Semantic relationships, such as hyponym–hypernym, cause–effect, meronym–holonym etc., between a pair of entities in a sentence are usually reflected through syntactic patterns. Automatic extraction of such patterns benefits several downstream tasks, including, entity extraction, ontology building, and question answering. Unfortunately, automatic extraction of such patterns has not yet received much attention from NLP and information retrieval researchers. In this work, we propose an attention-based supervised deep learning model, ASPER, which extracts syntactic patterns between entities exhibiting a given semantic relation in the sentential context. We validate the performance of ASPER on three distinct semantic relations—hyponym–hypernym, cause–effect, and meronym–holonym on six datasets. Experimental results show that for all these semantic relations, ASPER can automatically identify a collection of syntactic patterns reflecting the existence of such a relation between a pair of entities in a sentence. In comparison to the existing methodologies of syntactic pattern extraction, ASPER’s performance is substantially superior.
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SearchIE: A Retrieval Approach for Information Extraction
We address the problem of entity extraction with a very few examples and address it with an information retrieval approach. Existing extraction approaches consider millions of features extracted from a large number of training data cases. Generally, these data cases are generated by a distant supervision approach with entities in a knowledge base. After that a model is learned and entities are extracted. However, with extremely limited data a ranked list of relevant entities can be helpful to obtain user feedback to get more training data. As Information Retrieval (IR) is a natural choice for ranked list generation, we explore its effectiveness in such a limited data case. To this end, we propose SearchIE, a hybrid of IR and NLP approach that indexes documents represented using handcrafted NLP features. At query time SearchIE samples terms from a Logistic Regression model trained with extremely limited data. We show that SearchIE supersedes state-of-the-art NLP models to find civilians killed by US police officers with even a single civilian name as example.
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- Award ID(s):
- 1617408
- PAR ID:
- 10175987
- Date Published:
- Journal Name:
- Proceedings of the International Conference on the Theory of Information Retrieval (ICTIR '19)
- Page Range / eLocation ID:
- 249 to 252
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Background Natural language processing (NLP) tasks in the health domain often deal with limited amount of labeled data due to high annotation costs and naturally rare observations. To compensate for the lack of training data, health NLP researchers often have to leverage knowledge and resources external to a task at hand. Recently, pretrained large-scale language models such as the Bidirectional Encoder Representations from Transformers (BERT) have been proven to be a powerful way of learning rich linguistic knowledge from massive unlabeled text and transferring that knowledge to downstream tasks. However, previous downstream tasks often used training data at such a large scale that is unlikely to obtain in the health domain. In this work, we aim to study whether BERT can still benefit downstream tasks when training data are relatively small in the context of health NLP.
Method We conducted a learning curve analysis to study the behavior of BERT and baseline models as training data size increases. We observed the classification performance of these models on two disease diagnosis data sets, where some diseases are naturally rare and have very limited observations (fewer than 2 out of 10,000). The baselines included commonly used text classification models such as sparse and dense bag-of-words models, long short-term memory networks, and their variants that leveraged external knowledge. To obtain learning curves, we incremented the amount of training examples per disease from small to large, and measured the classification performance in macro-averaged
$$F_{1}$$ Results On the task of classifying all diseases, the learning curves of BERT were consistently above all baselines, significantly outperforming them across the spectrum of training data sizes. But under extreme situations where only one or two training documents per disease were available, BERT was outperformed by linear classifiers with carefully engineered bag-of-words features.
Conclusion As long as the amount of training documents is not extremely few, fine-tuning a pretrained BERT model is a highly effective approach to health NLP tasks like disease classification. However, in extreme cases where each class has only one or two training documents and no more will be available, simple linear models using bag-of-words features shall be considered.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3341981.3344248
