Language Independent Named Entity Recognition via Orthogonal Transformation of Word Vectors
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Abstract (arXiv)
Word embeddings have been a key building block for NLP in which models relied heavily on word embeddings in many different tasks. In this paper, a model is proposed based on using Bidirectional LSTM/CRF with word embeddings to perform named entity recognition for any language. This is done by training a model on a source language (English) and transforming word embeddings from the target language into word embeddings of the source language by using an orthogonal linear transformation matrix. Evaluation of the model shows that by training a model on an English dataset the model was capable of detecting named entities in an Arabic dataset without neither training or fine tuning the model on an Arabic language dataset.
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References · 8
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
- 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
- 2016Enriching Word Vectors with Subword Informationcited by 1,583
- 2013Speech Recognition with Deep Recurrent Neural Networkscited by 669
- 2016Neural Architectures for Named Entity Recognitioncited by 550
- 2015Bidirectional LSTM-CRF Models for Sequence Taggingcited by 546
- 2017Offline bilingual word vectors, orthogonal transformations and the inverted softmaxcited by 96
- 2016Multi-Task Cross-Lingual Sequence Tagging from Scratchcited by 6
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