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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.18151 |
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| _version_ | 1866911048143994880 |
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| author | Ide, Yusuke Tanner, Joshua Nohejl, Adam Hoffman, Jacob Vasselli, Justin Kamigaito, Hidetaka Watanabe, Taro |
| author_facet | Ide, Yusuke Tanner, Joshua Nohejl, Adam Hoffman, Jacob Vasselli, Justin Kamigaito, Hidetaka Watanabe, Taro |
| contents | Multiword expressions (MWEs) refer to idiomatic sequences of multiple words. MWE identification, i.e., detecting MWEs in text, can play a key role in downstream tasks such as machine translation, but existing datasets for the task are inconsistently annotated, limited to a single type of MWE, or limited in size. To enable reliable and comprehensive evaluation, we created CoAM: Corpus of All-Type Multiword Expressions, a dataset of 1.3K sentences constructed through a multi-step process to enhance data quality consisting of human annotation, human review, and automated consistency checking. Additionally, for the first time in a dataset of MWE identification, CoAM's MWEs are tagged with MWE types, such as Noun and Verb, enabling fine-grained error analysis. Annotations for CoAM were collected using a new interface created with our interface generator, which allows easy and flexible annotation of MWEs in any form. Through experiments using CoAM, we find that a fine-tuned large language model outperforms MWEasWSD, which achieved the state-of-the-art performance on the DiMSUM dataset. Furthermore, analysis using our MWE type tagged data reveals that Verb MWEs are easier than Noun MWEs to identify across approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18151 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CoAM: Corpus of All-Type Multiword Expressions Ide, Yusuke Tanner, Joshua Nohejl, Adam Hoffman, Jacob Vasselli, Justin Kamigaito, Hidetaka Watanabe, Taro Computation and Language Multiword expressions (MWEs) refer to idiomatic sequences of multiple words. MWE identification, i.e., detecting MWEs in text, can play a key role in downstream tasks such as machine translation, but existing datasets for the task are inconsistently annotated, limited to a single type of MWE, or limited in size. To enable reliable and comprehensive evaluation, we created CoAM: Corpus of All-Type Multiword Expressions, a dataset of 1.3K sentences constructed through a multi-step process to enhance data quality consisting of human annotation, human review, and automated consistency checking. Additionally, for the first time in a dataset of MWE identification, CoAM's MWEs are tagged with MWE types, such as Noun and Verb, enabling fine-grained error analysis. Annotations for CoAM were collected using a new interface created with our interface generator, which allows easy and flexible annotation of MWEs in any form. Through experiments using CoAM, we find that a fine-tuned large language model outperforms MWEasWSD, which achieved the state-of-the-art performance on the DiMSUM dataset. Furthermore, analysis using our MWE type tagged data reveals that Verb MWEs are easier than Noun MWEs to identify across approaches. |
| title | CoAM: Corpus of All-Type Multiword Expressions |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.18151 |