Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields

• URL: http://arxiv.org/abs/2404.09383v1
• Date: Sun, 14 Apr 2024 23:44:49 GMT
• Title: Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields
• Authors: Ryan Cotterell, Kevin Duh,
• Abstract summary: Low-resource named entity recognition is still an open problem in NLP. We present a transfer learning scheme, whereby we train character-level neural CRFs to predict named entities for both high-resource languages and low resource languages jointly.
• Score: 68.17213992395041
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-resource named entity recognition is still an open problem in NLP. Most state-of-the-art systems require tens of thousands of annotated sentences in order to obtain high performance. However, for most of the world's languages, it is unfeasible to obtain such annotation. In this paper, we present a transfer learning scheme, whereby we train character-level neural CRFs to predict named entities for both high-resource languages and low resource languages jointly. Learning character representations for multiple related languages allows transfer among the languages, improving F1 by up to 9.8 points over a loglinear CRF baseline.

Related papers

• The Zeno's Paradox of `Low-Resource' Languages [20.559416975723142]
  We show how several interacting axes contribute to low-resourcedness' of a language. We hope our work elicits explicit definitions of the terminology when it is used in papers.
  arXiv  Detail & Related papers  (2024-10-28T08:05:34Z)
• NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages [54.808217147579036]
  We conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content.
  arXiv  Detail & Related papers  (2023-09-19T14:42:33Z)
• Transfer to a Low-Resource Language via Close Relatives: The Case Study on Faroese [54.00582760714034]
  Cross-lingual NLP transfer can be improved by exploiting data and models of high-resource languages. We release a new web corpus of Faroese and Faroese datasets for named entity recognition (NER), semantic text similarity (STS) and new language models trained on all Scandinavian languages.
  arXiv  Detail & Related papers  (2023-04-18T08:42:38Z)
• Reinforced Iterative Knowledge Distillation for Cross-Lingual Named Entity Recognition [54.92161571089808]
  Cross-lingual NER transfers knowledge from rich-resource language to languages with low resources. Existing cross-lingual NER methods do not make good use of rich unlabeled data in target languages. We develop a novel approach based on the ideas of semi-supervised learning and reinforcement learning.
  arXiv  Detail & Related papers  (2021-06-01T05:46:22Z)
• Soft Gazetteers for Low-Resource Named Entity Recognition [78.00856159473393]
  We propose a method of "soft gazetteers" that incorporates ubiquitously available information from English knowledge bases into neural named entity recognition models. Our experiments on four low-resource languages show an average improvement of 4 points in F1 score.
  arXiv  Detail & Related papers  (2020-05-04T21:58:02Z)
• Distant Supervision and Noisy Label Learning for Low Resource Named Entity Recognition: A Study on Hausa and Yor\`ub\'a [23.68953940000046]
  Techniques such as distant and weak supervision can be used to create labeled data in a (semi-) automatic way. We evaluate different embedding approaches and show that distant supervision can be successfully leveraged in a realistic low-resource scenario.
  arXiv  Detail & Related papers  (2020-03-18T17:48:35Z)
• LSCP: Enhanced Large Scale Colloquial Persian Language Understanding [2.7249643773851724]
  "Large Scale Colloquial Persian dataset" aims to describe the colloquial language of low-resourced languages. The proposed corpus consists of 120M sentences resulted from 27M tweets annotated with parsing tree, part-of-speech tags, sentiment polarity and translation in five different languages.
  arXiv  Detail & Related papers  (2020-03-13T22:24:14Z)
• Cross-lingual, Character-Level Neural Morphological Tagging [57.0020906265213]
  We train character-level recurrent neural taggers to predict morphological taggings for high-resource languages and low-resource languages together. Learning joint character representations among multiple related languages successfully enables knowledge transfer from the high-resource languages to the low-resource ones, improving accuracy by up to 30% over a monolingual model.
  arXiv  Detail & Related papers  (2017-08-30T08:14:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.