{'fraction_non_alphanumeric': 0.01984732824427481, 'fraction_numerical': 0.0, 'mean_word_length': 5.833333333333333, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Chinese Relation Extraction with Phrase Component Representations

Recently, the Lattice structure has been proved to be effective for Chinese entity relation extraction by incorporating the word information. However, this method cannot utilize phrases that contain sentence structure information. In order to solve the problem, we introduced a Lattice model with phrase component representation, which can merge word, phrase and character input. We also apply the multi-head self-attention mechanism in the classification layer to effectively learn the dependencies between distant words. After comparative experiments, our model achieves better results.