Contextual String Embeddings for Sequence Labeling

COLING 2018 • zalandoresearch/flair •

Recent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters.

CHUNKING LANGUAGE MODELLING NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING WORD EMBEDDINGS

Semi-supervised sequence tagging with bidirectional language models

ACL 2017 • flairNLP/flair •

Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks.

CHUNKING NAMED ENTITY RECOGNITION

NCRF++: An Open-source Neural Sequence Labeling Toolkit

ACL 2018 • jiesutd/PyTorchSeqLabel •

This paper describes NCRF++, a toolkit for neural sequence labeling.

CHUNKING NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING

Design Challenges and Misconceptions in Neural Sequence Labeling

COLING 2018 • jiesutd/PyTorchSeqLabel •

We investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct a systematic model comparison on three benchmarks (i. e. NER, Chunking, and POS tagging).

CHUNKING

Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks

21 Jul 2017 • jiesutd/PyTorchSeqLabel •

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

CHUNKING HYPERPARAMETER OPTIMIZATION WORD EMBEDDINGS

Bidirectional LSTM-CRF Models for Sequence Tagging

9 Aug 2015 • guillaumegenthial/tf_ner •

It can also use sentence level tag information thanks to a CRF layer.

CHUNKING

Sudachi: a Japanese Tokenizer for Business

LREC 2018 • WorksApplications/Sudachi

CHUNKING LEMMATIZATION MORPHOLOGICAL ANALYSIS PART-OF-SPEECH TAGGING TOKENIZATION

Hybrid semi-Markov CRF for Neural Sequence Labeling

ACL 2018 • ZhixiuYe/HSCRF-pytorch •

This paper proposes hybrid semi-Markov conditional random fields (SCRFs) for neural sequence labeling in natural language processing.

CHUNKING DOMAIN ADAPTATION NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING WORD EMBEDDINGS

Semi-supervised Multitask Learning for Sequence Labeling

ACL 2017 • marekrei/sequence-labeler •

We propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset.

CHUNKING GRAMMATICAL ERROR DETECTION LANGUAGE MODELLING NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING

Evaluation of Word Vector Representations by Subspace Alignment

EMNLP 2015 • ytsvetko/qvec

CHUNKING NAMED ENTITY RECOGNITION SENTIMENT ANALYSIS WORD EMBEDDINGS