Word embeddings have been widely used and proven to be effective in many natural language processing and text modeling tasks. It is obvious that one ambiguous word could have very different semantics in various contexts, which is called polysemy. Most existing works aim at generating only one single embedding for each word while a few works build a limited number of embeddings to present different meanings for each word. However, it is hard to determine the exact number of senses for each word as the word meaning is dependent on contexts. To address this problem, we propose a novel Adaptive Probabilistic Word Embedding (APWE) model, where the word polysemy is defined over a latent interpretable semantic space. Specifically, at first each word is represented by an embedding in the latent semantic space and then based on the proposed APWE model, the word embedding can be adaptively adjusted and updated based on different contexts to obtain the tailored word embedding. Empirical comparisons with state-of-the-art models demonstrate the superiority of the proposed APWE model.
{"title":"Adaptive Probabilistic Word Embedding","authors":"Shuangyin Li, Yu Zhang, Rong Pan, Kaixiang Mo","doi":"10.1145/3366423.3380147","DOIUrl":"https://doi.org/10.1145/3366423.3380147","url":null,"abstract":"Word embeddings have been widely used and proven to be effective in many natural language processing and text modeling tasks. It is obvious that one ambiguous word could have very different semantics in various contexts, which is called polysemy. Most existing works aim at generating only one single embedding for each word while a few works build a limited number of embeddings to present different meanings for each word. However, it is hard to determine the exact number of senses for each word as the word meaning is dependent on contexts. To address this problem, we propose a novel Adaptive Probabilistic Word Embedding (APWE) model, where the word polysemy is defined over a latent interpretable semantic space. Specifically, at first each word is represented by an embedding in the latent semantic space and then based on the proposed APWE model, the word embedding can be adaptively adjusted and updated based on different contexts to obtain the tailored word embedding. Empirical comparisons with state-of-the-art models demonstrate the superiority of the proposed APWE model.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82217739","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}
Understanding how people change their views during multiparty argumentative discussions is important in applications that involve human communication, e.g., in social media and education. Existing research focuses on lexical features of individual comments, dynamics of discussions, or the personalities of participants but deemphasizes the cumulative influence of the interplay of comments by different participants on a participant’s mindset. We address the task of predicting the points where a user’s view changes given an entire discussion, thereby tackling the confusion due to multiple plausible alternatives when considering the entirety of a discussion. We make the following contributions. (1) Through a human study, we show that modeling a user’s perception of comments is crucial in predicting persuasiveness. (2) We present a sequential model for cumulative influence that captures the interplay between comments as both local and nonlocal dependencies, and demonstrate its capability of selecting the most effective information for changing views. (3) We identify contextual and interactive features and propose sequence structures to incorporate these features. Our empirical evaluation using a Reddit Change My View dataset shows that contextual and interactive features are valuable in predicting view changes, and a sequential model notably outperforms the nonsequential baseline models.
了解人们在多方辩论中如何改变他们的观点在涉及人类交流的应用中是很重要的，例如在社交媒体和教育中。现有的研究侧重于个体评论的词汇特征、讨论的动态或参与者的个性，但不强调不同参与者的评论相互作用对参与者心态的累积影响。我们解决了在整个讨论中预测用户观点变化的点的任务，从而解决了在考虑整个讨论时由于多个似是而非的替代方案而造成的混乱。我们做出以下贡献。(1)通过一项人类研究，我们表明建模用户对评论的感知对于预测说服力至关重要。(2)我们提出了一个累积影响的顺序模型，该模型捕捉了评论之间作为本地和非本地依赖关系的相互作用，并证明了其选择最有效信息以改变观点的能力。(3)我们识别了上下文和交互特征，并提出了包含这些特征的序列结构。我们使用Reddit Change My View数据集进行的实证评估表明，上下文和交互特征在预测视图变化方面是有价值的，并且顺序模型明显优于非顺序基线模型。
{"title":"In Opinion Holders’ Shoes: Modeling Cumulative Influence for View Change in Online Argumentation","authors":"Zhen Guo, Zhe Zhang, Munindar P. Singh","doi":"10.1145/3366423.3380302","DOIUrl":"https://doi.org/10.1145/3366423.3380302","url":null,"abstract":"Understanding how people change their views during multiparty argumentative discussions is important in applications that involve human communication, e.g., in social media and education. Existing research focuses on lexical features of individual comments, dynamics of discussions, or the personalities of participants but deemphasizes the cumulative influence of the interplay of comments by different participants on a participant’s mindset. We address the task of predicting the points where a user’s view changes given an entire discussion, thereby tackling the confusion due to multiple plausible alternatives when considering the entirety of a discussion. We make the following contributions. (1) Through a human study, we show that modeling a user’s perception of comments is crucial in predicting persuasiveness. (2) We present a sequential model for cumulative influence that captures the interplay between comments as both local and nonlocal dependencies, and demonstrate its capability of selecting the most effective information for changing views. (3) We identify contextual and interactive features and propose sequence structures to incorporate these features. Our empirical evaluation using a Reddit Change My View dataset shows that contextual and interactive features are valuable in predicting view changes, and a sequential model notably outperforms the nonsequential baseline models.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82578050","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}
How can we assess a network’s ability to maintain its functionality under attacks? Network robustness has been studied extensively in the case of deterministic networks. However, applications such as online information diffusion and the behavior of networked public raise a question of robustness in probabilistic networks. We propose three novel robustness measures for networks hosting a diffusion under the Independent Cascade (IC) model, susceptible to node attacks. The outcome of such a process depends on the selection of its initiators, or seeds, by the seeder, as well as on two factors outside the seeder’s discretion: the attack strategy and the probabilistic diffusion outcome. We consider three levels of seeder awareness regarding these two uncontrolled factors, and evaluate the network’s viability aggregated over all possible extents of node attacks. We introduce novel algorithms from building blocks found in previous works to evaluate the proposed measures. A thorough experimental study with synthetic and real, scale-free and homogeneous networks establishes that these algorithms are effective and efficient, while the proposed measures highlight differences among networks in terms of robustness and the surprise they furnish when attacked. Last, we devise a new measure of diffusion entropy that can inform the design of probabilistically robust networks.
{"title":"On the Robustness of Cascade Diffusion under Node Attacks","authors":"Alvis Logins, Yuchen Li, Panagiotis Karras","doi":"10.1145/3366423.3380028","DOIUrl":"https://doi.org/10.1145/3366423.3380028","url":null,"abstract":"How can we assess a network’s ability to maintain its functionality under attacks? Network robustness has been studied extensively in the case of deterministic networks. However, applications such as online information diffusion and the behavior of networked public raise a question of robustness in probabilistic networks. We propose three novel robustness measures for networks hosting a diffusion under the Independent Cascade (IC) model, susceptible to node attacks. The outcome of such a process depends on the selection of its initiators, or seeds, by the seeder, as well as on two factors outside the seeder’s discretion: the attack strategy and the probabilistic diffusion outcome. We consider three levels of seeder awareness regarding these two uncontrolled factors, and evaluate the network’s viability aggregated over all possible extents of node attacks. We introduce novel algorithms from building blocks found in previous works to evaluate the proposed measures. A thorough experimental study with synthetic and real, scale-free and homogeneous networks establishes that these algorithms are effective and efficient, while the proposed measures highlight differences among networks in terms of robustness and the surprise they furnish when attacked. Last, we devise a new measure of diffusion entropy that can inform the design of probabilistically robust networks.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89904291","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}
In this paper we study the limiting dynamics of a sequential process that generalizes Pólya’s urn. This process has been studied also in the context of language generation, discrete choice, repeat consumption, and models for the web graph. The process we study generates future items by copying from past items. It is parameterized by a sequence of weights describing how much to prefer copying from recent versus more distant locations. We show that, if the weight sequence follows a power law with exponent α ∈ [0, 1), then the sequences generated by the model tend toward a limiting behavior in which the eventual frequency of each token in the alphabet attains a limit. Moreover, in the case α > 2, we show that the sequence converges to a token being chosen infinitely often, and each other token being chosen only constantly many times.
{"title":"Asymptotic Behavior of Sequence Models","authors":"Flavio Chierichetti, Ravi Kumar, A. Tomkins","doi":"10.1145/3366423.3380044","DOIUrl":"https://doi.org/10.1145/3366423.3380044","url":null,"abstract":"In this paper we study the limiting dynamics of a sequential process that generalizes Pólya’s urn. This process has been studied also in the context of language generation, discrete choice, repeat consumption, and models for the web graph. The process we study generates future items by copying from past items. It is parameterized by a sequence of weights describing how much to prefer copying from recent versus more distant locations. We show that, if the weight sequence follows a power law with exponent α ∈ [0, 1), then the sequences generated by the model tend toward a limiting behavior in which the eventual frequency of each token in the alphabet attains a limit. Moreover, in the case α > 2, we show that the sequence converges to a token being chosen infinitely often, and each other token being chosen only constantly many times.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89966420","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}
Amin Javari, Tyler Derr, Pouya Esmailian, Jiliang Tang, K. Chang
In real-world networks, nodes might have more than one type of relationship. Signed networks are an important class of such networks consisting of two types of relations: positive and negative. Recently, embedding signed networks has attracted increasing attention and is more challenging than classic networks since nodes are connected by paths with multi-types of links. Existing works capture the complex relationships by relying on social theories. However, this approach has major drawbacks, including the incompleteness/inaccurateness of such theories. Thus, we propose network transformation based embedding to address these shortcomings. The core idea is that rather than directly finding the similarities of two nodes from the complex paths connecting them, we can obtain their similarities through simple paths connecting their different roles. We employ this idea to build our proposed embedding technique that can be described in three steps: (1) the input directed signed network is transformed into an unsigned bipartite network with each node mapped to a set of nodes we denote as role-nodes. Each role-node captures a certain role that a node in the original network plays; (2) the network of role-nodes is embedded; and (3) the original network is encoded by aggregating the embedding vectors of role-nodes. Our experiments show the novel proposed technique substantially outperforms existing models.
{"title":"ROSE: Role-based Signed Network Embedding","authors":"Amin Javari, Tyler Derr, Pouya Esmailian, Jiliang Tang, K. Chang","doi":"10.1145/3366423.3380038","DOIUrl":"https://doi.org/10.1145/3366423.3380038","url":null,"abstract":"In real-world networks, nodes might have more than one type of relationship. Signed networks are an important class of such networks consisting of two types of relations: positive and negative. Recently, embedding signed networks has attracted increasing attention and is more challenging than classic networks since nodes are connected by paths with multi-types of links. Existing works capture the complex relationships by relying on social theories. However, this approach has major drawbacks, including the incompleteness/inaccurateness of such theories. Thus, we propose network transformation based embedding to address these shortcomings. The core idea is that rather than directly finding the similarities of two nodes from the complex paths connecting them, we can obtain their similarities through simple paths connecting their different roles. We employ this idea to build our proposed embedding technique that can be described in three steps: (1) the input directed signed network is transformed into an unsigned bipartite network with each node mapped to a set of nodes we denote as role-nodes. Each role-node captures a certain role that a node in the original network plays; (2) the network of role-nodes is embedded; and (3) the original network is encoded by aggregating the embedding vectors of role-nodes. Our experiments show the novel proposed technique substantially outperforms existing models.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86559587","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}
Syed Suleman Ahmad, Muhammad Daniyal Dar, Muhammad Fareed Zaffar, N. Vallina-Rodriguez, Rishab Nithyanand
Data generated by web crawlers has formed the basis for much of our current understanding of the Internet. However, not all crawlers are created equal and crawlers generally find themselves trading off between computational overhead, developer effort, data accuracy, and completeness. Therefore, the choice of crawler has a critical impact on the data generated and knowledge inferred from it. In this paper, we conduct a systematic study of the trade-offs presented by different crawlers and the impact that these can have on various types of measurement studies. We make the following contributions: First, we conduct a survey of all research published since 2015 in the premier security and Internet measurement venues to identify and verify the repeatability of crawling methodologies deployed for different problem domains and publication venues. Next, we conduct a qualitative evaluation of a subset of all crawling tools identified in our survey. This evaluation allows us to draw conclusions about the suitability of each tool for specific types of data gathering. Finally, we present a methodology and a measurement framework to empirically highlight the differences between crawlers and how the choice of crawler can impact our understanding of the web.
{"title":"Apophanies or Epiphanies? How Crawlers Impact Our Understanding of the Web","authors":"Syed Suleman Ahmad, Muhammad Daniyal Dar, Muhammad Fareed Zaffar, N. Vallina-Rodriguez, Rishab Nithyanand","doi":"10.1145/3366423.3380113","DOIUrl":"https://doi.org/10.1145/3366423.3380113","url":null,"abstract":"Data generated by web crawlers has formed the basis for much of our current understanding of the Internet. However, not all crawlers are created equal and crawlers generally find themselves trading off between computational overhead, developer effort, data accuracy, and completeness. Therefore, the choice of crawler has a critical impact on the data generated and knowledge inferred from it. In this paper, we conduct a systematic study of the trade-offs presented by different crawlers and the impact that these can have on various types of measurement studies. We make the following contributions: First, we conduct a survey of all research published since 2015 in the premier security and Internet measurement venues to identify and verify the repeatability of crawling methodologies deployed for different problem domains and publication venues. Next, we conduct a qualitative evaluation of a subset of all crawling tools identified in our survey. This evaluation allows us to draw conclusions about the suitability of each tool for specific types of data gathering. Finally, we present a methodology and a measurement framework to empirically highlight the differences between crawlers and how the choice of crawler can impact our understanding of the web.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87521507","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}
Data often contains sensitive information, which poses a major obstacle to publishing it. Some suggest to obfuscate the data or only releasing some data statistics. These approaches have, however, been shown to provide insufficient safeguards against de-anonymisation. Recently, differential privacy (DP), an approach that injects noise into the query answers to provide statistical privacy guarantees, has emerged as a solution to release sensitive data. This study investigates how to continuously release privacy-preserving histograms (or distributions) from online streams of sensitive data by combining DP and semantic web technologies. We focus on distributions, as they are the basis for many analytic applications. Specifically, we propose SihlQL, a query language that processes RDF streams in a privacy-preserving fashion. SihlQL builds on top of SPARQL and the w-event DP framework. We show how some peculiarities of w-event privacy constrain the expressiveness of SihlQL queries. Addressing these constraints, we propose an extension of w-event privacy that provides answers to a larger class of queries while preserving their privacy. To evaluate SihlQL, we implemented a prototype engine that compiles queries to Apache Flink topologies and studied its privacy properties using real-world data from an IPTV provider and an online e-commerce web site.
{"title":"Differentially Private Stream Processing for the Semantic Web","authors":"Daniele Dell'Aglio, A. Bernstein","doi":"10.1145/3366423.3380265","DOIUrl":"https://doi.org/10.1145/3366423.3380265","url":null,"abstract":"Data often contains sensitive information, which poses a major obstacle to publishing it. Some suggest to obfuscate the data or only releasing some data statistics. These approaches have, however, been shown to provide insufficient safeguards against de-anonymisation. Recently, differential privacy (DP), an approach that injects noise into the query answers to provide statistical privacy guarantees, has emerged as a solution to release sensitive data. This study investigates how to continuously release privacy-preserving histograms (or distributions) from online streams of sensitive data by combining DP and semantic web technologies. We focus on distributions, as they are the basis for many analytic applications. Specifically, we propose SihlQL, a query language that processes RDF streams in a privacy-preserving fashion. SihlQL builds on top of SPARQL and the w-event DP framework. We show how some peculiarities of w-event privacy constrain the expressiveness of SihlQL queries. Addressing these constraints, we propose an extension of w-event privacy that provides answers to a larger class of queries while preserving their privacy. To evaluate SihlQL, we implemented a prototype engine that compiles queries to Apache Flink topologies and studied its privacy properties using real-world data from an IPTV provider and an online e-commerce web site.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"121 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77440630","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}
P. Analytis, D. Barkoczi, Philipp Lorenz-Spreen, Stefan M. Herzog
People’s ability to influence others’ opinion on matters of taste varies greatly—both offline and in recommender systems. What are the mechanisms underlying these striking differences? Using the weighted k-nearest neighbors algorithm (k-nn) to represent an array of social learning strategies, we show—leveraging methods from network science—how the k-nn algorithm gives rise to networks of social influence in six real-world domains of taste. We show three novel results that apply both to offline advice taking and online recommender settings. First, influential individuals have mainstream tastes and high dispersion in their taste similarity with others. Second, the fewer people an individual or algorithm consults (i.e., the lower k is) or the larger the weight placed on the opinions of more similar others, the smaller the group of people with substantial influence. Third, the influence networks emerging from deploying the k-nn algorithm are hierarchically organized. Our results shed new light on classic empirical findings in communication and network science and can help improve the understanding of social influence offline and online.
{"title":"The Structure of Social Influence in Recommender Networks","authors":"P. Analytis, D. Barkoczi, Philipp Lorenz-Spreen, Stefan M. Herzog","doi":"10.1145/3366423.3380020","DOIUrl":"https://doi.org/10.1145/3366423.3380020","url":null,"abstract":"People’s ability to influence others’ opinion on matters of taste varies greatly—both offline and in recommender systems. What are the mechanisms underlying these striking differences? Using the weighted k-nearest neighbors algorithm (k-nn) to represent an array of social learning strategies, we show—leveraging methods from network science—how the k-nn algorithm gives rise to networks of social influence in six real-world domains of taste. We show three novel results that apply both to offline advice taking and online recommender settings. First, influential individuals have mainstream tastes and high dispersion in their taste similarity with others. Second, the fewer people an individual or algorithm consults (i.e., the lower k is) or the larger the weight placed on the opinions of more similar others, the smaller the group of people with substantial influence. Third, the influence networks emerging from deploying the k-nn algorithm are hierarchically organized. Our results shed new light on classic empirical findings in communication and network science and can help improve the understanding of social influence offline and online.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89197786","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}
Zhijing Wu, Jiaxin Mao, Yiqun Liu, Jingtao Zhan, Yukun Zheng, Min Zhang, Shaoping Ma
Document ranking is one of the most studied but challenging problems in information retrieval (IR) research. A number of existing document ranking models capture relevance signals at the whole document level. Recently, more and more research has begun to address this problem from fine-grained document modeling. Several works leveraged fine-grained passage-level relevance signals in ranking models. However, most of these works focus on context-independent passage-level relevance signals and ignore the context information, which may lead to inaccurate estimation of passage-level relevance. In this paper, we investigate how information gain accumulates with passages when users sequentially read a document. We propose the context-aware Passage-level Cumulative Gain (PCG), which aggregates relevance scores of passages and avoids the need to formally split a document into independent passages. Next, we incorporate the patterns of PCG into a BERT-based sequential model called Passage-level Cumulative Gain Model (PCGM) to predict the PCG sequence. Finally, we apply PCGM to the document ranking task. Experimental results on two public ad hoc retrieval benchmark datasets show that PCGM outperforms most existing ranking models and also indicates the effectiveness of PCG signals. We believe that this work contributes to improving ranking performance and providing more explainability for document ranking.
{"title":"Leveraging Passage-level Cumulative Gain for Document Ranking","authors":"Zhijing Wu, Jiaxin Mao, Yiqun Liu, Jingtao Zhan, Yukun Zheng, Min Zhang, Shaoping Ma","doi":"10.1145/3366423.3380305","DOIUrl":"https://doi.org/10.1145/3366423.3380305","url":null,"abstract":"Document ranking is one of the most studied but challenging problems in information retrieval (IR) research. A number of existing document ranking models capture relevance signals at the whole document level. Recently, more and more research has begun to address this problem from fine-grained document modeling. Several works leveraged fine-grained passage-level relevance signals in ranking models. However, most of these works focus on context-independent passage-level relevance signals and ignore the context information, which may lead to inaccurate estimation of passage-level relevance. In this paper, we investigate how information gain accumulates with passages when users sequentially read a document. We propose the context-aware Passage-level Cumulative Gain (PCG), which aggregates relevance scores of passages and avoids the need to formally split a document into independent passages. Next, we incorporate the patterns of PCG into a BERT-based sequential model called Passage-level Cumulative Gain Model (PCGM) to predict the PCG sequence. Finally, we apply PCGM to the document ranking task. Experimental results on two public ad hoc retrieval benchmark datasets show that PCGM outperforms most existing ranking models and also indicates the effectiveness of PCG signals. We believe that this work contributes to improving ranking performance and providing more explainability for document ranking.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87705009","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}
Social network user location inference technology has been widely used in various geospatial applications like public health monitoring and local advertising recommendation. Due to insufficient consideration of relationships between users and location indicative words, most of existing inference methods estimate label propagation probabilities solely based on statistical features, resulting in large location inference error. In this paper, a Twitter user location inference method based on representation learning and label propagation is proposed. Firstly, the heterogeneous connection relation graph is constructed based on relationships between Twitter users and relationships between users and location indicative words, and relationships unrelated to geographic attributes are filtered. Then, vector representations of users are learnt from the connection relation graph. Finally, label propagation probabilities between adjacent users are calculated based on vector representations, and the locations of unknown users are predicted through iterative label propagation. Experiments on two representative Twitter datasets - GeoText and TwUs, show that the proposed method can accurately calculate label propagation probabilities based on vector representations and improve the accuracy of location inference. Compared with existing typical Twitter user location inference methods - GCN and MLP-TXT+NET, the median error distance of the proposed method is reduced by 18% and 16%, respectively.
{"title":"Twitter User Location Inference Based on Representation Learning and Label Propagation","authors":"Hechan Tian, Meng Zhang, Xiangyang Luo, Fenlin Liu, Yaqiong Qiao","doi":"10.1145/3366423.3380019","DOIUrl":"https://doi.org/10.1145/3366423.3380019","url":null,"abstract":"Social network user location inference technology has been widely used in various geospatial applications like public health monitoring and local advertising recommendation. Due to insufficient consideration of relationships between users and location indicative words, most of existing inference methods estimate label propagation probabilities solely based on statistical features, resulting in large location inference error. In this paper, a Twitter user location inference method based on representation learning and label propagation is proposed. Firstly, the heterogeneous connection relation graph is constructed based on relationships between Twitter users and relationships between users and location indicative words, and relationships unrelated to geographic attributes are filtered. Then, vector representations of users are learnt from the connection relation graph. Finally, label propagation probabilities between adjacent users are calculated based on vector representations, and the locations of unknown users are predicted through iterative label propagation. Experiments on two representative Twitter datasets - GeoText and TwUs, show that the proposed method can accurately calculate label propagation probabilities based on vector representations and improve the accuracy of location inference. Compared with existing typical Twitter user location inference methods - GCN and MLP-TXT+NET, the median error distance of the proposed method is reduced by 18% and 16%, respectively.","PeriodicalId":20754,"journal":{"name":"Proceedings of The Web Conference 2020","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85412856","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}
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