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2020 International workshop on Knowledge Representation and Representation Learning, KR4L 2020

作者： Alam, Mirza Mohtashim Nayyeri, Mojtaba Xu, Chengjin Al Hasan Rony, Md Rashad Yazdi, Hamed Shariat Sadeghi, Afshin Lehmann, Jens SDA Research Group University of Bonn Bonn Germany Fraunhofer IAIS Dresden Germany

Knowledge graph embedding (KGE) has become a prominent topic for many AI-based tasks such as recommendation systems, natural language processing, and link prediction. Inclusion of additional knowledge such as ontology, logical rules and text improves the learning process of KGE models. One of the main characteristics of knowledge graphs (KGs) is the existence of relational patterns (e.g., symmetric and inverse relations) which usually remain unseen by the embedding models. Inclusion of logical rules provides embedding models with additional information about the patterns already present in the KGs. The injection of logical rules has not yet been studied in depth for KGE models. In this paper, we propose an approach for rule-based learning on top of the two embedding models namely RotatE and TransE within this scope of the paper. We first study the effect of rule injection in the performance of the selected models. Second, we explore how the removal of leakage from popular KGs such as FB15k and WN18 affects the results. By leakage we are referring to the patterns exist in the training set from the test set (e.g. if the test set contains (h, r, t) then it also contains t, r, h in the training set which is considered as a symmetric leakage where t, r and h refers to tail, relation and head respectively). Empirical results suggest that incorporation of logical rules in the training process improves the performance of KGE models. © 2020 CEUR-WS. All rights reserved.

关键词： Knowledge graph

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Faceted hierarchy: A new graph type to organize scientific concepts and a construction method 13

Faceted hierarchy: A new graph type to organize scientific c...

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13th workshop on graph-based methods for natural language processing, Textgraphs 2019, in conjunction with the 2019 Conference on Empirical methods in natural language processing and 9th International Joint Conference on natural language processing, EMNLP-IJCNLP 2019

作者： Zeng, Qingkai Yu, Mengxia Yu, Wenhao Xiong, Jinjun Shi, Yiyu Jiang, Meng Department of Computer Science and Engineering University of Notre Dame Notre DameIN United States IBM T. J. Watson Research Center Yorktown HeightsNY United States

ISBN: (纸本)9781950737864

On a scientific concept hierarchy, a parent concept may have a few attributes, each of which has multiple values being a group of child concepts. We call these attributes facets: classification has a few facets such as application (e.g., face recognition), model (e.g., svm, knn), and metric (e.g., precision). In this work, we aim at building faceted concept hierarchies from scientific literature. Hierarchy construction methods heavily rely on hypernym detection, however, the faceted relations are parent-to-child links but the hypernym relation is a multi-hop, i.e., ancestor-todescendent link with a specific facet "type-of". We use information extraction techniques to find synonyms, sibling concepts, and ancestordescendent relations from a data science corpus. And we propose a hierarchy growth algorithm to infer the parent-child links from the three types of relationships. It resolves conflicts by maintaining the acyclic structure of a hierarchy. © 2019 EMNLP-IJCNLP 2019 - graph-based methods for natural language processing - proceedings of the 13th workshop. All rights reserved.

关键词： Face recognition

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Do sentence interactions matter? Leveraging sentence level representations for fake news classification 13

Do sentence interactions matter? Leveraging sentence level r...

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作者： Vaibhav Annasamy, Raghuram Mandyam Hovy, Eduard Language Technologies Institute School of Computer Science Carnegie Mellon University United States

ISBN: (纸本)9781950737864

The rising growth of fake news and misleading information through online media outlets demands an automatic method for detecting such news articles. Of the few limited works which differentiate between trusted vs other types of news article (satire, propaganda, hoax), none of them model sentence interactions within a document. We observe an interesting pattern in the way sentences interact with each other across different kind of news articles. To capture this kind of information for long news articles, we propose a graph neural networkbased model which does away with the need of feature engineering for fine grained fake news classification. Through experiments, we show that our proposed method beats strong neural baselines and achieves state-of-the-art accuracy on existing datasets. Moreover, we establish the generalizability of our model by evaluating its performance in out-of-domain scenarios. Code is available at https://***/MysteryVaibhav/ fake_news_semantics. © 2019 EMNLP-IJCNLP 2019 - graph-based methods for natural language processing - proceedings of the 13th workshop. All rights reserved.

关键词： Classification (of information)

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Identifying personal experience tweets of medication effects using pre-trained RoBERTa language model and its updating 11

Identifying personal experience tweets of medication effects...

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11th International workshop on Health Text Mining and Information Analysis, LOUHI 2020, co-located with EMNLP 2020

作者： Zhu, Minghao Song, Youzhe Jin, Ge Jiang, Keyuan Donghua University Shanghai China Purdue University Northwest HammondIN United States

ISBN: (纸本)9781952148811

Post-market surveillance, the practice of monitoring the safe use of pharmaceutical drugs is an important part of pharmacovigilance. Being able to collect personal experience related to pharmaceutical product use could help us gain insight into how the human body reacts to different medications. Twitter, a popular social media service, is being considered as an important alternative data source for collecting personal experience information with medications. Identifying personal experience tweets is a challenging classification task in natural language processing. In this study, we utilized three methods based on Facebook's Robustly Optimized BERT Pretraining Approach (RoBERTa) to predict personal experience tweets related to medication use: the first one combines the pre-trained RoBERTa model with a classifier, the second combines the updated pre-trained RoBERTa model using a corpus of unlabeled tweets with a classifier, and the third combines the RoBERTa model that was trained with our unlabeled tweets from scratch with the classifier too. Our results show that all of these approaches outperform the published methods (Word Embedding + LSTM) in classification performance (p © 2020 Association for Computational Linguistics

关键词： Computational linguistics

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KagNet: Knowledge-Aware graph Networks for Commonsense Reasoning 9

KagNet: Knowledge-Aware Graph Networks for Commonsense Reaso...

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Conference on Empirical methods in natural language processing / 9th International Joint Conference on natural language processing (EMNLP-IJCNLP)

作者： Lin, Bill Yuchen Chen, Xinyue Chen, Jamin Ren, Xiang Univ Southern Calif Comp Sci Dept Los Angeles CA 90007 USA Shanghai Jiao Tong Univ Comp Sci Dept Shanghai Peoples R China

ISBN: (纸本)9781950737901

Commonsense reasoning aims to empower machines with the human ability to make presumptions about ordinary situations in our daily life. In this paper, we propose a textual inference framework for answering commonsense questions, which effectively utilizes external, structured commonsense knowledge graphs to perform explainable inferences. The framework first grounds a question-answer pair from the semantic space to the knowledge-based symbolic space as a schema graph, a related sub-graph of external knowledge graphs. It represents schema graphs with a novel knowledge-aware graph network module named KAGNE T, and finally scores answers with graph representations. Our model is based on graph convolutional networks and LSTMs, with a hierarchical path-based attention mechanism. The intermediate attention scores make it transparent and interpretable, which thus produce trustworthy inferences. Using ConceptNet as the only external resource for BERT-based models, we achieved state-of-the-art performance on the CommonsenseQA, a large-scale dataset for commonsense reasoning. We open-source our code1 to the community for future research in knowledge-aware commonsense reasoning.

关键词： Knowledge graph

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Agreeing to Disagree: Choosing Among Eight Topic-Modeling methods

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BIG DATA RESEARCH 2021年 23卷

作者： Fu, Qiang Zhuang, Yufan Gu, Jiaxin Zhu, Yushu Guo, Xin Univ British Columbia Dept Sociol Vancouver BC Canada IBM Res Yorktown Hts NY USA Simon Fraser Univ Urban Studies Program Vancouver BC Canada Simon Fraser Univ Sch Publ Policy Vancouver BC Canada Hong Kong Polytech Univ Dept Appl Math Hong Kong Peoples R China Univ Queensland Sch Math & Phys Brisbane Qld Australia

Topic modeling is a key research area in natural language processing and has inspired innovative studies in a wide array of social-science disciplines. Yet, the use of topic modeling in computational social science has been hampered by two critical issues. first, social scientists tend to focus on a few standard ways of topic modeling. Our understanding of semantic patterns has not been informed by rapid methodological advances in topic modeling. Moreover, a systematic comparison of the performance of different methods in this field is warranted. Second, the choice of the optimal number of topics remains a challenging task. A comparison of topic-modeling techniques has rarely been situated in a social-science context and the choice appears to be arbitrary for most social scientists. based on about 120,000 Canadian newspaper articles since 1977, we review and compare eight traditional, generative, and neural methods for topic modeling (Latent Semantic Analysis, Principal Component Analysis, Factor Analysis, Non-negative Matrix Factorization, Latent Dirichlet Allocation, Neural Autoregressive Topic Model, Neural Variational Document Model, and Hierarchical Dirichlet Process). Three measures (coherence statistics, held-out likelihood, and graph-based dimensionality selection) are then used to assess the performance of these methods. Findings are presented and discussed to guide the choice of topic-modeling methods, especially in social science research. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Topic modeling natural language processing Computational social science Optimal number of topics

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Deep Attention Networks for Images and graphs

Deep Attention Networks for Images and Graphs

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作者： Wang, Zhengyang Texas A&M University

学位级别：Ph.D., Doctor of Philosophy

Deep learning has achieved great success in various machine learning areas, such as computer vision, natural language processing, and graph representation learning. While numerous deep neural networks (DNNs) have been proposed, the set of fundamental building blocks of DNNs remains small, including fully-connected layers, convolutions and recurrent units. Recently, the attention mechanism has shown promise in serving as a new kind of fundamental building blocks. Deep attention networks (DANs), i.e. DNNs that use the attention mechanism as a fundamental building block, have revolutionized the area of natural language processing. However, developing DANs for computer vision and graph representation learning applications is still challenging. Due to the intrinsic differences in data and applications, directly migrating DANs from textual data to images and graphs is usually either infeasible or ineffective. In this dissertation, we address this challenge by analyzing the functionality of the attention mechanism and exploring scenarios where DANs can push the limits of current DNNs. We propose several effective DANs for images and graphs. For images, we build DANs for a variety of image-to-image transformation applications by proposing powerful attention-based building blocks. first, we start the exploration through studying a common problem in dilated convolutions, which naturally results in the use of the attention mechanism. Dilated convolutions, a variant of convolutions, have been widely applied in deep convolutional neural networks (DCNNs) for image segmentation. However, dilated convolutions suffer from the gridding artifacts, which hampers the performance. We propose two simple yet effective degridding methods by studying a decomposition of dilated convolutions, and generalize them by defining separable and shared (SS) operators. Then we connect the SS operators with the attention mechanism and propose the SS output layer, which is able to smooth the entire DCNNs

关键词： Deep learning Attention mechanism Deep Attention Networks Machine Learning graph representation graph Neural Networks

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Utilizing deep learning and graph mining to identify drug use on Twitter data

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2020年第11-Sup期20卷 304-304页

作者： Tassone, Joseph Yan, Peizhi Simpson, Mackenzie Mendhe, Chetan Mago, Vijay Choudhury, Salimur Lakehead Univ Dept Comp Sci 955 Oliver Rd Thunder Bay P7B 5E1 ON Canada

Background: The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. methods: Social media data (tweets and attributes) were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset of 3,696,150 rows. The predictive classification power of multiple methods was compared including SVM, XGBoost, BERT and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. Results:To test the predictive capability of the model, SVM and XGBoost were first employed. The results calculated from the models respectively displayed an accuracy of 59.33% and 54.90%, with AUC's of 0.87 and 0.71. The values show a low predictive capability with little discrimination. Conversely, the CNN-based classifiers presented a significant improvement, between the two models tested. The first was trained with 2661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as "smoke", "cocaine", and "marijuana" triggering a drug-positive classification. Conclusion:Predictive analysis with a CNN is promising, whereas attribute-based models presented little predictive capability and were not suitable for analyzing text of data. This research found that the commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefuln

关键词： natural language processing Convolutional neural network BERT Twitter analysis Big data

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Enhancing Dialogue Symptom Diagnosis with Global Attention and Symptom graph 9

Enhancing Dialogue Symptom Diagnosis with Global Attention a...

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Conference on Empirical methods in natural language processing / 9th International Joint Conference on natural language processing (EMNLP-IJCNLP)

作者： Chen, Qin Lin, Xinzhu He, Xiahui Tou, Huaixiao Chen, Ting Wei, Zhongyu Fudan Univ Sch Data Sci Shanghai Peoples R China Alibaba Grp Hangzhou Peoples R China Ping An Technol Hangzhou Peoples R China

ISBN: (纸本)9781950737901

Symptom diagnosis is a challenging yet profound problem in natural language processing. Most previous research focus on investigating the standard electronic medical records for symptom diagnosis, while the dialogues between doctors and patients that contain more rich information are not well studied. In this paper, we first construct a dialogue symptom diagnosis dataset based on an online medical forum with a large amount of dialogues between patients and doctors. Then, we provide some benchmark models on this dataset to boost the research of dialogue symptom diagnosis. In order to further enhance the performance of symptom diagnosis over dialogues, we propose a global attention mechanism to capture more symptom related information, and build a symptom graph to model the associations between symptoms rather than treating each symptom independently. Experimental results show that both the global attention and symptom graph are effective to boost dialogue symptom diagnosis. In particular, our proposed model achieves the state-of-the-art performance on the constructed dataset.

关键词： Diagnosis

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DOVER-Lap: A Method for Combining Overlap-Aware Diarization Outputs

DOVER-Lap: A Method for Combining Overlap-Aware Diarization ...

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IEEE Spoken language Technology workshop

作者： Desh Raj Leibny Paola Garcia-Perera Zili Huang Shinji Watanabe Daniel Povey Andreas Stolcke Sanjeev Khudanpur Center for Language and Speech Processing The Johns Hopkins University Baltimore MD USA Human Language Technology Center of Excellence The Johns Hopkins University Baltimore MD USA Xiaomi Corp. Beijing China Amazon Alexa Speech Sunnyvale CA USA

ISBN: (数字)9781728170664

ISBN: (纸本)9781728170671

Several advances have been made recently towards handling overlapping speech for speaker diarization. Since speech and natural language tasks often benefit from ensemble techniques, we propose an algorithm for combining outputs from such diarization systems through majority voting. Our method, DOVER-Lap, is inspired from the recently proposed DOVER algorithm, but is designed to handle overlapping segments in diarization outputs. We also modify the pair-wise incremental label mapping strategy used in DOVER, and propose an approximation algorithm based on weighted k-partite graph matching, which performs this mapping using a global cost tensor. We demonstrate the strength of our method by combining outputs from diverse systems - clustering-based, region proposal networks, and target-speaker voice activity detection - on AMI and LibriCSS datasets, where it consistently outperforms the single best system. Additionally, we show that DOVER-Lap can be used for late fusion in multichannel diarization, and compares favorably with early fusion methods like beamforming.

关键词： Voice activity detection Tensors natural languages Diversity reception Approximation algorithms Proposals Task analysis

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