The WordNet Weaver: Multi-criteria Voting for Semi-automatic Extension of a Wordnet

Piasecki, Maciej; Broda, Bartosz; Marcińczuk, Michał; Śzpakowicz, Stan

doi:10.1007/978-3-642-01818-3_32

Cited by 2 publications

(4 citation statements)

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“…Similarly, thanks to a direct mapping between the plWordNet (Piasecki et al, 2009 ) and the Princeton WordNet (Fellbaum, 1998 ; Miller, 1995 ), the present dataset could be useful in research involving multiple languages. The Princeton WordNet was conceived in 1986 at Princeton University and is the first and most widely known such resource in the world.…”

Section: Discussionmentioning

confidence: 99%

“…Having access to large linguistic resources is certainly powerful and has many desirable implications. First, the present dataset is directly linked to plWordNet (Piasecki et al, 2009 ), a large lexico-semantic network that interlinks words by means of lexical and conceptual relations. Furthermore, word meanings are accompanied by relevant linguistic data, derived from other open-source resources.…”

Section: Discussionmentioning

confidence: 99%

“…Drawing on corpus linguistics and natural language processing research, the present dataset comprises words directly linked to the precise indications of meaning, derived from the Polish wordnet (plWordNet, Słowosieć 1 ). plWordNet is a large and comprehensive relational dictionary, which reflects the lexical system of the Polish language and currently contains 285,000 word meanings, linked with each other by over 600,000 semantic relations (Dziob et al, 2019 ; Piasecki et al, 2009 ). By having its roots in plWordNet, the present dataset can also be easily mapped to other languages, as long as the mapping between respective wordnets is available.…”

Section: Introductionmentioning

confidence: 99%

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Emotion norms for 6000 Polish word meanings with a direct mapping to the Polish wordnet

et al. 2021

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Emotion lexicons are useful in research across various disciplines, but the availability of such resources remains limited for most languages. While existing emotion lexicons typically comprise words, it is a particular meaning of a word (rather than the word itself) that conveys emotion. To mitigate this issue, we present the Emotion Meanings dataset, a novel dataset of 6000 Polish word meanings. The word meanings are derived from the Polish wordnet (plWordNet), a large semantic network interlinking words by means of lexical and conceptual relations. The word meanings were manually rated for valence and arousal, along with a variety of basic emotion categories (anger, disgust, fear, sadness, anticipation, happiness, surprise, and trust). The annotations were found to be highly reliable, as demonstrated by the similarity between data collected in two independent samples: unsupervised (n = 21,317) and supervised (n = 561). Although we found the annotations to be relatively stable for female, male, younger, and older participants, we share both summary data and individual data to enable emotion research on different demographically specific subgroups. The word meanings are further accompanied by the relevant metadata, derived from open-source linguistic resources. Direct mapping to Princeton WordNet makes the dataset suitable for research on multiple languages. Altogether, this dataset provides a versatile resource that can be employed for emotion research in psychology, cognitive science, psycholinguistics, computational linguistics, and natural language processing.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Emotion norms for 6000 Polish word meanings with a direct mapping to the Polish wordnet

et al. 2021

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“…5 The other wordnet-building strategy is to avoid the influence of Princeton WordNet. Polish WordNet (Piasecki et al 2009) is one such resource built from scratch. Its development was supported, among others, by WordNet Weaver, a tool which helps increase the vocabulary of a wordnet.…”

Section: Wordnets In Other Languagesmentioning

confidence: 99%

Evaluation of automatic updates of Roget’s Thesaurus

Kennedy

Śzpakowicz

2014

JLM

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Thesauri and similarly organised resources attract increasing interest of Natural Language Processing researchers. Thesauri age fast, so there is a constant need to update their vocabulary. Since a manual update cycle takes considerable time, automated methods are required. This work presents a tuneable method of measuring semantic relatedness, trained on Roget's Thesaurus, which generates lists of terms related to words not yet in the Thesaurus. Using these lists of terms, we experiment with three methods of adding words to the Thesaurus. We add, with high confidence, over 5500 and 9600 new word senses to versions of Roget's Thesaurus from 1911 and 1987 respectively. We evaluate our work both manually and by applying the updated thesauri in three NLP tasks: selection of the best synonym from a set of candidates, pseudo-word-sense disambiguation and SAT-style analogy problems. We find that the newly added words are of high quality. The additions significantly improve the performance of Roget's-based methods in these NLP tasks. The performance of our system compares favourably with that of WordNet-based methods. Our methods are general enough to work with different versions of Roget's Thesaurus.

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The WordNet Weaver: Multi-criteria Voting for Semi-automatic Extension of a Wordnet

Cited by 2 publications

References 5 publications

Emotion norms for 6000 Polish word meanings with a direct mapping to the Polish wordnet

Emotion norms for 6000 Polish word meanings with a direct mapping to the Polish wordnet

Evaluation of automatic updates of Roget’s Thesaurus

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