Chunking

20 papers with code · Natural Language Processing

Chunking, also known as shallow parsing, identifies continuous spans of tokens that form syntactic units such as noun phrases or verb phrases.

Example:

Vinken , 61 years old
B-NLP I-NP I-NP I-NP I-NP

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Greatest papers with code

Semi-supervised sequence tagging with bidirectional language models

ACL 2017 zalandoresearch/flair

Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level representations to produce context sensitive representations is trained on relatively little labeled data.

CHUNKING NAMED ENTITY RECOGNITION WORD EMBEDDINGS

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

ACL 2018 jiesutd/NCRFpp

This paper describes NCRF++, a toolkit for neural sequence labeling. NCRF++ is designed for quick implementation of different neural sequence labeling models with a CRF inference layer.

CHUNKING NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING

Design Challenges and Misconceptions in Neural Sequence Labeling

COLING 2018 jiesutd/NCRFpp

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). Misconceptions and inconsistent conclusions in existing literature are examined and clarified under statistical experiments.

CHUNKING

Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks

21 Jul 2017jiesutd/NCRFpp

Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance. However, little is published which parameters and design choices should be evaluated or selected making the correct hyperparameter optimization often a "black art that requires expert experiences" (Snoek et al., 2012).

CHUNKING WORD EMBEDDINGS

Natural Language Processing (almost) from Scratch

2 Mar 2011facebook/fb-caffe-exts

We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge.

CHUNKING NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING SEMANTIC ROLE LABELING

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. This language modeling objective incentivises the system to learn general-purpose patterns of semantic and syntactic composition, which are also useful for improving accuracy on different sequence labeling tasks.

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

Substitute Based SCODE Word Embeddings in Supervised NLP Tasks

25 Jul 2014ai-ku/wvec

The results show that the proposed method achieves as good as or better results compared to the other word embeddings in the tasks we investigate. It achieves state-of-the-art results in multilingual dependency parsing.

CHUNKING DEPENDENCY PARSING NAMED ENTITY RECOGNITION WORD EMBEDDINGS

Bidirectional LSTM-CRF Models for Sequence Tagging

9 Aug 2015GlassyWing/bi-lstm-crf

In this paper, we propose a variety of Long Short-Term Memory (LSTM) based models for sequence tagging. It can also use sentence level tag information thanks to a CRF layer.

CHUNKING

A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

EMNLP 2017 rubythonode/joint-many-task-model

Transfer and multi-task learning have traditionally focused on either a single source-target pair or very few, similar tasks. Ideally, the linguistic levels of morphology, syntax and semantics would benefit each other by being trained in a single model.

CHUNKING MULTI-TASK LEARNING