Text augmentation for semantic frame induction and parsing.
Semantic frames are formal structures describing situations, actions or events, e.g., Commerce buy, Kidnapping, or Exchange. Each frame provides a set of frame elements or semantic roles corresponding to participants of the situation and lexical units (LUs)—words and phrases that can evoke this part...
| Published in: | Language Resources & Evaluation Vol. 58; no. 2; pp. 363 - 409 |
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| Main Authors: | , , , , |
| Format: | Article |
| Published: |
Springer Nature
Jun2024
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=178064682&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 178064682 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Jun2024 vid: 58 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 178064682 10.1007/s10579-023-09679-8 ppf: 363 ppct: 46 formats: fmt: – @attributes: type: T – @attributes: type: P size: 6.9MB tig: atl: Text augmentation for semantic frame induction and parsing. aug: au: Anwar, Saba Shelmanov, Artem Arefyev, Nikolay Panchenko, Alexander Biemann, Chris affil: https://ror.org/00g30e956 Universität Hamburg, Hamburg, Germany https://ror.org/0258gkt32 Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates https://ror.org/01xtthb56 University of Oslo, Oslo, Norway https://ror.org/03f9nc143 Skolkovo Institute of Science and Technology, Moscow, Russia https://ror.org/014a87f14 Artificial Intelligence Research Institute, Moscow, Russia su: Frames (Linguistics) Task performance Frames (Social sciences) sug: subj: Frames (Linguistics) Task performance Frames (Social sciences) keyword: BERT FrameNet Lexical substitution Semantic-frame induction Semantic-frame parser XLNet ab: Semantic frames are formal structures describing situations, actions or events, e.g., Commerce buy, Kidnapping, or Exchange. Each frame provides a set of frame elements or semantic roles corresponding to participants of the situation and lexical units (LUs)—words and phrases that can evoke this particular frame in texts. For example, for the frame Kidnapping, two key roles are Perpetrator and the Victim, and this frame can be evoked with lexical units abduct, kidnap, or snatcher. While formally sound, the scarce availability of semantic frame resources and their limited lexical coverage hinders the wider adoption of frame semantics across languages and domains. To tackle this problem, firstly, we propose a method that takes as input a few frame-annotated sentences and generates alternative lexical realizations of lexical units and semantic roles matching the original frame definition. Secondly, we show that the obtained synthetically generated semantic frame annotated examples help to improve the quality of frame-semantic parsing. To evaluate our proposed approach, we decompose our work into two parts. In the first part of text augmentation for LUs and roles, we experiment with various types of models such as distributional thesauri, non-contextualized word embeddings (word2vec, fastText, GloVe), and Transformer-based contextualized models, such as BERT or XLNet. We perform the intrinsic evaluation of these induced lexical substitutes using FrameNet gold annotations. Models based on Transformers show overall superior performance, however, they do not always outperform simpler models (based on static embeddings) unless information about the target word is suitably injected. However, we observe that non-contextualized models also show comparable performance on the task of LU expansion. We also show that combining substitutes of individual models can significantly improve the quality of final substitutes. Because intrinsic evaluation scores are highly dependent on the gold dataset and the frame preservation, and cannot be ensured by an automatic evaluation mechanism because of the incompleteness of gold datasets, we also carried out experiments with manual evaluation on sample datasets to further analyze the usefulness of our approach. The results show that the manual evaluation framework significantly outperforms automatic evaluation for lexical substitution. For extrinsic evaluation, the second part of this work assesses the utility of these lexical substitutes for the improvement of frame-semantic parsing. We took a small set of frame-annotated sentences and augmented them by replacing corresponding target words with their closest substitutes, obtained from best-performing models. Our extensive experiments on the original and augmented set of annotations with two semantic parsers show that our method is effective for improving the downstream parsing task by training set augmentation, as well as for quickly building FrameNet-like resources for new languages or subject domains. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2024. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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