Supervised Learning for Robust Term Extraction

We propose a machine learning method to automatically classify the extracted ngrams from a corpus into terms and non-terms. We use 10 common statistics in previous term extraction literature as features for training. The proposed method, applicable to term recognition in multiple domains and languages, can help 1) avoid the laborious work in the post-processing (e.g. subjective threshold setting); 2) handle the skewness and demonstrate noticeable resilience to domain-shift issue of training data. Experiments are carried out on 6 corpora of multiple domains and languages, including GENIA and ACLRD-TEC(1.0) corpus as training set and four TTC subcorpora of wind energy and mobile technology in both Chinese and English as test set. Promising results are found, which indicate that this approach is capable of identifying both single word terms and multiword terms with reasonably good precision and recall

Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction

Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations. However, the generated training data typically contain massive noise, and may result in poor performances with the vanilla supervised learning. In this paper, we propose to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C$^2$SA), which leads to noise-robust training for distant supervised relation extractor. Specifically, we employ the sentence-level selective attention to reduce the effect of noisy or mismatched sentences, while the correlation among relations were captured to improve the quality of attention weights. Moreover, instead of treating all entity-pairs equally, we try to pay more attention to entity-pairs with a higher quality. Similarly, we adopt the selective attention mechanism to achieve this goal. Experiments with two types of relation extractor demonstrate the superiority of the proposed approach over the state-of-the-art, while further ablation studies verify our intuitions and demonstrate the effectiveness of our proposed two techniques.Comment: AAAI 201