Literature Review: Neural Architectures for Named Entity Recognition

Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016). Neural Architectures for Named Entity Recognition. https://doi.org/10.18653/v1/N16-1030

Research Gap

State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available.

Research Work

They introduce two new neural architectures

• one based on bidirectional LSTMs and conditional random fields
• one that constructs and labels segments using a transition-based approach inspired by shift-reduce parsers.

Their model rely on two sources of information about words:

• character-based word representations learned from the supervised corpus.
• unsupervised word representations learned from unannotated corpora.

Token-level evidence for "being a name" includes both

• orthographic evidence (what does the word being tagged as a name look like?)
  • use character-based word representation model (Ling et al., 2015b) to capture orthographic sensitivity.
• distributional evidence (where does the word being tagged tend to occur in a corpus)
  • use distributional representations (Mikolov et al., 2013b) to capture distributional sensitivity.

Results

obtain state-of-the-art performance in NER in four languages

Innovation

without resorting to any language-specific knowledge or resources such as gazetteers.