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README.md

README.md

Veyn

Veyn is a system for automatic identification of multiword expressions in running text submitted to the PARSEME shared task 2018. The model is first trained on a MWE-annotated corpus, and then can be applied to any new text to identify MWEs that are similar to those in the training corpus.

Veyn is based on a sequence tagger using recurrent neural networks. As input features it takes the lemmas and POS tags of words. We represent the output MWEs using a variant of the begin-inside-outside encoding scheme combined with the MWE category tag.

Veyn is implemented using Python's keras library

For more details, check the following scientific article:

Nicolas Zampieri, Manon Scholivet, Carlos Ramisch and Benoit Favre (2018). Veyn at PARSEME Shared Task 2018: Recurrent Neural Networks for VMWE Identification. In LAW-MWE-CxG 2018 workshop. Santa Fe, NM, USA.

Installation

Veyn was developed with python2.7 using the free libraries keras(2.2.0), tensorflow(1.8.0) and keras-contrib (only to use --activationCRF). Download all the required libraries, and then simply clone this git repository.

When training the models, we used a directory named Model to stock our system models. However, this directory is too large and we cannot push it on the github directory. You can create this repository with this command : mkdir Model and then train the models.

Data

We used the shared task corpora to create/tune this system. You can show data on the official website of the PARSEME shared task 2018 or directly on there gitlab repository.

Commands

./bin/Veyn.py -h to show all commands in a terminal.

Examples commands

Command to create and train a model:

./bin/Veyn.py --file fileTest/trial-train.cupt --mode train --model Model/trial-model -cat

Command to load and test a model:

./bin/Veyn.py --file fileTest/trial-test.cupt --mode test --model Model/trial-model

To use Veyn in test mode, only options (--file, --mode, --model) are required.

Command table

Commands Required Definition
-h, --help False Helpers and print all commands in stdout
-feat, --featureColumns False To treat columns as features. The first column is number 1, the second 2... By default, features are LEMME and POS, e.g 3 4
--mweTags False To give the number of the column containing tags (default 11) Careful! The first column is number 1, the second number 2, ...
--embeddings False To give some files containing embeddings. First, you give the path of the file containing embeddings, and separate with a "," you gave the column concern by this file. eg: file1,2 file2,5 ... You could have only column match with featureColumns.
--file True Give a file in the Extended CoNLL-U (.cupt) format. You can only give one file to train/test a model. You can give a CoNLL file to only test it.
--mode True To choice the mode of the system : train/test. If the file is a train file and you want to create a model use 'train'. If the file is a test/dev file and you want to load a model use 'test'. In test mode the system doesn't need params RNN.
--model True Name of the model which you want to save/load without extension. e.g 'nameModel' , and the system save/load files nameModel.h5, nameModel.json and nameModel.voc. nameModel.h5 is the model file. nameModel.voc is the vocabulary file. nameModel.args is the arguments file which train your model.
--io False Option to use the representation of IO. You can combine with other options like --nogap or/and --cat. By default, the representation is BIO.
-ng, --ngap False Option to use the representation of BIO/IO without gap. By default, the gap it is using to the representation of BIO/IO.
-cat, --category False Option to use the representation of BIO/IO with categories. By default, the representation of BIO/IO is without categories.
--sentences_per_batch False Option to initialize the size of mini batch for the RNN. By default, batch_size is 128.
--max_sentence_size False Option to initialize the size of sentence for the RNN. By default, max_sentence_size is 200.
--overlaps False Option to use the representation of BIO/IO with overlaps. We can't load a file test with overlaps, if option test and overlaps are activated, only the option test is considered. By default, the representation is without overlaps.
--validation_split False Option to configure the validation_split to train the RNN. By default 0.3(30%) of train file is use to validation data.
--validation_data False Give a file in the Extended CoNLL-U (.cupt) format to loss function for the RNN.
--epochs False Number of epochs to train RNN. By default, RNN trains on 10 epochs.
--recurrent_unit False This option allows choosing the type of recurrent units in the recurrent layer. By default it is biGRU. You can choice GRU, LSTM, biGRU, biLSTM.
--number_recurrent_layer False This option allows choosing the numbers of recurrent layer. By default it is 2 recurrent layers.
--size_recurrent_layer False This option allows choosing the size of recurrent layer. By default it is 512.
--feat_embedding_size False Option that takes as input a sequence of integers corresponding to the dimension/size of the embeddings layer of each column given to the --feat option. By default, all embeddings have the same size, use the current default value (64)
--early_stopping_mode False Option to save the best model training in function of acc/loss value, only if you use validation_data or validation_split. By default, it is in function of the loss value.
--patience_early_stopping False Option to choice patience for the early stopping. By default, it is 5 epochs.
--numpy_seed False Option to initialize manually the seed of numpy. By default, it is initialized to 42.
--tensorflow_seed False Option to initialize manually the seed of tensorflow. By default, it is initialized to 42.
--random_seed False Option to initialize manually the seed of random library. By default, it is initialized to 42.
--dropout False Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
--recurrent_dropout False Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
--no_fine_tuning_embeddings False Option to no tune embeddings in train. We can't used its option without --embeddings.
--activationCRF False Option to replace activation('softmax') by a CRF layer.
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