Unsupervised word sense disambiguation for Korean through the acyclic weighted digraph using corpus ...
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Unsupervised word sense disambiguation for Korean through the acyclic weighted digraph using corpus and dictionary. Presenter: Chun-Ping Wu Authors: Yeohoon Yoon, Choong-Nyoung Seon , Songwook Lee, Jungynu Seo. 國立雲林科技大學 National Yunlin University of Science and Technology. IPM 2007.

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Presenter chun ping wu

Unsupervised word sense disambiguation for Korean through the acyclic weighted digraph using corpus and dictionary

Presenter: Chun-Ping Wu

Authors: Yeohoon Yoon, Choong-NyoungSeon, Songwook Lee, JungynuSeo

國立雲林科技大學

National Yunlin University of Science and Technology

IPM 2007


Outline

Outline

  • Motivation

  • Objective

  • Methodology

  • Experiments

  • Conclusion

  • Comments


Motivation

Motivation

  • The Word Sense Disambiguation is a common problem in natural language processing.

  • Traditional approaches only consider the co-occurrence probability alone.

Sample:I deposit some money in the bank.

Options:

bank = 銀行?

bank = 堤; 岸?

bank = (一)排; (一)組


Objective

Objective

  • To construct a WSD system, which can be easily implemented by learning all polysemous words at once, while covering all polysemous words which are listed in MRD.

  • To consider relation between each sense of context words and the sense of the target word.

Sample:I deposit some money In the bank.

Ans:

bank = 銀行


Methodology

Methodology

  • Learning step

    • Similarity matrix

    • Word vector

    • Vector representations of sense definitions in MRD

  • Disambiguation step

    • The definition of acyclic weighted digraph.

    • Selecting context words

    • Constructing the acyclic weighted digraph

    • Searching the optimal path on the acyclic weighted digraph


Methodology1

Methodology

  • Learning step

    • Similarity matrix

    • Word vector

    • Vector representations of sense definitions

      in MRD


Methodology2

Methodology

  • Learning step

    • Similarity matrix

    • Word vector

    • Vector representations of sense definitions

      in MRD.


Methodology3

Methodology

  • Learning step

    • Similarity matrix

    • Word vector

    • Vector representations of sense definitions

      in MRD


Methodology4

Methodology

  • Disambiguation step

    • The definition of acyclic weighted digraph.

    • Selecting context words

    • Constructing the acyclic weighted digraph

    • Searching the optimal path on the acyclic

      weighted digraph


Methodology5

Methodology

  • Disambiguation step

    • The definition of acyclic weighted digraph.

    • Selecting context words

    • Constructing the acyclic weighted digraph

    • Searching the optimal path on the acyclic

      weighted digraph


Methodology6

Methodology

  • Disambiguation step

    • The definition of acyclic weighted digraph.

    • Selecting context words

    • Constructing the acyclic weighted digraph

    • Searching the optimal path on the acyclic

      weighted digraph


Methodology7

Methodology

  • Disambiguation step

    • The definition of acyclic weighted digraph.

    • Selecting context words

    • Constructing the acyclic weighted digraph

    • Searching the optimal path on the acyclic

      weighted digraph


Experiments

Experiments

  • System results


Experiments1

Experiments

  • Experiment on English

    • The accuracy of the system is 30.7% on average.

    • The result is very low; there are some reasons as follows.

      • Context words are not appropriate although context words are very important in that they decide which sense of the target word might be the best.

      • Mapping English senses to Korean for using English-Korean dictionary leads to some loss of information.

      • The errors of the stemming process disturbed us to search the right root of the verb in the MRD.


Conclusion

Conclusion

  • To consider the relationship between each sense of context words and the sense of the target word

  • By using Viterbi algorithm to reduce computational complexity.

  • The system showed bad results on English (30.7), but it resulted in suitable performances, 76.4% by accuracy, over the semantically ambiguous Korean words.

  • To apply this method to other languages by studying language characteristics.

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Comments

Comments

  • Advantage

    • To consider the relationship between each sense of context words and the sense of the target word.

    • By using Viterbi algorithm to reduce computational complexity.

  • Drawback

    • The performance of this system is better in Korean.

  • Application

    • Word Sense Disambiguation

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