguation by Marker Passing on Very Large Semantic Networks
This paper presents a method for word sense disambiguation with very large semantic networks (VLSN) that computes the score which depends on the number of shared words in denition, and shows the scoring measure that takes into account the degree of importance of words inDenition.
Semantic relatedness maximisation for word sense disambiguation using a hybrid firefly algorithm
The proposed glosses-overlapping method can be considered as an efficient solver for the WSD task by hybridising the FA with a one-point search algorithm to maximise the semantic relatedness of an eligible set of senses.
Word Sense Disambiguation Using Cosine Similarity Collaborates with Word 2 vec and WordNet
This method used Word2vec to construct a context sentence vector, and sense definition vectors then give each word sense a score using cosine similarity to compute the similarity between those sentence vectors, and shows that this method outperforms many unsupervised systems participating in the SENSEVAL-3 English lexical sample task.
An Optimized Lesk-Based Algorithm for Word Sense Disambiguation
This paper develops a simple and optimized variant of the algorithm using topic composition in documents based on the theory underlying topic models that achieves a superior performance on the general domain dataset and superior performance for knowledge-based techniques on the domain-specific dataset.


Subject-Dependent Co-Occurence and Word Sense Disambiguation
Using the subject classifications given in the machine-redable version of Longman's Dictionary of Contemporary English, subject-dependent co-occurrence links between words of the defining vocabulary are established to construct "neighborhoods" and the application of these neighborhoods to information retrieval is described.
Tagging for Learning: Collecting Thematic Relations from Corpus
A method for collecting co-occurrence data, acquiring lexical relations from the data, and applying these relations to semantic analysis is discussed.
A Stochastic Approach to Parsing
Grammatical parsing -resolving unanalysed linear sequences of words into meaningful grammatical structures -can be regarded as a perception problem logically analogous to those just cited, and simulated annealing holds great promise as a parsing technique.
Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone
This procedure uses available dictionaries, so that it will process any text; and uses solely the immediate context to decide which sense of a word is intended (in written English).
Longman Dictionary of Contemporary English
The Longman dictionary of contemporary English is a collection ofverbs, idioms andverbs used in English since the mid-19th century that reflect the changing nature of the language.
An intelligent analyzer and understander of English
The paper describes a working analysis and generation program for natural language, which handles paragraph length input. Its core is a system of preferential choice between deep semantic patterns,