This is a neural network sequence labeling system. Given a sequence of tokens, it will learn to assign labels to each token. Can be used for named entity recognition, POS-tagging, error detection, chunking, CCG supertagging, etc.
The main model implements a bidirectional LSTM for sequence tagging. In addition, you can incorporate character-level information -- either by concatenating a character-based representation, or by using an attention/gating mechanism for combining it with a word embedding.
Run with:
python sequence_labeling_experiment.py config.conf
Preferably with Theano set up to use CUDA, so the process can run on a GPU.
- numpy
- theano
- lasagne
Edit the values in config.conf as needed:
- path_train - Path to the training data, in CoNLL tab-separated format. One word per line, first column is the word, last column is the label. Empty lines between sentences.
- path_dev - Path to the development data, used for choosing the best epoch.
- path_test - Path to the test file. Can contain multiple files, colon separated.
- main_label - The output label for which precision/recall/F-measure are calculated.
- conll_eval - Whether the standard CoNLL NER evaluation should be run.
- preload_vectors - Path to the pretrained word embeddings, in word2vec plain text format. If your embeddings are in binary, you can use convertvec to convert them to plain text.
- word_embedding_size - Size of the word embeddings used in the model.
- char_embedding_size - Size of the character embeddings.
- word_recurrent_size - Size of the word-level LSTM hidden layers.
- char_recurrent_size - Size of the char-level LSTM hidden layers.
- narrow_layer_size - Size of the extra hidden layer on top of the bi-LSTM.
- best_model_selector - What is measured on the dev set for model selection: "dev_conll_f:high" for NER and chunking, "dev_acc:high" for POS-tagging, "dev_f05:high" for error detection.
- epochs - Maximum number of epochs to run.
- stop_if_no_improvement_for_epochs - Training will be stopped if there has been no improvement for n epochs.
- learningrate - Learning rate.
- min_word_freq - Minimal frequency of words to be included in the vocabulary. Others will be considered OOV.
- max_batch_size - Maximum batch size.
- save - Path to save the model.
- load - Path to load the model.
- random_seed - Random seed for initialisation and data shuffling. This can affect results, so for robust conclusions I recommend running multiple experiments with different seeds and averaging the metrics.
- crf_on_top - If True, use a CRF as the output layer. If False, use softmax instead.
- char_integration_method - How character information is integrated. Options are: "none" (not integrated), "input" (concatenated), "attention" (the method proposed in Rei et al. (2016)).
If you use the main sequence labeling code, please reference:
Compositional Sequence Labeling Models for Error Detection in Learner Writing
Marek Rei and Helen Yannakoudakis
In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL-2016)
If you use the character-level attention component, please reference:
Attending to characters in neural sequence labeling models
Marek Rei, Gamal K.O. Crichton and Sampo Pyysalo
In Proceedings of the 26th International Conference on Computational Linguistics (COLING-2016)
The CRF implementation is based on:
Neural Architectures for Named Entity Recognition
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami and Chris Dyer
In Proceedings of NAACL-HLT 2016
The conlleval.py script is from: https://github.com/spyysalo/conlleval.py
MIT License
Copyright (c) 2016 Marek Rei
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