Exploring Different Dimensions of Attention for Uncertainty Detection

Neural networks with attention have proven effective for many natural language processing tasks. In this paper, we develop attention mechanisms for uncertainty detection. In particular, we generalize standardly used attention mechanisms by introducing external attention and sequence-preserving attention. These novel architectures differ from standard approaches in that they use external resources to compute attention weights and preserve sequence information. We compare them to other configurations along different dimensions of attention. Our novel architectures set the new state of the art on a Wikipedia benchmark dataset and perform similar to the state-of-the-art model on a biomedical benchmark which uses a large set of linguistic features.Comment: accepted at EACL 201

An NLP Analysis of Health Advice Giving in the Medical Research Literature

Health advice – clinical and policy recommendations – plays a vital role in guiding medical practices and public health policies. Whether or not authors should give health advice in medical research publications is a controversial issue. The proponents of actionable research advocate for the more efficient and effective transmission of science evidence into practice. The opponents are concerned about the quality of health advice in individual research papers, especially that in observational studies. Arguments both for and against giving advice in individual studies indicate a strong need for identifying and accessing health advice, for either practical use or quality evaluation purposes. However, current information services do not support the direct retrieval of health advice. Compared to other natural language processing (NLP) applications, health advice has not been computationally modeled as a language construct either. A new information service for directly accessing health advice should be able to reduce information barriers and to provide external assessment in science communication. This dissertation work built an annotated corpus of scientific claims that distinguishes health advice according to its occurrence and strength. The study developed NLP-based prediction models to identify health advice in the PubMed literature. Using the annotated corpus and prediction models, the study answered research questions regarding the practice of advice giving in medical research literature. To test and demonstrate the potential use of the prediction model, it was used to retrieve health advice regarding the use of hydroxychloroquine (HCQ) as a treatment for COVID-19 from LitCovid, a large COVID-19 research literature database curated by the National Institutes of Health. An evaluation of sentences extracted from both abstracts and discussions showed that BERT-based pre-trained language models performed well at detecting health advice. The health advice prediction model may be combined with existing health information service systems to provide more convenient navigation of a large volume of health literature. Findings from the study also show researchers are careful not to give advice solely in abstracts. They also tend to give weaker and non-specific advice in abstracts than in discussions. In addition, the study found that health advice has appeared consistently in the abstracts of observational studies over the past 25 years. In the sample, 41.2% of the studies offered health advice in their conclusions, which is lower than earlier estimations based on analyses of much smaller samples processed manually. In the abstracts of observational studies, journals with a lower impact are more likely to give health advice than those with a higher impact, suggesting the significance of the role of journals as gatekeepers of science communication. For the communities of natural language processing, information science, and public health, this work advances knowledge of the automated recognition of health advice in scientific literature. The corpus and code developed for the study have been made publicly available to facilitate future efforts in health advice retrieval and analysis. Furthermore, this study discusses the ways in which researchers give health advice in medical research articles, knowledge of which could be an essential step towards curbing potential exaggeration in the current global science communication. It also contributes to ongoing discussions of the integrity of scientific output. This study calls for caution in advice-giving in medical research literature, especially in abstracts alone. It also calls for open access to medical research publications, so that health researchers and practitioners can fully review the advice in scientific outputs and its implications. More evaluative strategies that can increase the overall quality of health advice in research articles are needed by journal editors and reviewers, given their gatekeeping role in science communication

Deep learning methods for knowledge base population

Knowledge bases store structured information about entities or concepts of the world and can be used in various applications, such as information retrieval or question answering. A major drawback of existing knowledge bases is their incompleteness. In this thesis, we explore deep learning methods for automatically populating them from text, addressing the following tasks: slot filling, uncertainty detection and type-aware relation extraction. Slot filling aims at extracting information about entities from a large text corpus. The Text Analysis Conference yearly provides new evaluation data in the context of an international shared task. We develop a modular system to address this challenge. It was one of the top-ranked systems in the shared task evaluations in 2015. For its slot filler classification module, we propose contextCNN, a convolutional neural network based on context splitting. It improves the performance of the slot filling system by 5.0% micro and 2.9% macro F1. To train our binary and multiclass classification models, we create a dataset using distant supervision and reduce the number of noisy labels with a self-training strategy. For model optimization and evaluation, we automatically extract a labeled benchmark for slot filler classification from the manual shared task assessments from 2012-2014. We show that results on this benchmark are correlated with slot filling pipeline results with a Pearson's correlation coefficient of 0.89 (0.82) on data from 2013 (2014). The combination of patterns, support vector machines and contextCNN achieves the best results on the benchmark with a micro (macro) F1 of 51% (53%) on test. Finally, we analyze the results of the slot filling pipeline and the impact of its components. For knowledge base population, it is essential to assess the factuality of the statements extracted from text. From the sentence "Obama was rumored to be born in Kenya", a system should not conclude that Kenya is the place of birth of Obama. Therefore, we address uncertainty detection in the second part of this thesis. We investigate attention-based models and make a first attempt to systematize the attention design space. Moreover, we propose novel attention variants: External attention, which incorporates an external knowledge source, k-max average attention, which only considers the vectors with the k maximum attention weights, and sequence-preserving attention, which allows to maintain order information. Our convolutional neural network with external k-max average attention sets the new state of the art on a Wikipedia benchmark dataset with an F1 score of 68%. To the best of our knowledge, we are the first to integrate an uncertainty detection component into a slot filling pipeline. It improves precision by 1.4% and micro F1 by 0.4%. In the last part of the thesis, we investigate type-aware relation extraction with neural networks. We compare different models for joint entity and relation classification: pipeline models, jointly trained models and globally normalized models based on structured prediction. First, we show that using entity class prediction scores instead of binary decisions helps relation classification. Second, joint training clearly outperforms pipeline models on a large-scale distantly supervised dataset with fine-grained entity classes. It improves the area under the precision-recall curve from 0.53 to 0.66. Third, we propose a model with a structured prediction output layer, which globally normalizes the score of a triple consisting of the classes of two entities and the relation between them. It improves relation extraction results by 4.4% F1 on a manually labeled benchmark dataset. Our analysis shows that the model learns correct correlations between entity and relation classes. Finally, we are the first to use neural networks for joint entity and relation classification in a slot filling pipeline. The jointly trained model achieves the best micro F1 score with a score of 22% while the neural structured prediction model performs best in terms of macro F1 with a score of 25%

Deep learning methods for knowledge base population

Knowledge bases store structured information about entities or concepts of the world and can be used in various applications, such as information retrieval or question answering. A major drawback of existing knowledge bases is their incompleteness. In this thesis, we explore deep learning methods for automatically populating them from text, addressing the following tasks: slot filling, uncertainty detection and type-aware relation extraction. Slot filling aims at extracting information about entities from a large text corpus. The Text Analysis Conference yearly provides new evaluation data in the context of an international shared task. We develop a modular system to address this challenge. It was one of the top-ranked systems in the shared task evaluations in 2015. For its slot filler classification module, we propose contextCNN, a convolutional neural network based on context splitting. It improves the performance of the slot filling system by 5.0% micro and 2.9% macro F1. To train our binary and multiclass classification models, we create a dataset using distant supervision and reduce the number of noisy labels with a self-training strategy. For model optimization and evaluation, we automatically extract a labeled benchmark for slot filler classification from the manual shared task assessments from 2012-2014. We show that results on this benchmark are correlated with slot filling pipeline results with a Pearson's correlation coefficient of 0.89 (0.82) on data from 2013 (2014). The combination of patterns, support vector machines and contextCNN achieves the best results on the benchmark with a micro (macro) F1 of 51% (53%) on test. Finally, we analyze the results of the slot filling pipeline and the impact of its components. For knowledge base population, it is essential to assess the factuality of the statements extracted from text. From the sentence "Obama was rumored to be born in Kenya", a system should not conclude that Kenya is the place of birth of Obama. Therefore, we address uncertainty detection in the second part of this thesis. We investigate attention-based models and make a first attempt to systematize the attention design space. Moreover, we propose novel attention variants: External attention, which incorporates an external knowledge source, k-max average attention, which only considers the vectors with the k maximum attention weights, and sequence-preserving attention, which allows to maintain order information. Our convolutional neural network with external k-max average attention sets the new state of the art on a Wikipedia benchmark dataset with an F1 score of 68%. To the best of our knowledge, we are the first to integrate an uncertainty detection component into a slot filling pipeline. It improves precision by 1.4% and micro F1 by 0.4%. In the last part of the thesis, we investigate type-aware relation extraction with neural networks. We compare different models for joint entity and relation classification: pipeline models, jointly trained models and globally normalized models based on structured prediction. First, we show that using entity class prediction scores instead of binary decisions helps relation classification. Second, joint training clearly outperforms pipeline models on a large-scale distantly supervised dataset with fine-grained entity classes. It improves the area under the precision-recall curve from 0.53 to 0.66. Third, we propose a model with a structured prediction output layer, which globally normalizes the score of a triple consisting of the classes of two entities and the relation between them. It improves relation extraction results by 4.4% F1 on a manually labeled benchmark dataset. Our analysis shows that the model learns correct correlations between entity and relation classes. Finally, we are the first to use neural networks for joint entity and relation classification in a slot filling pipeline. The jointly trained model achieves the best micro F1 score with a score of 22% while the neural structured prediction model performs best in terms of macro F1 with a score of 25%

An Empirical Study on Uncertainty Identification in Social Media Context

Uncertainty text detection is important to many social-media-based applications since more and more users utilize social media platforms (e.g., Twitter, Facebook, etc.) as information source to produce or derive interpretations based on them. However, existing uncertainty cues are ineffective in social media context because of its specific characteristics. In this paper, we propose a variant of annotation scheme for uncertainty identification and construct the first uncertainty corpus based on tweets. We then conduct experiments on the generated tweets corpus to study the effectiveness of different types of features for uncertainty text identification.