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Methods, Models and Tools for Improving the Quality of Textual Annotations
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In: Modelling; Volume 3; Issue 2; Pages: 224-242 (2022)
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Models of diachronic semantic change using word embeddings ; Modèles diachroniques à base de plongements de mot pour l'analyse du changement sémantique
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In: https://tel.archives-ouvertes.fr/tel-03199801 ; Document and Text Processing. Université Paris-Saclay, 2021. English. ⟨NNT : 2021UPASG006⟩ (2021)
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Teachers of Color's Perception on Identity and Academic Success: A Reflective Narrative
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In: All Antioch University Dissertations & Theses (2021)
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A Survey on Multilingual Hate Speech Detection and Classification by Machine Learning Techniques ...
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A Survey on Multilingual Hate Speech Detection and Classification by Machine Learning Techniques ...
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APiCS-Ligt: Towards Semantic Enrichment of Interlinear Glossed Text ...
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Ionov, Maxim. - : Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021
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Towards Learning Terminological Concept Systems from Multilingual Natural Language Text ...
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Improving Multilingual Models for the Swedish Language : Exploring CrossLingual Transferability and Stereotypical Biases
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NASca and NASes: Two Monolingual Pre-Trained Models for Abstractive Summarization in Catalan and Spanish
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In: Applied Sciences ; Volume 11 ; Issue 21 (2021)
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Inductive Bias and Modular Design for Sample-Efficient Neural Language Learning ...
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Analyzing Non-Textual Content Elements to Detect Academic Plagiarism
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Learning to scale multilingual representations for vision-language tasks
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Inductive Bias and Modular Design for Sample-Efficient Neural Language Learning
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SberQuAD – Russian Reading Comprehension Dataset: Description and Analysis
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In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2020)
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Assessing English Writing in Multilingual Writers in Higher Education: A Longitudinal Study
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In: Applied Linguistics and English as a Second Language Dissertations (2019)
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| 19 |
Character language models for generalization of multilingual named entity recognition
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Abstract:
State-of-the-art Named Entity Recognition (NER) models usually achieve high performance on entities that they have seen in training data, but a significantly lower performance on unseen entities. This is one of the key reasons in performance degradation observed when NER models are evaluated on new domains. Motivated by this observation, quantified for the first time in this thesis, we study an improved, multi-domain and multi-lingual, capability for identifying \what is a name". Character-level patterns have been widely used as features in English Named Entity Recognition (NER) systems. However, to date there has been no direct investigation of the inherent differences between name and non-name tokens in text, nor whether this property holds across multiple languages. The key contribution of this thesis is to develop a Character-level Language Model (CLM) that, as we show, allow us to better learn \what is a name". We analyze the capabilities of corpus-agnostic Character-level Language Models (CLMs) in the binary task of distinguishing name tokens from non-name tokens and demonstrate that CLMs provide a simple yet powerful model for capturing these differences. Specifically, we show that it can identify named entity tokens in a diverse set of languages at close to the performance of full NER systems. Moreover, by adding very simple CLM-based features we can significantly improve the performance of an o -the-shelf NER system for multiple languages.
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Keyword:
Character Language Models; Generalization; Multilingual; Multilingual Named Entity Recognition; Named Entity Recognition; NER
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URL: http://hdl.handle.net/2142/104934
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Multilingual Information Access (MLIA) Tools on Google and WorldCat: Bi/Multilingual University Students’ Experience and Perceptions
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In: FIMS Publications (2019)
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