A Bi-LSTM-RNN Model for Relation ClassificationUsing Low-Cost Sequence Features
TABLE I: Comparisons with other published results (%) of neural network models. NER denotes the features of named entity recognition.
['[BOLD] Approaches', '[BOLD] Features', '[BOLD] F1']
The weight matrices W, bias vectors b and embedding lookup tables Eran, Echar, Epos, Ewnh, are randomly initialized in the range (-0.01, 0.01) with a uniform distribution. The Wikipedia text is preprocessed in the following steps: non-English characters or words are removed; a sentence is removed if it is too short; text is tokenized and all the tokens are transformed into their lowercase forms. Feature embeddings are tuned during training except pre-trained word embeddings. Results MVRNN CNN-based depLCNN After taking the relation directionality into consideration by a negative sampling strategy, depLCNN achieves state-of-the-art performance (85.6%). only use sequence features such as words and word positions. CR-CNN can achieve 84.1% in F1 with special treatment for noisy Other class, but its F1 is 82.7% without such special treatment. Our model obtains slightly lower but comparable performance compared with them.