Papers with Bi-LSTM-

2 papers
SiNER: A Large Dataset for Sindhi Named Entity Recognition (2020.lrec-1)

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Challenge: Named entity recognition is an essential lower-level task in natural language processing (NLP).
Approach: They propose to develop a named entity recognition dataset for low-resourced Sindhi language with quality baselines.
Outcome: The proposed dataset is likely to be a significant resource for statistical Sindhi language processing.
M-CNER: A Corpus for Chinese Named Entity Recognition in Multi-Domains (L18-1)

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Challenge: NER is one of the most important natural language processing tasks.
Approach: They propose to annotate sentences from human-computer interaction, social media, and e-commerce using two rounds of annotation.
Outcome: The proposed system performs the best on all the data sets.

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