Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling

• URL: http://arxiv.org/abs/2203.16169v1
• Date: Wed, 30 Mar 2022 09:46:51 GMT
• Title: Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling
• Authors: Elena \'Alvarez-Mellado, Constantine Lignos
• Abstract summary: We introduce a new annotated corpus of Spanish newswire rich in unassimilated lexical borrowings. We use it to evaluate how several sequence labeling models (CRF, BiLSTM-CRF, and Transformer-based models) perform.
• Score: 2.741266294612776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work presents a new resource for borrowing identification and analyzes the performance and errors of several models on this task. We introduce a new annotated corpus of Spanish newswire rich in unassimilated lexical borrowings -- words from one language that are introduced into another without orthographic adaptation -- and use it to evaluate how several sequence labeling models (CRF, BiLSTM-CRF, and Transformer-based models) perform. The corpus contains 370,000 tokens and is larger, more borrowing-dense, OOV-rich, and topic-varied than previous corpora available for this task. Our results show that a BiLSTM-CRF model fed with subword embeddings along with either Transformer-based embeddings pretrained on codeswitched data or a combination of contextualized word embeddings outperforms results obtained by a multilingual BERT-based model.

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