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. The script will train the model on the training data, test it on the test data, and print various evaluation metrics.

• python (tested with 2.7.6)
• numpy (tested with 1.11.3)
• theano (tested with 0.8.2)
• lasagne (tested with 0.1)

The training and test data is expected in standard CoNLL-type tab-separated format. One word per line, separate column for token and label, empty line between sentences.

For error detection, this would be something like:

I       c
saws    i
the     c
show    c

The first column is assumed to be the token and the last column is the label. There can be other columns in the middle, which are currently not used. For example:

EU      NNP     I-NP    S-ORG
rejects VBZ     I-VP    O
German  JJ      I-NP    S-MISC
call    NN      I-NP    O
to      TO      I-VP    O
boycott VB      I-VP    O
British JJ      I-NP    S-MISC
lamb    NN      I-NP    O
.       .       O       O

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.
• lowercase_words - Whether words should be lowercased when mapping to word embeddings.
• lowercase_chars - Whether characters should be lowercased when mapping to character embeddings.
• replace_digits - Whether all digits should be replaced by 0.
• min_word_freq - Minimal frequency of words to be included in the vocabulary. Others will be considered OOV.
• use_singletons - Option for randomly mapping words with count 1 to OOVs.
• allowed_word_length - Maximum allowed word length, clipping the rest. Can be necessary if the text contains unreasonably long tokens, eg URLs.
• 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.
• 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)).
• dropout_input - The probability for applying dropout. 0.0 means no dropout.
• lmcost_gamma - Weight for the language modeling loss.
• lmcost_layer_size = Hidden layer size for the language modeling loss.
• lmcost_max_vocab_size = Maximum vocabulary size for the language modeling loss. The remaining words are mapped to a single entry.
• epochs - Maximum number of epochs to run.
• 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.
• stop_if_no_improvement_for_epochs - Training will be stopped if there has been no improvement for n epochs.
• learningrate - Learning rate.
• opt_strategy - Optimisation method: sgd/adadelta/adam.
• max_batch_size - Maximum batch size.
• save - Path to save the model.
• load - Path to load the model.
• garbage_collection - Whether garbage collection is explicitly called. Makes things slower but can operate with bigger models.
• 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.

There is now a separate script for loading a saved model and using it to print output for a given input file. Use the save option in the config file for saving the model. The input file needs to be in the same format as the training data (one word per line, labels in a separate column). The labels are expected for printing output as well. If you don't know the correct labels, just print any valid label in that field.

To print the output, run:

python print_output.py labels model_file input_file

This will print the input file to standard output, with an extra column at the end that shows the prediction.

You can also use:

python print_output.py probs model_file input_file

This will print the individual probabilities for each of the possible labels.

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)

If you use the language modeling objective, please reference:

Semi-supervised Multitask Learning for Sequence Labeling
Marek Rei
In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL-2017)

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) 2017 Marek Rei

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