The ubiquitous availability of mobile devices with GPS capabilities and the popularity of social media platforms have created a rich source for textual data with spatio-temporal information. Also, other domains like crime incident description and search engine queries, can provide spatio-temporal textual data. These data sources can be used to discover space-time related insights of human behavior. This work focuses on modeling text that is associated with a particular time and place. We extend the traditional language modeling task from natural language processing to language modeling under spatio-temporal conditions. This task definition allows us to use the same evaluation framework used in language modeling. A model for spatio-temporal text data representation should be able to capture the patterns that guide how text is generated in a spatio-temporal context. We aim to develop neural network models for language modeling conditioned on spatio-temporal variables with the ability to capture properties such as: neighborhood, periodicity and hierarchy.
{"title":"Spatio-temporal Conditioned Language Models","authors":"Juglar Diaz","doi":"10.1145/3397271.3401450","DOIUrl":"https://doi.org/10.1145/3397271.3401450","url":null,"abstract":"The ubiquitous availability of mobile devices with GPS capabilities and the popularity of social media platforms have created a rich source for textual data with spatio-temporal information. Also, other domains like crime incident description and search engine queries, can provide spatio-temporal textual data. These data sources can be used to discover space-time related insights of human behavior. This work focuses on modeling text that is associated with a particular time and place. We extend the traditional language modeling task from natural language processing to language modeling under spatio-temporal conditions. This task definition allows us to use the same evaluation framework used in language modeling. A model for spatio-temporal text data representation should be able to capture the patterns that guide how text is generated in a spatio-temporal context. We aim to develop neural network models for language modeling conditioned on spatio-temporal variables with the ability to capture properties such as: neighborhood, periodicity and hierarchy.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127780140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zheng Gao, Hongsong Li, Zhuoren Jiang, Xiaozhong Liu
Cyberspace hosts abundant interactions between users and different kinds of objects, and their relations are often encapsulated as bipartite graphs. Detecting user community in such heterogeneous graphs is an essential task to uncover user information needs and to further enhance recommendation performance. While several main cyber domains carrying high-quality graphs, unfortunately, most others can be quite sparse. However, as users may appear in multiple domains (graphs), their high-quality activities in the main domains can supply community detection in the sparse ones, e.g., user behaviors on Google can help thousands of applications to locate his/her local community when s/he uses Google ID to login those applications. In this paper, our model, Pairwise Cross-graph Community Detection (PCCD), is proposed to cope with the sparse graph problem by involving external graph knowledge to learn user pairwise community closeness instead of detecting direct communities. Particularly in our model, to avoid taking excessive propagated information, a two-level filtering module is utilized to select the most informative connections through both community and node level filters. Subsequently, a Community Recurrent Unit (CRU) is designed to estimate pairwise user community closeness. Extensive experiments on two real-world graph datasets validate our model against several strong alternatives. Supplementary experiments also validate its robustness on graphs with varied sparsity scales.
网络空间承载着用户与各种对象之间丰富的交互，它们之间的关系往往被封装为二部图。在这种异构图中检测用户社区是发现用户信息需求和进一步提高推荐性能的基本任务。虽然有几个主要的网络域携带高质量的图表，但不幸的是，大多数其他的网络域可能相当稀疏。然而，由于用户可能出现在多个域(图)中，他们在主域中的高质量活动可以在稀疏域中提供社区检测，例如，当用户使用Google ID登录这些应用程序时，用户在Google上的行为可以帮助成千上万的应用程序定位他/她的本地社区。在本文中，我们提出了PCCD (Pairwise Cross-graph Community Detection)模型来解决稀疏图问题，通过引入外部图知识来学习用户成对的社区亲密度，而不是直接检测社区。特别是在我们的模型中，为了避免获取过多的传播信息，我们使用了一个两级过滤模块，通过社区级和节点级过滤来选择信息量最大的连接。随后，设计了一个社区循环单元(CRU)来估计两两用户社区亲密度。在两个真实世界的图形数据集上进行的大量实验验证了我们的模型与几个强大的替代方案的对比。补充实验也验证了其对不同稀疏度尺度图的鲁棒性。
{"title":"Detecting User Community in Sparse Domain via Cross-Graph Pairwise Learning","authors":"Zheng Gao, Hongsong Li, Zhuoren Jiang, Xiaozhong Liu","doi":"10.1145/3397271.3401055","DOIUrl":"https://doi.org/10.1145/3397271.3401055","url":null,"abstract":"Cyberspace hosts abundant interactions between users and different kinds of objects, and their relations are often encapsulated as bipartite graphs. Detecting user community in such heterogeneous graphs is an essential task to uncover user information needs and to further enhance recommendation performance. While several main cyber domains carrying high-quality graphs, unfortunately, most others can be quite sparse. However, as users may appear in multiple domains (graphs), their high-quality activities in the main domains can supply community detection in the sparse ones, e.g., user behaviors on Google can help thousands of applications to locate his/her local community when s/he uses Google ID to login those applications. In this paper, our model, Pairwise Cross-graph Community Detection (PCCD), is proposed to cope with the sparse graph problem by involving external graph knowledge to learn user pairwise community closeness instead of detecting direct communities. Particularly in our model, to avoid taking excessive propagated information, a two-level filtering module is utilized to select the most informative connections through both community and node level filters. Subsequently, a Community Recurrent Unit (CRU) is designed to estimate pairwise user community closeness. Extensive experiments on two real-world graph datasets validate our model against several strong alternatives. Supplementary experiments also validate its robustness on graphs with varied sparsity scales.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125354213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
While great strides are made in the field of search and recommendation, there are still challenges and opportunities to address information access issues that involve solving tasks and accomplishing goals for a wide variety of users. Specifically, we lack intelligent systems that can detect not only the request an individual is making (what), but also understand and utilize the intention (why) and strategies (how) while providing information. Many scholars in the fields of information retrieval, recommender systems, productivity (especially in task management and time management), and artificial intelligence have recognized the importance of extracting and understanding people's tasks and the intentions behind performing those tasks in order to serve them better. However, we are still struggling to support them in task completion, e.g., in search and assistance, it has been challenging to move beyond single-query or single-turn interactions. The proliferation of intelligent agents has opened up new modalities for interacting with information, but these agents will need to be able to work more intelligently in understanding the context and helping the users at task level. This tutorial will introduce the attendees to the issues of detecting, understanding, and using task and task-related information in an information episode (with or without active searching). Specifically, it will cover several recent theories, models, and methods that show how to represent tasks and use behavioral data to extract task information. It will then show how this knowledge or model could contribute to addressing emerging retrieval and recommendation problems.
{"title":"Tutorial on Task-Based Search and Assistance","authors":"C. Shah, Ryen W. White","doi":"10.1145/3397271.3401422","DOIUrl":"https://doi.org/10.1145/3397271.3401422","url":null,"abstract":"While great strides are made in the field of search and recommendation, there are still challenges and opportunities to address information access issues that involve solving tasks and accomplishing goals for a wide variety of users. Specifically, we lack intelligent systems that can detect not only the request an individual is making (what), but also understand and utilize the intention (why) and strategies (how) while providing information. Many scholars in the fields of information retrieval, recommender systems, productivity (especially in task management and time management), and artificial intelligence have recognized the importance of extracting and understanding people's tasks and the intentions behind performing those tasks in order to serve them better. However, we are still struggling to support them in task completion, e.g., in search and assistance, it has been challenging to move beyond single-query or single-turn interactions. The proliferation of intelligent agents has opened up new modalities for interacting with information, but these agents will need to be able to work more intelligently in understanding the context and helping the users at task level. This tutorial will introduce the attendees to the issues of detecting, understanding, and using task and task-related information in an information episode (with or without active searching). Specifically, it will cover several recent theories, models, and methods that show how to represent tasks and use behavioral data to extract task information. It will then show how this knowledge or model could contribute to addressing emerging retrieval and recommendation problems.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"78 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126050453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Voice shopping using natural language introduces new challenges related to customer queries, like handling mispronounced, misexpressed, and misunderstood queries. Voice null queries, which result in no offers, have negative impact on customers shopping experience. Query rewriting (QR) attempts to automatically replace null queries with alternatives that lead to relevant results. We present a new approach for pre-retrieval QR of voice shopping null queries. Our proposed QR framework first generates alternative queries using a search index-based approach that targets different potential failures in voice queries. Then, a machine-learning component ranks these alternatives, and the original query is amended by the selected alternative. We provide an experimental evaluation of our approach based on data logs of a commercial voice assistant and an e-commerce website, demonstrating that it outperforms several baselines by more than $22%$. Our evaluation also highlights an interesting phenomenon, showing that web shopping null queries are considerably different, and apparently easier to fix, than voice queries. This further substantiates the use of specialized mechanisms for the voice domain. We believe that our proposed framework, mapping tail queries to head queries, is of independent interest since it can be extended and applied to other domains.
{"title":"Query Rewriting for Voice Shopping Null Queries","authors":"Iftah Gamzu, Marina Haikin, N. Halabi","doi":"10.1145/3397271.3401052","DOIUrl":"https://doi.org/10.1145/3397271.3401052","url":null,"abstract":"Voice shopping using natural language introduces new challenges related to customer queries, like handling mispronounced, misexpressed, and misunderstood queries. Voice null queries, which result in no offers, have negative impact on customers shopping experience. Query rewriting (QR) attempts to automatically replace null queries with alternatives that lead to relevant results. We present a new approach for pre-retrieval QR of voice shopping null queries. Our proposed QR framework first generates alternative queries using a search index-based approach that targets different potential failures in voice queries. Then, a machine-learning component ranks these alternatives, and the original query is amended by the selected alternative. We provide an experimental evaluation of our approach based on data logs of a commercial voice assistant and an e-commerce website, demonstrating that it outperforms several baselines by more than $22%$. Our evaluation also highlights an interesting phenomenon, showing that web shopping null queries are considerably different, and apparently easier to fix, than voice queries. This further substantiates the use of specialized mechanisms for the voice domain. We believe that our proposed framework, mapping tail queries to head queries, is of independent interest since it can be extended and applied to other domains.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126588178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hashing techniques have recently been successfully applied to solve similarity search problems in the information retrieval field because of their significantly reduced storage and high-speed search capabilities. However, the hash codes learned from most recent cross-modal hashing methods lack the ability to comprehensively preserve adequate information, resulting in a less than desirable performance. To solve this limitation, we propose a novel method termed Nonlinear Robust Discrete Hashing (NRDH), for cross-modal retrieval. The main idea behind NRDH is motivated by the success of neural networks, i.e., nonlinear descriptors, in the field of representation learning, and the use of nonlinear descriptors instead of simple linear transformations is more in line with the complex relationships that exist between common latent representation and heterogeneous multimedia data in the real world. In NRDH, we first learn a common latent representation through nonlinear descriptors to encode complementary and consistent information from the features of the heterogeneous multimedia data. Moreover, an asymmetric learning scheme is proposed to correlate the learned hash codes with the common latent representation. Empirically, we demonstrate that NRDH is able to successfully generate a comprehensive common latent representation that significantly improves the quality of the learned hash codes. Then, NRDH adopts a linear learning strategy to fast learn the hash function with the learned hash codes. Extensive experiments performed on two benchmark datasets highlight the superiority of NRDH over several state-of-the-art methods.
{"title":"Nonlinear Robust Discrete Hashing for Cross-Modal Retrieval","authors":"Zhan Yang, J. Long, Lei Zhu, Wenti Huang","doi":"10.1145/3397271.3401152","DOIUrl":"https://doi.org/10.1145/3397271.3401152","url":null,"abstract":"Hashing techniques have recently been successfully applied to solve similarity search problems in the information retrieval field because of their significantly reduced storage and high-speed search capabilities. However, the hash codes learned from most recent cross-modal hashing methods lack the ability to comprehensively preserve adequate information, resulting in a less than desirable performance. To solve this limitation, we propose a novel method termed Nonlinear Robust Discrete Hashing (NRDH), for cross-modal retrieval. The main idea behind NRDH is motivated by the success of neural networks, i.e., nonlinear descriptors, in the field of representation learning, and the use of nonlinear descriptors instead of simple linear transformations is more in line with the complex relationships that exist between common latent representation and heterogeneous multimedia data in the real world. In NRDH, we first learn a common latent representation through nonlinear descriptors to encode complementary and consistent information from the features of the heterogeneous multimedia data. Moreover, an asymmetric learning scheme is proposed to correlate the learned hash codes with the common latent representation. Empirically, we demonstrate that NRDH is able to successfully generate a comprehensive common latent representation that significantly improves the quality of the learned hash codes. Then, NRDH adopts a linear learning strategy to fast learn the hash function with the learned hash codes. Extensive experiments performed on two benchmark datasets highlight the superiority of NRDH over several state-of-the-art methods.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"42 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120995368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Knowledge graphs have been widely adopted to improve recommendation accuracy. The multi-hop user-item connections on knowledge graphs also endow reasoning about why an item is recommended. However, reasoning on paths is a complex combinatorial optimization problem. Traditional recommendation methods usually adopt brute-force methods to find feasible paths, which results in issues related to convergence and explainability. In this paper, we address these issues by better supervising the path finding process. The key idea is to extract imperfect path demonstrations with minimum labeling efforts and effectively leverage these demonstrations to guide path finding. In particular, we design a demonstration-based knowledge graph reasoning framework for explainable recommendation. We also propose an ADversarial Actor-Critic (ADAC) model for the demonstration-guided path finding. Experiments on three real-world benchmarks show that our method converges more quickly than the state-of-the-art baseline and achieves better recommendation accuracy and explainability.
{"title":"Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs","authors":"Kangzhi Zhao, Xiting Wang, Yuren Zhang, Li Zhao, Zheng Liu, Chunxiao Xing, Xing Xie","doi":"10.1145/3397271.3401171","DOIUrl":"https://doi.org/10.1145/3397271.3401171","url":null,"abstract":"Knowledge graphs have been widely adopted to improve recommendation accuracy. The multi-hop user-item connections on knowledge graphs also endow reasoning about why an item is recommended. However, reasoning on paths is a complex combinatorial optimization problem. Traditional recommendation methods usually adopt brute-force methods to find feasible paths, which results in issues related to convergence and explainability. In this paper, we address these issues by better supervising the path finding process. The key idea is to extract imperfect path demonstrations with minimum labeling efforts and effectively leverage these demonstrations to guide path finding. In particular, we design a demonstration-based knowledge graph reasoning framework for explainable recommendation. We also propose an ADversarial Actor-Critic (ADAC) model for the demonstration-guided path finding. Experiments on three real-world benchmarks show that our method converges more quickly than the state-of-the-art baseline and achieves better recommendation accuracy and explainability.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123068795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The most important unsolved problem with artificial neural networks is how to do unsupervised learning as effectively as the brain. There are currently two main approaches to unsupervised learning. In the first approach, exemplified by BERT and Variational Autoencoders, a deep neural network is used to reconstruct its input. This is problematic for images because the deepest layers of the network need to encode the fine details of the image. An alternative approach, introduced by Becker and Hinton in 1992, is to train two copies of a deep neural network to produce output vectors that have high mutual information when given two different crops of the same image as their inputs. This approach was designed to allow the representations to be untethered from irrelevant details of the input. The method of optimizing mutual information used by Becker and Hinton was flawed (for a subtle reason that I will explain) so Pacannaro and Hinton (2001) replaced it by a discriminative objective in which one vector representation must select a corresponding vector representation from among many alternatives. With faster hardware, contrastive learning of representations has recently become very popular and is proving to be very effective, but it suffers from a major flaw: To learn pairs of representation vectors that have N bits of mutual information we need to contrast the correct corresponding vector with about 2N incorrect alternatives. I will describe a novel and effective way of dealing with this limitation. I will also show that this leads to a simple way of implementing perceptual learning in cortex.
{"title":"The Next Generation of Neural Networks","authors":"Geoffrey E. Hinton","doi":"10.1145/3397271.3402425","DOIUrl":"https://doi.org/10.1145/3397271.3402425","url":null,"abstract":"The most important unsolved problem with artificial neural networks is how to do unsupervised learning as effectively as the brain. There are currently two main approaches to unsupervised learning. In the first approach, exemplified by BERT and Variational Autoencoders, a deep neural network is used to reconstruct its input. This is problematic for images because the deepest layers of the network need to encode the fine details of the image. An alternative approach, introduced by Becker and Hinton in 1992, is to train two copies of a deep neural network to produce output vectors that have high mutual information when given two different crops of the same image as their inputs. This approach was designed to allow the representations to be untethered from irrelevant details of the input. The method of optimizing mutual information used by Becker and Hinton was flawed (for a subtle reason that I will explain) so Pacannaro and Hinton (2001) replaced it by a discriminative objective in which one vector representation must select a corresponding vector representation from among many alternatives. With faster hardware, contrastive learning of representations has recently become very popular and is proving to be very effective, but it suffers from a major flaw: To learn pairs of representation vectors that have N bits of mutual information we need to contrast the correct corresponding vector with about 2N incorrect alternatives. I will describe a novel and effective way of dealing with this limitation. I will also show that this leads to a simple way of implementing perceptual learning in cortex.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127376582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Patients are increasingly using the web for understanding medical information, making health decisions, and validating physicians' advice. However, most of this content is tailored to an expert audience, due to which people with inadequate health literacy often find it difficult to access, comprehend, and act upon this information. Medical text simplification aims to alleviate this problem by computationally simplifying medical text. Most text simplification methods employ neural seq-to-seq models for this task. However, training such models requires a corpus of aligned complex and simple sentences. Creating such a dataset manually is effort intensive, while creating it automatically is prone to alignment errors. To overcome these challenges, we propose a denoising autoencoder based neural model for this task which leverages the simplistic writing style of medical social media text. Experiments on four datasets show that our method significantly outperforms the best known medical text simplification models across multiple automated and human evaluation metrics. Our model achieves an improvement of up to 16.52% over the existing best performing model on SARI which is the primary metric to evaluate text simplification models.
{"title":"Leveraging Social Media for Medical Text Simplification","authors":"Nikhil Pattisapu, Nishant Prabhu, Smriti Bhati, Vasudeva Varma","doi":"10.1145/3397271.3401105","DOIUrl":"https://doi.org/10.1145/3397271.3401105","url":null,"abstract":"Patients are increasingly using the web for understanding medical information, making health decisions, and validating physicians' advice. However, most of this content is tailored to an expert audience, due to which people with inadequate health literacy often find it difficult to access, comprehend, and act upon this information. Medical text simplification aims to alleviate this problem by computationally simplifying medical text. Most text simplification methods employ neural seq-to-seq models for this task. However, training such models requires a corpus of aligned complex and simple sentences. Creating such a dataset manually is effort intensive, while creating it automatically is prone to alignment errors. To overcome these challenges, we propose a denoising autoencoder based neural model for this task which leverages the simplistic writing style of medical social media text. Experiments on four datasets show that our method significantly outperforms the best known medical text simplification models across multiple automated and human evaluation metrics. Our model achieves an improvement of up to 16.52% over the existing best performing model on SARI which is the primary metric to evaluate text simplification models.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127883356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Question-answering sentiment analysis (QASA) is a novel but meaningful sentiment analysis task based on question-answering online reviews. Existing neural network-based models that conduct sentiment analysis of online reviews have already achieved great success. However, the syntax and implicitly semantic connection in the dependency tree have not been made full use of, especially for Chinese which has specific syntax. In this work, we propose a Residual-Duet Network leveraging textual and tree dependency information for Chinese question-answering sentiment analysis. In particular, we explore the synergies of graph embedding with structural dependency links to learn syntactic information. The transverse and longitudinal compression encoders are developed to capture sentiment evidence with disparate types of compression and different residual connections. We evaluate our model on three Chinese QASA datasets in different domains. Experimental results demonstrate the superiority of our proposed model in Chinese question-answering sentiment analysis.
{"title":"Residual-Duet Network with Tree Dependency Representation for Chinese Question-Answering Sentiment Analysis","authors":"Guangyi Hu, Chongyang Shi, Shufeng Hao, Yunru Bai","doi":"10.1145/3397271.3401226","DOIUrl":"https://doi.org/10.1145/3397271.3401226","url":null,"abstract":"Question-answering sentiment analysis (QASA) is a novel but meaningful sentiment analysis task based on question-answering online reviews. Existing neural network-based models that conduct sentiment analysis of online reviews have already achieved great success. However, the syntax and implicitly semantic connection in the dependency tree have not been made full use of, especially for Chinese which has specific syntax. In this work, we propose a Residual-Duet Network leveraging textual and tree dependency information for Chinese question-answering sentiment analysis. In particular, we explore the synergies of graph embedding with structural dependency links to learn syntactic information. The transverse and longitudinal compression encoders are developed to capture sentiment evidence with disparate types of compression and different residual connections. We evaluate our model on three Chinese QASA datasets in different domains. Experimental results demonstrate the superiority of our proposed model in Chinese question-answering sentiment analysis.","PeriodicalId":252050,"journal":{"name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129214909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Systematic reviews constitute the cornerstone of Evidence-based Medicine. They can provide guidance to medical policy-making by synthesizing all available studies regarding a certain topic. However, conducting systematic reviews has become a laborious and time-consuming task due to the large amount and rapid growth of published literature. The TAR approaches aim to accelerate the screening stage of systematic reviews by combining machine learning algorithms and human relevance feedback. In this work, we built an online active search system for systematic reviews, named APS, by applying an state-of-the-art TAR approach -- Continuous Active Learning. The system is built on the top of the PubMed collection, which is a widely used database of biomedical literature. It allows users to conduct the abstract screening for systematic reviews. We demonstrate the effectiveness and robustness of the APS in detecting relevant literature and reducing workload for systematic reviews using the CLEF TAR 2017 benchmark.
系统评价是循证医学的基石。他们可以通过综合有关某一主题的所有现有研究，为医疗决策提供指导。然而，由于已发表的文献数量庞大且增长迅速，进行系统评价已成为一项费力且耗时的任务。TAR方法旨在通过结合机器学习算法和人类相关性反馈来加速系统评论的筛选阶段。在这项工作中，我们通过应用最先进的TAR方法——持续主动学习，为系统评论建立了一个在线主动搜索系统，名为APS。该系统建立在PubMed collection的基础上，PubMed collection是一个广泛使用的生物医学文献数据库。它允许用户进行系统审查的抽象筛选。我们使用CLEF TAR 2017基准证明了APS在检测相关文献和减少系统评价工作量方面的有效性和稳健性。
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