- (Lesk, 1986) ⇒ Michael E. Lesk. (1986). “Automatic Sense Disambiguation Uusing Machine Readable Dictionaries: How to tell a pine cone from a ice cream cone.” In: Proceedings of the Fifth International Conference on Systems Documentation, (SIGDOC 1986). doi:10.1145/318723.318728
- Seminal paper on Word Sense Disambiguation Algorithms.
- It computes a Similarity Function based on the overlap between a Word Sense Definition in a Target Word's Dictionary Entry (Glosses) and the Word Mention Context Windows.
- Base assumption: The most plausible Word Sense assignment is the one that maximizes Similarity among the senses.
- Input: Surrounding words; 2) machine readable Dictionary with an entry for each Word Sense and a Description.
- (Banerjee and Pedersen, 2002) ⇒ Satanjeev Banerjee, and Ted Pedersen. (2002). “An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet.” In: Proceedings of CICLing (2002). Lecture Notes In Computer Science; Vol. 2276.
- The original Lesk algorithm  disambiguates words in short phrases. The definition, or gloss, of each sense of a word in a phrase is compared to the glosses of every other word in the phrase. A word is assigned the sense whose gloss shares the largest number of words in common with the glosses of the other words. For example, in time flies like an arrow, the algorithm compares the glosses of time to all the glosses of fly and arrow. Next it compares the glosses of fly with those of time and arrow, and so on. The algorithm begins anew for each word and does not utilize the senses it previously assigned.
- The meaning of an English word can vary widely depending on which sense is intended. Does a firemena feed fires of put them out? It depends on whether he is a steam locomotive. I am trying to decide automatically which sense of a words is intended (in written English) by using machine readable dictionaries, and looking for words in the sense definitions that overlap words in the definition of nearby words.
- How wide a span of words should be counted? The program uses ten words as its default window; changing this to 4, 6 or 8 seems to make little difference. Should the span by syntactic (sense or phrase rather than count of words)? Should the effect of a words on a decision be weighted inversely by its distance? I haven't coded such choices yet.
- 1 J. F. Sowa, Conceptual Structures, Addison-Wesley, 1984.
- 2 R. Granger, "Scruffy Text Understanding," Proceedings of 20th ACL Meeting, pp. 157-160, 1982.
- 3 R. Amsler and D. Walker, "The Use of Machine- Readable Dictionaries in Sublanguage Analysis," in Subtanguage.' Description and Processing, ed. Ralph Grishman and R. Kittredge, Lawrence Ertbaum, (1985).,
|1986 AutoSenseDisambig||Michael E. Lesk||Automatic Sense Disambiguation Uusing Machine Readable Dictionaries: How to tell a pine cone from a ice cream cone||Proceedings of the Fifth International Conference on Systems Documentation||http://zeno.ling.gu.se:8080/bologna/kurshemsidor/komputationell-syntax-och-semantik/artiklar/Lesk-1986a.pdf||10.1145/318723.318728||1986|
Facts about "1986 AutoSenseDisambig"
|Author||Michael E. Lesk +|
|journal||Proceedings of the Fifth International Conference on Systems Documentation +|
|title||Automatic Sense Disambiguation Uusing Machine Readable Dictionaries: How to tell a pine cone from a ice cream cone +|