Automatically constructing Wordnet Synsets
Lam, Khang Nhut and Al Tarouti, Feras and Kalita, Jugal

Article Structure

Abstract

Manually constructing a Wordnet is a difficult task, needing years of experts’ time.

Introduction

Wordnets are intricate and substantive repositories of lexical knowledge and have become important resources for computational processing of natural languages and for information retrieval.

Proposed approaches

In this section, we propose approaches to create Wordnet synsets for a target languages T using existing Wordnets and the MT and/or a single bilingual dictionary.

Experiments

3.1 Publicly available Wordnets

Conclusion and future work

We present approaches to create Wordnet synsets for languages using available Wordnets, a public MT and a single bilingual dictionary.

Topics

Wordnets

Appears in 82 sentences as: (5) Wordnet (44) Wordnets (51)
In Automatically constructing Wordnet Synsets
  1. Manually constructing a Wordnet is a difficult task, needing years of experts’ time.
    Page 1, “Abstract”
  2. As a first step to automatically construct full Wordnets, we propose approaches to generate Wordnet synsets for languages both resource-rich and resource-poor, using publicly available Wordnets , a machine translator and/or a single bilingual dictionary.
    Page 1, “Abstract”
  3. Our algorithms translate synsets of existing Wordnets to a target language T, then apply a ranking method on the translation candidates to find best translations in T. Our approaches are applicable to any language which has at least one existing bilingual dictionary translating from English to it.
    Page 1, “Abstract”
  4. Wordnets are intricate and substantive repositories of lexical knowledge and have become important resources for computational processing of natural languages and for information retrieval.
    Page 1, “Introduction”
  5. Good quality Wordnets are available only for a few "resource-rich" languages such as English and Japanese.
    Page 1, “Introduction”
  6. Published approaches to automatically build new Wordnets are manual or semiautomatic and can be used only for languages that already possess some lexical resources.
    Page 1, “Introduction”
  7. The Princeton Wordnet (PWN) (Fellbaum, 1998) was painstakingly constructed manually over many decades.
    Page 1, “Introduction”
  8. Wordnets , except the PWN, have been usually constructed by one of two approaches.
    Page 1, “Introduction”
  9. The first approach translates the PWN to T (Bilgin et al., 2004), (Barbu and Mititelu, 2005), (Kaji and Watanabe, 2006), (Sagot and Fiser, 2008), (Saveski and Trajkovsk, 2010) and (Oliver and Climent, 2012); while the second approach builds a Wordnet in T, and then aligns it with the PWN by generating translations (Gu-
    Page 1, “Introduction”
  10. Wordnets generated using the second approach have different structures from the PWN; however, the complex agglutinative morphology, culture specific meanings and usages of words and phrases of target languages can be maintained.
    Page 1, “Introduction”
  11. In contrast, Wordnets created using the first approach have the same structure as the PWN.
    Page 1, “Introduction”

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synsets

Appears in 58 sentences as: (3) Synset (13) synset (13) Synsets (4) synsets (46)
In Automatically constructing Wordnet Synsets
  1. As a first step to automatically construct full Wordnets, we propose approaches to generate Wordnet synsets for languages both resource-rich and resource-poor, using publicly available Wordnets, a machine translator and/or a single bilingual dictionary.
    Page 1, “Abstract”
  2. Our algorithms translate synsets of existing Wordnets to a target language T, then apply a ranking method on the translation candidates to find best translations in T. Our approaches are applicable to any language which has at least one existing bilingual dictionary translating from English to it.
    Page 1, “Abstract”
  3. One of our goals is to automatically generate high quality synsets , each of which is a set of cognitive synonyms, for Wordnets having the same structure as the PWN in several languages.
    Page 1, “Introduction”
  4. In particular, given public Wordnets aligned to the PWN ( such as the FinnWordNet (FWN) (Linden, 2010) and the J apaneseWordNet (J WN) (Isahara et al., 2008) ) and the Microsoft Translator, we build Wordnet synsets for arb, asm, dis, ajz and vie.
    Page 1, “Introduction”
  5. In this section, we propose approaches to create Wordnet synsets for a target languages T using existing Wordnets and the MT and/or a single bilingual dictionary.
    Page 2, “Proposed approaches”
  6. We take advantage of the fact that every synset in PWN has a unique oflset-POS, referring to the offset for a synset with a particular part-of—speech (POS) from the beginning of its data file.
    Page 2, “Proposed approaches”
  7. Each synset may have one or more words, each of which may be in one or more synsets .
    Page 2, “Proposed approaches”
  8. Words in a synset have the same sense.
    Page 2, “Proposed approaches”
  9. The basic idea is to extract corresponding synsets for each oflset-POS from existing Wordnets linked to PWN, in several languages.
    Page 2, “Proposed approaches”
  10. Next, we translate extracted synsets in each language to T to produce so-called synset candidates using MT.
    Page 2, “Proposed approaches”
  11. 2.1 Generating synset candidates
    Page 2, “Proposed approaches”

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