Lexical chains as representations of context for the detection and correction of malapropisms Graeme Hirst David St-Onge Department of Computer Science University of Toronto
Structure
Introduction & Lexical chains WordNet as a knowledge source for a lexical chainer Automatically detecting malapropisms Conclusion
Introduction & Lexical chains
Lexical cohesion Halliday and Hasan (1976): some concepts were previous mentioned to other concepts that related to them Cohesive chains
Introduction & Lexical chains
An example: The major potential complication of total joint replacement is inflection. It may occur just in the area of the wound or deep around the prosthesis. It may occur during the hospital stay or after the patient goes home…… Infections in the wound areas are generally treated with antibiotics.
Introduction & Lexical chains
Morris and Hirst: lexical chains Lexical chain: A cohesive chain in which the criterion for inclusion of a word is that it bears some kind of cohesive relationship to a word that is already in the chain Thesaurus: Roget’s
Introduction & Lexical chains
Their index entries point to the same, or adjacent thesaurus categories The index entry of one contains the other The index entry of one points to a category that contains the other The index entry of one points to a category that in turn contains a pointer to a category pointed by the index entry of the other Both of their index entries contain a pointer to the same category
Introduction & Lexical chains
An example of regular relation between two words:
WordNet as a knowledge source for a lexical chainer
Synset The relation between words------the relation between the synsets of both words Weight=C-Path length-k*number of changes of direction Three kinds of relation: 1.extra-strong 2.strong 3.medium-strong
WordNet as a knowledge source for a lexical chainer
Extra-strong: only between a word and its literal repetition Strong: 3 kinds 1.A synset common to two words 2.A horizontal link between a synset of each word 3.Compound word or phase Medium-strong: when a member of a set of allowable paths connects a synset of each word
WordNet as a knowledge source for a lexical chainer
Two rules for defining allowable patterns R1: No other direction may precede an upward link. R2: At most one change of direction is allowed. R2’: It is permitted to use a horizontal link to make a transition from an upward to a downward direction.
WordNet as a knowledge source for a lexical chainer
WordNet as a knowledge source for a lexical chainer
How to build lexical chains?
WordNet as a knowledge source for a lexical chainer
WordNet as a knowledge source for a lexical chainer
WordNet as a knowledge source for a lexical chainer
Extra-strongStrong(7)Mediumstrong(3)
Automatically detecting malapropisms
Two kinds of word errors 1. Non-word error 2. Real-word error Two techniques for non-word error 1. Lexicon lookup 2. N-gram analysis
Automatically detecting malapropisms
syntactic errors (e.g. the students are doing there homework) semantic errors or malapropisms (e.g. he spent his summer traveling around the word) structural errors (e.g. I need three ingredients: red wine, sugar, cinnamon, and cloves) pragmatic errors (e.g. he studies at the University of Toronto in England and she studies at Cambridge)
Automatically detecting malapropisms
Hypothesis
The more distant a word is semantically from all the other words of a text, the higher the probability is that it is a malapropism.
Automatically detecting malapropisms
The algorithm looks for non-word error and solicits corrections from the user Construct lexical chains between high-content words in the text Find w’ besides w and raises alarm for replacement, based on the hypothesis above (PS: w’ is a word which is orthographically similar to w and does fit in one of the chains, while w doesn’t )
Automatically detecting malapropisms
The experiment text: naturally occurring texts? First draft by unprofessional Inserting deliberate malapropisms to published text
Automatically detecting malapropisms
Some examples of deliberate malapropisms: Much of that data, he notes, is available toady [today] electronically. Among the largest OTC issues, Farmers Group, which expects B.A.T. Industries to launch a hostile tenter [tender] offer for it, jumped 2 and half to 62 yesterday. But most of yesterday’s popular issues were small out-of-the-limelight technology companies that slipped in price a bit last year after the crush [crash], although their earnings are on the rise.
Automatically detecting malapropisms
“toady” is replaced by “today” tenter IS A framework / frame INCLUDES handbarrow HAS PART handle / grip / hold INCLUDES stock “crush” is found to be wrong, while “brush” was suggested instead of “crash”
Automatically detecting malapropisms
Analysis of the overall result
31.4% 7.01%
89.8% 36.6%
Conclusion
A large cost……25.3/1000 Encouraging result! A further-developed WordNet is desired in future to “protect” the right words Lexical chains as context: tasks do not require a complete analysis of meaning
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