Using Machine Reading to Understand Alzheimer's and Related Diseases from the Literature

Purpose： This paper aims to better understand a large number of papers in the medical domain of Alzheimer＇s disease （AD） and related diseases using the machine reading approach. Design/methodology/approach： The study uses the topic modeling method to obtain an overview of the field, and employs open information extraction to further comprehend the field at a specific fact level. Findings： Several topics within the AD research field are identified, such as the Human Immunodeficiency Virus （HIV）/Acquired Immune Deficiency Syndrome （AIDS）, which can help answer the question of how A1DS/HIV and AD are very different yet related diseases. Research limitations： Some manual data cleaning could improve the study, such as removing incorrect facts found by open information extraction. Practical implications： This study uses the literature to answer specific questions on a scientific domain, which can help domain experts find interesting and meaningful relations among entities in a similar manner, such as to discover relations between AD and AIDS/HIV. Origlnality/value： Both the overview and specific information from the literature are obtained using two distinct methods in a complementary manner. This combination is novel because previous work has only focused on one of them, and thus provides a better way to understand an important scientific field using data-driven methods.

Knowledge Graph based Mutual Attention for Machine Reading Comprehension over Anti-Terrorism Corpus

Machine reading comprehension has been a research focus in natural language processing and intelligence engineering.However,there is a lack of models and datasets for the MRC tasks in the anti-terrorism domain.Moreover,current research lacks the ability to embed accurate background knowledge and provide precise answers.To address these two problems,this paper first builds a text corpus and testbed that focuses on the anti-terrorism domain in a semi-automatic manner.Then,it proposes a knowledge-based machine reading comprehension model that fuses domain-related triples from a large-scale encyclopedic knowledge base to enhance the semantics of the text.To eliminate knowledge noise that could lead to semantic deviation,this paper uses a mixed mutual ttention mechanism among questions,passages,and knowledge triples to select the most relevant triples before embedding their semantics into the sentences.Experiment results indicate that the proposed approach can achieve a 70.70%EM value and an 87.91%F1 score,with a 4.23%and 3.35%improvement over existing methods,respectively.

Hybrid embedding and joint training of stacked encoder for opinion question machine reading comprehension

Opinion question machine reading comprehension(MRC)requires a machine to answer questions by analyzing corresponding passages.Compared with traditional MRC tasks where the answer to every question is a segment of text in corresponding passages,opinion question MRC is more challenging because the answer to an opinion question may not appear in corresponding passages but needs to be deduced from multiple sentences.In this study,a novel framework based on neural networks is proposed to address such problems,in which a new hybrid embedding training method combining text features is used.Furthermore,extra attention and output layers which generate auxiliary losses are introduced to jointly train the stacked recurrent neural networks.To deal with imbalance of the dataset,irrelevancy of question and passage is used for data augmentation.Experimental results show that the proposed method achieves state-of-the-art performance.We are the biweekly champion in the opinion question MRC task in Artificial Intelligence Challenger 2018(AIC2018).