2024
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Allen Institute for AI @ SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages
Lester James Miranda
Proceedings of the 6th Workshop on Research in Computational Linguistic Typology and Multilingual NLP
In this paper, we describe Allen AI’s submission to the constrained track of the SIGTYP 2024 Shared Task. Using only the data provided by the organizers, we pretrained a transformer-based multilingual model, then finetuned it on the Universal Dependencies (UD) annotations of a given language for a downstream task. Our systems achieved decent performance on the test set, beating the baseline in most language-task pairs, yet struggles with subtoken tags in multiword expressions as seen in Coptic and Ancient Hebrew. On the validation set, we obtained ≥70% F1- score on most language-task pairs. In addition, we also explored the cross-lingual capability of our trained models. This paper highlights our pretraining and finetuning process, and our findings from our internal evaluations.
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Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark
Stephen Mayhew
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Terra Blevins
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Shuheng Liu
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Marek Suppa
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Hila Gonen
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Joseph Marvin Imperial
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Börje Karlsson
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Peiqin Lin
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Nikola Ljubešić
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Lester James Miranda
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Barbara Plank
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Arij Riabi
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Yuval Pinter
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
We introduce Universal NER (UNER), an open, community-driven project to develop gold-standard NER benchmarks in many languages. The overarching goal of UNER is to provide high-quality, cross-lingually consistent annotations to facilitate and standardize multilingual NER research. UNER v1 contains 19 datasets annotated with named entities in a cross-lingual consistent schema across 13 diverse languages. In this paper, we detail the dataset creation and composition of UNER; we also provide initial modeling baselines on both in-language and cross-lingual learning settings. We will release the data, code, and fitted models to the public.
2023
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Developing a Named Entity Recognition Dataset for Tagalog
Lester James Miranda
Proceedings of the First Workshop in South East Asian Language Processing
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calamanCy: A Tagalog Natural Language Processing Toolkit
Lester James Miranda
Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)
We introduce calamanCy, an open-source toolkit for constructing natural language processing (NLP) pipelines for Tagalog. It is built on top of spaCy, enabling easy experimentation and integration with other frameworks. calamanCy addresses the development gap by providing a consistent API for building NLP applications and offering general-purpose multitask models with out-of-the-box support for dependency parsing, parts-of-speech (POS) tagging, and named entity recognition (NER). calamanCy aims to accelerate the progress of Tagalog NLP by consolidating disjointed resources in a unified framework.The calamanCy toolkit is available on GitHub: https://github.com/ljvmiranda921/calamanCy.