# Empirical Study of Named Entity Recognition Performance Using Distribution-aware Word Embedding

arXiv: https://arxiv.org/abs/2109.01636 · HTML: https://mlsanity.com/paper/2109.01636/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 3 Sep 2021.

## Builds on (most cited first, 14 of 14)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation](https://mlsanity.com/paper/1408.3456/) (2014)
- [A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://mlsanity.com/paper/1910.11470/) (2019)
- [Deep Active Learning for Named Entity Recognition](https://mlsanity.com/paper/1707.05928/) (2017)
- [Word Representations via Gaussian Embedding](https://mlsanity.com/paper/1412.6623/) (2014)
- [CrossWeigh: Training Named Entity Tagger from Imperfect Annotations](https://mlsanity.com/paper/1909.01441/) (2019)
- [Spherical Text Embedding](https://mlsanity.com/paper/1911.01196/) (2019)
- [Discriminative Topic Mining via Category-Name Guided Text Embedding](https://mlsanity.com/paper/1908.07162/) (2019)
- [Robust Named Entity Recognition with Truecasing Pretraining](https://mlsanity.com/paper/1912.07095/) (2019)
- [Embedding Words as Distributions with a Bayesian Skip-gram Model](https://mlsanity.com/paper/1711.11027/) (2017)
- [Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling](https://mlsanity.com/paper/1804.07827/) (2018)
- [End-to-end Recurrent Neural Network Models for Vietnamese Named Entity Recognition: Word-level vs. Character-level](https://mlsanity.com/paper/1705.04044/) (2017)
- [Raw-to-End Name Entity Recognition in Social Media](https://mlsanity.com/paper/1908.05344/) (2019)

## Abstract (arXiv)

With the fast development of Deep Learning techniques, Named Entity Recognition (NER) is becoming more and more important in the information extraction task. The greatest difficulty that the NER task faces is to keep the detectability even when types of NE and documents are unfamiliar. Realizing that the specificity information may contain potential meanings of a word and generate semantic-related features for word embedding, we develop a distribution-aware word embedding and implement three different methods to make use of the distribution information in a NER framework. And the result shows that the performance of NER will be improved if the word specificity is incorporated into existing NER methods.