CNN Bidirectional LSTM

Introduced by Chiu et al. in Named Entity Recognition with Bidirectional LSTM-CNNs

A CNN BiLSTM is a hybrid bidirectional LSTM and CNN architecture. In the original formulation applied to named entity recognition, it learns both character-level and word-level features. The CNN component is used to induce the character-level features. For each word the model employs a convolution and a max pooling layer to extract a new feature vector from the per-character feature vectors such as character embeddings and (optionally) character type.

Source: Named Entity Recognition with Bidirectional LSTM-CNNs

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Papers

Paper	Code	Results	Date	Stars

Tasks

Task	Papers	Share
Named Entity Recognition (NER)	6	13.04%
Dependency Parsing	2	4.35%
Feature Engineering	2	4.35%
Graph Representation Learning	1	2.17%
Link Prediction	1	2.17%
Language Modelling	1	2.17%
Semantic Textual Similarity	1	2.17%
Sentence Embedding	1	2.17%
Transportation Mode Detection	1	2.17%

Usage Over Time

This feature is experimental; we are continuously improving our matching algorithm.

Components

Component	Type	Add Remove
BiLSTM	Deep Tabular Learning
Convolution	Convolutions

Categories

Add Remove

Bidirectional Recurrent Neural Networks