Named Entity Recognition with Bidirectional LSTM-CNNs

TACL 2016 10 code implementations

Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance.

ENTITY LINKING FEATURE ENGINEERING NAMED ENTITY RECOGNITION (NER) WORD EMBEDDINGS

LSTM-based Deep Learning Models for Non-factoid Answer Selection

12 Nov 20153 code implementations

One direction is to define a more composite representation for questions and answers by combining convolutional neural network with the basic framework.

ANSWER SELECTION

Regularizing and Optimizing LSTM Language Models

ICLR 2018 27 code implementations

Recurrent neural networks (RNNs), such as long short-term memory networks (LSTMs), serve as a fundamental building block for many sequence learning tasks, including machine translation, language modeling, and question answering.

LANGUAGE MODELLING

Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition

27 Sep 20172 code implementations

Named Entity Recognition (NER) is one of the most common tasks of the natural language processing.

NAMED ENTITY RECOGNITION (NER) WORD EMBEDDINGS

End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

ACL 2016 12 code implementations

State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of hand-crafted features and data pre-processing.

FEATURE ENGINEERING NAMED ENTITY RECOGNITION (NER) PART-OF-SPEECH TAGGING

Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks

21 Jul 20173 code implementations

Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance.

CHUNKING WORD EMBEDDINGS

Chinese NER Using Lattice LSTM

ACL 2018 2 code implementations

We investigate a lattice-structured LSTM model for Chinese NER, which encodes a sequence of input characters as well as all potential words that match a lexicon.

CHINESE NAMED ENTITY RECOGNITION

Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging

EMNLP 2017 5 code implementations

In this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches.

Learning Natural Language Inference with LSTM

HLT 2016 4 code implementations

On the SNLI corpus, our model achieves an accuracy of 86. 1%, outperforming the state of the art.

NATURAL LANGUAGE INFERENCE SENTENCE EMBEDDINGS