| Challenge: | Konkani is a low-resource language spoken by 2.5 million speakers . idiomatic sense processing is challenging due to the nature of idioms . |
| Approach: | They propose to use crowdsourced idiomatic sentence identification to build a corpus for idioms in the Konkani language. |
| Outcome: | The proposed corpus consists of 6520 sentences written in the Konkani language. |
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Designing a Russian Idiom-Annotated Corpus (L18-1)
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| Challenge: | a pilot experiment using the idiom-annotated corpus of Russian is described . corpora that could be used for training idiomatic classifiers are scarce, especially if one turns to other languages. |
| Approach: | They describe the development of an idiom-annotated corpus of Russian . the corpus is compiled from freely available online resources . |
| Outcome: | The proposed corpus is based on an online corpus of Russian texts . it is available for research purposes and can be used for linguistic studies and pedagogy . |
ID10M: Idiom Identification in 10 Languages (2022.findings-naacl)
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| Challenge: | Identifying and understanding idioms in context is a key goal and challenge in Natural Language Understanding tasks. |
| Approach: | They propose a multilingual Transformer-based system for the identification of idioms and a manually-curated evaluation benchmark. |
| Outcome: | The proposed system performs well in 10 languages and is released on github. |
No more beating about the bush : A Step towards Idiom Handling for Indian Language NLP (L18-1)
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| Challenge: | idioms are a part of natural language and are difficult to learn with a parallel corpora database. |
| Approach: | They propose to use a parallel idiom dataset to train two NLP subtasks . they show significant improvement in the two subtask training without the idiomatic dataset . |
| Outcome: | The proposed model improves on baseline models with the idiom dataset for two NLP applications. |
Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms (2022.lrec-1)
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Tosin Adewumi, Roshanak Vadoodi, Aparajita Tripathy, Konstantina Nikolaido, Foteini Liwicki, Marcus Liwicki
| Challenge: | Potential Idiomatic Expression (PIE) dataset for NLP in English contains over 20,100 samples with almost 1,200 cases of idioms from 10 classes (or senses). |
| Approach: | They present a large Potential Idiomatic Expression (PIE) dataset for Natural Language Processing (NLP) in English. |
| Outcome: | The proposed dataset contains over 20,100 samples with almost 1,200 cases of idioms (with their meanings) from 10 classes (or senses). |
Croatian Idioms Integration: Enhancing the LIdioms Multilingual Linked Idioms Dataset (2024.lrec-main)
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| Challenge: | Existing datasets that include idioms from English, German, Italian, Portuguese and Russian do not include a comprehensive representation of idiomatic expressions in Croatian. |
| Approach: | They propose to extend existing RDF-based multilingual representation of idioms to include 1,042 Croatian idiomes in an Ontolex Lemon format. |
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The DReaM Corpus: A Multilingual Annotated Corpus of Grammars for the World’s Languages (2020.lrec-1)
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| Challenge: | Until recently, language descriptions were available in paper form only, with indexes as the only search aid. |
| Approach: | They propose to digitize a multilingual corpus of language descriptions and annotate it with various meta, word, and text attributes to make searching and analysis easier and more useful. |
| Outcome: | The proposed corpus is searchable through a couple of well-established corpus infrastructures. |
MAGPIE: A Large Corpus of Potentially Idiomatic Expressions (2020.lrec-1)
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| Challenge: | Existing corpora cover less than 5,000 instances of less than 100 different idiom types . large corpus allows for better evaluation of assumptions about idiomatic expressions . |
| Approach: | They propose to build the largest-to-date corpus of idioms for English using crowdsourcing methods. |
| Outcome: | The proposed corpus is larger than existing resources and contains rich metadata and is made publicly available. |
LIdioms: A Multilingual Linked Idioms Data Set (L18-1)
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| Challenge: | Recent studies have focused on linguistic data sets that are bilingual on the Linguistic Linked Open Data (LLOD) 1 . |
| Approach: | They describe a multilingual RDF representation of idioms currently containing five languages . they use a model to structure the data and a method to link the data to well-known multilingual data sets such as BabelNet. |
| Outcome: | The proposed model complies with best practices according to Linguistic Linked Open Data Community. |
Assessing the Quality of an Italian Crowdsourced Idiom Corpus:the Dodiom Experiment (2022.lrec-1)
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| Challenge: | a crowdsourcing experiment has been used to collect idiom-related language resources . the data were collected through a game-with-a-purpose . |
| Approach: | They propose to use a game-with-a-purpose to collect idiom-related language resources . they use criteria adopted for the data annotation and evaluation process . |
| Outcome: | The proposed project evaluated idiom-related language resources from a game-with-a-purpose . the results and future work are presented. |
Beyond Multiword Expressions: Processing Idioms and Metaphors (P18-5)
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| Challenge: | idioms and metaphors processing is a rapidly growing area in NLP, says dr. s. robertson . idiomatic idiomas are characteristic to all areas of human activity and to all types of discourse. |
| Approach: | This tutorial will provide attendees with a clear notion of idioms and metaphors . it will provide them with computational models of linguistic characteristics and methods . |
| Outcome: | This tutorial aims to provide attendees with a clear notion of the linguistic characteristics of idioms and metaphors . it outlines how to model idiomatic idiomes and their processing and what resources are available to support their use . |