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  • Item Recommendation from Implicit Feedback
    arXiv.cs.IR Pub Date : 2021-01-21
    Steffen Rendle

    The task of item recommendation is to select the best items for a user from a large catalogue of items. Item recommenders are commonly trained from implicit feedback which consists of past actions that are positive only. Core challenges of item recommendation are (1) how to formulate a training objective from implicit feedback and (2) how to efficiently train models over a large item catalogue. This

    更新日期:2021-01-22
  • Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline
    arXiv.cs.IR Pub Date : 2021-01-21
    Luyu Gao; Zhuyun Dai; Jamie Callan

    Pre-trained deep language models~(LM) have advanced the state-of-the-art of text retrieval. Rerankers fine-tuned from deep LM estimates candidate relevance based on rich contextualized matching signals. Meanwhile, deep LMs can also be leveraged to improve search index, building retrievers with better recall. One would expect a straightforward combination of both in a pipeline to have additive performance

    更新日期:2021-01-22
  • Joint Autoregressive and Graph Models for Software and Developer Social Networks
    arXiv.cs.IR Pub Date : 2021-01-21
    Rima Hazra; Hardik Aggarwal; Pawan Goyal; Animesh Mukherjee; Soumen Chakrabarti

    Social network research has focused on hyperlink graphs, bibliographic citations, friend/follow patterns, influence spread, etc. Large software repositories also form a highly valuable networked artifact, usually in the form of a collection of packages, their developers, dependencies among them, and bug reports. This "social network of code" is rarely studied by social network researchers. We introduce

    更新日期:2021-01-22
  • Assessing the Benefits of Model Ensembles in Neural Re-Ranking for Passage Retrieval
    arXiv.cs.IR Pub Date : 2021-01-21
    Luís Borges; Bruno Martins; Jamie Callan

    Our work aimed at experimentally assessing the benefits of model ensembling within the context of neural methods for passage reranking. Starting from relatively standard neural models, we use a previous technique named Fast Geometric Ensembling to generate multiple model instances from particular training schedules, then focusing or attention on different types of approaches for combining the results

    更新日期:2021-01-22
  • Fast Clustering of Short Text Streams Using Efficient Cluster Indexing and Dynamic Similarity Thresholds
    arXiv.cs.IR Pub Date : 2021-01-21
    Md Rashadul Hasan Rakib; Muhammad Asaduzzaman

    Short text stream clustering is an important but challenging task since massive amount of text is generated from different sources such as micro-blogging, question-answering, and social news aggregation websites. One of the major challenges of clustering such massive amount of text is to cluster them within a reasonable amount of time. The existing state-of-the-art short text stream clustering methods

    更新日期:2021-01-22
  • Templates of generic geographic information for answering where-questions
    arXiv.cs.IR Pub Date : 2021-01-21
    Ehsan Hamzei; Stephan Winter; Martin Tomko

    In everyday communication, where-questions are answered by place descriptions. To answer where-questions automatically, computers should be able to generate relevant place descriptions that satisfy inquirers' information needs. Human-generated answers to where-questions constructed based on a few anchor places that characterize the location of inquired places. The challenge for automatically generating

    更新日期:2021-01-22
  • Explainable Patterns: Going from Findings to Insights to Support Data Analytics Democratization
    arXiv.cs.IR Pub Date : 2021-01-19
    Leonardo Christino; Martha D. Ferreira; Asal Jalilvand; Fernando V. Paulovich

    In the past decades, massive efforts involving companies, non-profit organizations, governments, and others have been put into supporting the concept of data democratization, promoting initiatives to educate people to confront information with data. Although this represents one of the most critical advances in our free world, access to data without concrete facts to check or the lack of an expert to

    更新日期:2021-01-22
  • Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval
    arXiv.cs.IR Pub Date : 2021-01-21
    Robert Litschko; Ivan Vulić; Simone Paolo Ponzetto; Goran Glavaš

    Pretrained multilingual text encoders based on neural Transformer architectures, such as multilingual BERT (mBERT) and XLM, have achieved strong performance on a myriad of language understanding tasks. Consequently, they have been adopted as a go-to paradigm for multilingual and cross-lingual representation learning and transfer, rendering cross-lingual word embeddings (CLWEs) effectively obsolete

    更新日期:2021-01-22
  • What is all this new MeSH about? Exploring the semantic provenance of new descriptors in the MeSH thesaurus
    arXiv.cs.IR Pub Date : 2021-01-20
    Anastasios Nentidis; Anastasia Krithara; Grigorios Tsoumakas; Georgios Paliouras

    The Medical Subject Headings (MeSH) thesaurus is a controlled vocabulary widely used in biomedical knowledge systems, particularly for semantic indexing of scientific literature. As the MeSH hierarchy evolves through annual version updates, some new descriptors are introduced that were not previously available. This paper explores the conceptual provenance of these new descriptors. In particular, we

    更新日期:2021-01-22
  • Open-Domain Conversational Search Assistant with Transformers
    arXiv.cs.IR Pub Date : 2021-01-20
    Rafael Ferreira; Mariana Leite; David Semedo; Joao Magalhaes

    Open-domain conversational search assistants aim at answering user questions about open topics in a conversational manner. In this paper we show how the Transformer architecture achieves state-of-the-art results in key IR tasks, leveraging the creation of conversational assistants that engage in open-domain conversational search with single, yet informative, answers. In particular, we propose an open-domain

    更新日期:2021-01-21
  • PGT: Pseudo Relevance Feedback Using a Graph-Based Transformer
    arXiv.cs.IR Pub Date : 2021-01-20
    HongChien Yu; Zhuyun Dai; Jamie Callan

    Most research on pseudo relevance feedback (PRF) has been done in vector space and probabilistic retrieval models. This paper shows that Transformer-based rerankers can also benefit from the extra context that PRF provides. It presents PGT, a graph-based Transformer that sparsifies attention between graph nodes to enable PRF while avoiding the high computational complexity of most Transformer architectures

    更新日期:2021-01-21
  • Chronological Citation Recommendation with Time Preference
    arXiv.cs.IR Pub Date : 2021-01-19
    Shutian Ma; Heng Zhang; Chengzhi Zhang; Xiaozhong Liu

    Citation recommendation is an important task to assist scholars in finding candidate literature to cite. Traditional studies focus on static models of recommending citations, which do not explicitly distinguish differences between papers that are caused by temporal variations. Although, some researchers have investigated chronological citation recommendation by adding time related function or modeling

    更新日期:2021-01-20
  • Density-Ratio Based Personalised Ranking from Implicit Feedback
    arXiv.cs.IR Pub Date : 2021-01-19
    Riku Togashi; Masahiro Kato; Mayu Otani; Shin'ichi Satoh

    Learning from implicit user feedback is challenging as we can only observe positive samples but never access negative ones. Most conventional methods cope with this issue by adopting a pairwise ranking approach with negative sampling. However, the pairwise ranking approach has a severe disadvantage in the convergence time owing to the quadratically increasing computational cost with respect to the

    更新日期:2021-01-20
  • A Comparison of Question Rewriting Methods for Conversational Passage Retrieval
    arXiv.cs.IR Pub Date : 2021-01-19
    Svitlana Vakulenko; Nikos Voskarides; Zhucheng Tu; Shayne Longpre

    Conversational passage retrieval relies on question rewriting to modify the original question so that it no longer depends on the conversation history. Several methods for question rewriting have recently been proposed, but they were compared under different retrieval pipelines. We bridge this gap by thoroughly evaluating those question rewriting methods on the TREC CAsT 2019 and 2020 datasets under

    更新日期:2021-01-20
  • Learnable Embedding Sizes for Recommender Systems
    arXiv.cs.IR Pub Date : 2021-01-19
    Siyi Liu; Chen Gao; Yihong Chen; Depeng Jin; Yong Li

    The embedding-based representation learning is commonly used in deep learning recommendation models to map the raw sparse features to dense vectors. The traditional embedding manner that assigns a uniform size to all features has two issues. First, the numerous features inevitably lead to a gigantic embedding table that causes a high memory usage cost. Second, it is likely to cause the over-fitting

    更新日期:2021-01-20
  • Classification of Pedagogical content using conventional machine learning and deep learning model
    arXiv.cs.IR Pub Date : 2021-01-18
    Vedat Apuk; Krenare Pireva Nuçi

    The advent of the Internet and a large number of digital technologies has brought with it many different challenges. A large amount of data is found on the web, which in most cases is unstructured and unorganized, and this contributes to the fact that the use and manipulation of this data is quite a difficult process. Due to this fact, the usage of different machine and deep learning techniques for

    更新日期:2021-01-20
  • Tip of the Tongue Known-Item Retrieval: A Case Study in Movie Identification
    arXiv.cs.IR Pub Date : 2021-01-18
    Jaime Arguello; Adam Ferguson; Emery Fine; Bhaskar Mitra; Hamed Zamani; Fernando Diaz

    While current information retrieval systems are effective for known-item retrieval where the searcher provides a precise name or identifier for the item being sought, systems tend to be much less effective for cases where the searcher is unable to express a precise name or identifier. We refer to this as tip of the tongue (TOT) known-item retrieval, named after the cognitive state of not being able

    更新日期:2021-01-19
  • Studying Catastrophic Forgetting in Neural Ranking Models
    arXiv.cs.IR Pub Date : 2021-01-18
    Jesus Lovon-Melgarejo; Laure Soulier; Karen Pinel-Sauvagnat; Lynda Tamine

    Several deep neural ranking models have been proposed in the recent IR literature. While their transferability to one target domain held by a dataset has been widely addressed using traditional domain adaptation strategies, the question of their cross-domain transferability is still under-studied. We study here in what extent neural ranking models catastrophically forget old knowledge acquired from

    更新日期:2021-01-19
  • Mitigating the Position Bias of Transformer Models in Passage Re-Ranking
    arXiv.cs.IR Pub Date : 2021-01-18
    Sebastian Hofstätter; Aldo Lipani; Sophia Althammer; Markus Zlabinger; Allan Hanbury

    Supervised machine learning models and their evaluation strongly depends on the quality of the underlying dataset. When we search for a relevant piece of information it may appear anywhere in a given passage. However, we observe a bias in the position of the correct answer in the text in two popular Question Answering datasets used for passage re-ranking. The excessive favoring of earlier positions

    更新日期:2021-01-19
  • Robustness of Meta Matrix Factorization Against Strict Privacy Constraints
    arXiv.cs.IR Pub Date : 2021-01-18
    Peter Müllner; Dominik Kowald; Elisabeth Lex

    In this paper, we explore the reproducibility of MetaMF, a meta matrix factorization framework introduced by Lin et al. MetaMF employs meta learning for federated rating prediction to preserve users' privacy. We reproduce the experiments of Lin et al. on five datasets, i.e., Douban, Hetrec-MovieLens, MovieLens 1M, Ciao, and Jester. Also, we study the impact of meta learning on the accuracy of MetaMF's

    更新日期:2021-01-19
  • ExpFinder: An Ensemble Expert Finding Model Integrating $N$-gram Vector Space Model and $μ$CO-HITS
    arXiv.cs.IR Pub Date : 2021-01-18
    Yong-Bin KangFellow, IEEE; Hung DuFellow, IEEE; Abdur Rahim Mohammad ForkanFellow, IEEE; Prem Prakash JayaramanFellow, IEEE; Amir AryaniFellow, IEEE; Timos SellisFellow, IEEE

    Finding an expert plays a crucial role in driving successful collaborations and speeding up high-quality research development and innovations. However, the rapid growth of scientific publications and digital expertise data makes identifying the right experts a challenging problem. Existing approaches for finding experts given a topic can be categorised into information retrieval techniques based on

    更新日期:2021-01-19
  • Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation
    arXiv.cs.IR Pub Date : 2021-01-16
    Junliang Yu; Hongzhi Yin; Jundong Li; Qinyong Wang; Nguyen Quoc Viet Hung; Xiangliang Zhang

    Social relations are often used to improve recommendation quality and most existing social recommendation models exploit pairwise relations to mine potential user preferences. However, real-life interactions among users are very complicated and user relations can be high-order. Hypergraph provides a natural way to model complex high-order relations, while its potential for social recommendation is

    更新日期:2021-01-19
  • A Survey on Extraction of Causal Relations from Natural Language Text
    arXiv.cs.IR Pub Date : 2021-01-16
    Jie Yang; Soyeon Caren Han; Josiah Poon

    As an essential component of human cognition, cause-effect relations appear frequently in text, and curating cause-effect relations from text helps in building causal networks for predictive tasks. Existing causality extraction techniques include knowledge-based, statistical machine learning(ML)-based, and deep learning-based approaches. Each method has its advantages and weaknesses. For example, knowledge-based

    更新日期:2021-01-19
  • A Zero Attentive Relevance Matching Networkfor Review Modeling in Recommendation System
    arXiv.cs.IR Pub Date : 2021-01-16
    Hansi Zeng; Zhichao Xu; Qingyao Ai

    User and item reviews are valuable for the construction of recommender systems. In general, existing review-based methods for recommendation can be broadly categorized into two groups: the siamese models that build static user and item representations from their reviews respectively, and the interaction-based models that encode user and item dynamically according to the similarity or relationships

    更新日期:2021-01-19
  • Controlling the Risk of Conversational Search via Reinforcement Learning
    arXiv.cs.IR Pub Date : 2021-01-15
    Zhenduo Wang; Qingyao Ai

    Users often formulate their search queries with immature language without well-developed keywords and complete structures. Such queries fail to express their true information needs and raise ambiguity as fragmental language often yield various interpretations and aspects. This gives search engines a hard time processing and understanding the query, and eventually leads to unsatisfactory retrieval results

    更新日期:2021-01-19
  • Reinforcement learning based recommender systems: A survey
    arXiv.cs.IR Pub Date : 2021-01-15
    M. Mehdi Afsar; Trafford Crump; Behrouz Far

    Recommender systems (RSs) are becoming an inseparable part of our everyday lives. They help us find our favorite items to purchase, our friends on social networks, and our favorite movies to watch. Traditionally, the recommendation problem was considered as a simple classification or prediction problem; however, the sequential nature of the recommendation problem has been shown. Accordingly, it can

    更新日期:2021-01-19
  • AMALGAM: A Matching Approach to fairfy tabuLar data with knowledGe grAph Model
    arXiv.cs.IR Pub Date : 2021-01-17
    Rabia Azzi; Gayo Diallo

    In this paper we present AMALGAM, a matching approach to fairify tabular data with the use of a knowledge graph. The ultimate goal is to provide fast and efficient approach to annotate tabular data with entities from a background knowledge. The approach combines lookup and filtering services combined with text pre-processing techniques. Experiments conducted in the context of the 2020 Semantic Web

    更新日期:2021-01-19
  • Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation
    arXiv.cs.IR Pub Date : 2021-01-16
    Hamed Jelodar; Rita Orji; Stan Matwin; Swarna Weerasinghe; Oladapo Oyebode; Yongli Wang

    Taking advantage of social media platforms, such as Twitter, this paper provides an effective framework for emotion detection among those who are quarantined. Early detection of emotional feelings and their trends help implement timely intervention strategies. Given the limitations of medical diagnosis of early emotional change signs during the quarantine period, artificial intelligence models provide

    更新日期:2021-01-19
  • Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching
    arXiv.cs.IR Pub Date : 2021-01-16
    Liang Pang; Yanyan Lan; Xueqi Cheng

    Semantic text matching models have been widely used in community question answering, information retrieval, and dialogue. However, these models cannot well address the long-form text matching problem. That is because there are usually many noises in the setting of long-form text matching, and it is difficult for existing semantic text matching to capture the key matching signals from this noisy information

    更新日期:2021-01-19
  • Annotation of epidemiological information in animal disease-related news articles: guidelines
    arXiv.cs.IR Pub Date : 2021-01-15
    Sarah Valentin; Elena Arsevska; Aline Vilain; Valérie De Waele; Renaud Lancelot; Mathieu Roche

    This paper describes a method for annotation of epidemiological information in animal disease-related news articles. The annotation guidelines are generic and aim to embrace all animal or zoonotic infectious diseases, regardless of the pathogen involved or its way of transmission (e.g. vector-borne, airborne, by contact). The framework relies on the successive annotation of all the sentences from a

    更新日期:2021-01-18
  • Operationalizing Framing to Support MultiperspectiveRecommendations of Opinion Pieces
    arXiv.cs.IR Pub Date : 2021-01-15
    Mats Mulder; Oana Inel; Jasper Oosterman; Nava Tintarev

    Diversity in personalized news recommender systems is often defined as dissimilarity, and based on topic diversity (e.g., corona versus farmers strike). Diversity in news media, however, is understood as multiperspectivity (e.g., different opinions on corona measures), and arguably a key responsibility of the press in a democratic society. While viewpoint diversity is often considered synonymous with

    更新日期:2021-01-18
  • Ensemble Learning Based Classification Algorithm Recommendation
    arXiv.cs.IR Pub Date : 2021-01-15
    Guangtao Wang; Qinbao Song; Xiaoyan Zhu

    Recommending appropriate algorithms to a classification problem is one of the most challenging issues in the field of data mining. The existing algorithm recommendation models are generally constructed on only one kind of meta-features by single learners. Considering that i) ensemble learners usually show better performance and ii) different kinds of meta-features characterize the classification problems

    更新日期:2021-01-18
  • Reviving Purpose Limitation and Data Minimisation in Personalisation, Profiling and Decision-Making Systems
    arXiv.cs.IR Pub Date : 2021-01-15
    Michèle Finck; Asia Biega

    This paper determines, through an interdisciplinary law and computer science lens, whether data minimisation and purpose limitation can be meaningfully implemented in data-driven algorithmic systems, including personalisation, profiling and decision-making systems. Our analysis reveals that the two legal principles continue to play an important role in mitigating the risks of personal data processing

    更新日期:2021-01-18
  • The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models
    arXiv.cs.IR Pub Date : 2021-01-14
    Ronak Pradeep; Rodrigo Nogueira; Jimmy Lin

    We propose a design pattern for tackling text ranking problems, dubbed "Expando-Mono-Duo", that has been empirically validated for a number of ad hoc retrieval tasks in different domains. At the core, our design relies on pretrained sequence-to-sequence models within a standard multi-stage ranking architecture. "Expando" refers to the use of document expansion techniques to enrich keyword representations

    更新日期:2021-01-15
  • $C^3DRec$: Cloud-Client Cooperative Deep Learning for Temporal Recommendation in the Post-GDPR Era
    arXiv.cs.IR Pub Date : 2021-01-13
    Jialiang Han; Yun Ma

    Mobile devices enable users to retrieve information at any time and any place. Considering the occasional requirements and fragmentation usage pattern of mobile users, temporal recommendation techniques are proposed to improve the efficiency of information retrieval on mobile devices by means of accurately recommending items via learning temporal interests with short-term user interaction behaviors

    更新日期:2021-01-15
  • Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twitter
    arXiv.cs.IR Pub Date : 2021-01-09
    Sarah Alqurashi; Btool Hamoui; Abdulaziz Alashaikh; Ahmad Alhindi; Eisa Alanazi

    The rapid growth of social media content during the current pandemic provides useful tools for disseminating information which has also become a root for misinformation. Therefore, there is an urgent need for fact-checking and effective techniques for detecting misinformation in social media. In this work, we study the misinformation in the Arabic content of Twitter. We construct a large Arabic dataset

    更新日期:2021-01-15
  • TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation
    arXiv.cs.IR Pub Date : 2021-01-12
    Guangneng Hu; Qiang Yang

    We investigate how to solve the cross-corpus news recommendation for unseen users in the future. This is a problem where traditional content-based recommendation techniques often fail. Luckily, in real-world recommendation services, some publisher (e.g., Daily news) may have accumulated a large corpus with lots of consumers which can be used for a newly deployed publisher (e.g., Political news). To

    更新日期:2021-01-15
  • Learning Student Interest Trajectory for MOOCThread Recommendation
    arXiv.cs.IR Pub Date : 2021-01-10
    Shalini Pandey; Andrew Lan; George Karypis; Jaideep Srivastava

    In recent years, Massive Open Online Courses (MOOCs) have witnessed immense growth in popularity. Now, due to the recent Covid19 pandemic situation, it is important to push the limits of online education. Discussion forums are primary means of interaction among learners and instructors. However, with growing class size, students face the challenge of finding useful and informative discussion forums

    更新日期:2021-01-15
  • Analysis of E-commerce Ranking Signals via Signal Temporal Logic
    arXiv.cs.IR Pub Date : 2021-01-14
    Tommaso DreossiAmazon Search; Giorgio BallardinAmazon Search; Parth GuptaAmazon Search; Jan BakusAmazon Search; Yu-Hsiang LinAmazon Search; Vamsi SalakaAmazon Search

    The timed position of documents retrieved by learning to rank models can be seen as signals. Signals carry useful information such as drop or rise of documents over time or user behaviors. In this work, we propose to use the logic formalism called Signal Temporal Logic (STL) to characterize document behaviors in ranking accordingly to the specified formulas. Our analysis shows that interesting document

    更新日期:2021-01-15
  • Knowledge-Enhanced Top-K Recommendation in Poincaré Ball
    arXiv.cs.IR Pub Date : 2021-01-13
    Chen Ma; Liheng Ma; Yingxue Zhang; Haolun Wu; Xue Liu; Mark Coates

    Personalized recommender systems are increasingly important as more content and services become available and users struggle to identify what might interest them. Thanks to the ability for providing rich information, knowledge graphs (KGs) are being incorporated to enhance the recommendation performance and interpretability. To effectively make use of the knowledge graph, we propose a recommendation

    更新日期:2021-01-14
  • Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search
    arXiv.cs.IR Pub Date : 2021-01-13
    Ziyang Liu; Zhaomeng Cheng; Yunjiang Jiang; Yue Shang; Wei Xiong; Sulong Xu; Bo Long; Di Jin

    Result relevance prediction is an essential task of e-commerce search engines to boost the utility of search engines and ensure smooth user experience. The last few years eyewitnessed a flurry of research on the use of Transformer-style models and deep text-match models to improve relevance. However, these two types of models ignored the inherent bipartite network structures that are ubiquitous in

    更新日期:2021-01-14
  • Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation
    arXiv.cs.IR Pub Date : 2021-01-13
    Chen Ma; Liheng Ma; Yingxue Zhang; Ruiming Tang; Xue Liu; Mark Coates

    Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them. Although matrix factorization and deep learning based methods have proved effective in user preference modeling, they violate the triangle inequality and fail to capture fine-grained preference information. To tackle this

    更新日期:2021-01-14
  • Discrete Knowledge Graph Embedding based on Discrete Optimization
    arXiv.cs.IR Pub Date : 2021-01-13
    Yunqi Li; Shuyuan Xu; Bo Liu; Zuohui Fu; Shuchang Liu; Xu Chen; Yongfeng Zhang

    This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method, which projects KG entities and relations into the Hamming space based on a computationally tractable discrete optimization algorithm, to solve the formidable storage and computation cost challenges in traditional continuous graph embedding methods. The convergence of DKGE can be guaranteed theoretically. Extensive experiments

    更新日期:2021-01-14
  • Distributed storage algorithms with optimal tradeoffs
    arXiv.cs.IR Pub Date : 2021-01-13
    Michael Luby; Thomas Richardson

    One of the primary objectives of a distributed storage system is to reliably store large amounts of source data for long durations using a large number $N$ of unreliable storage nodes, each with $c$ bits of storage capacity. Storage nodes fail randomly over time and are replaced with nodes of equal capacity initialized to zeroes, and thus bits are erased at some rate $e$. To maintain recoverability

    更新日期:2021-01-14
  • LaDiff ULMFiT: A Layer Differentiated training approach for ULMFiT
    arXiv.cs.IR Pub Date : 2021-01-13
    Mohammed Azhan; Mohammad Ahmad

    In our paper, we present Deep Learning models with a layer differentiated training method which were used for the SHARED TASK@ CONSTRAINT 2021 sub-tasks COVID19 Fake News Detection in English and Hostile Post Detection in Hindi. We propose a Layer Differentiated training procedure for training a pre-trained ULMFiT arXiv:1801.06146 model. We used special tokens to annotate specific parts of the tweets

    更新日期:2021-01-14
  • On the Calibration and Uncertainty of Neural Learning to Rank Models
    arXiv.cs.IR Pub Date : 2021-01-12
    Gustavo Penha; Claudia Hauff

    According to the Probability Ranking Principle (PRP), ranking documents in decreasing order of their probability of relevance leads to an optimal document ranking for ad-hoc retrieval. The PRP holds when two conditions are met: [C1] the models are well calibrated, and, [C2] the probabilities of relevance are reported with certainty. We know however that deep neural networks (DNNs) are often not well

    更新日期:2021-01-13
  • Neural News Recommendation with Negative Feedback
    arXiv.cs.IR Pub Date : 2021-01-12
    Chuhan Wu; Fangzhao Wu; Yongfeng Huang; Xing Xie

    News recommendation is important for online news services. Precise user interest modeling is critical for personalized news recommendation. Existing news recommendation methods usually rely on the implicit feedback of users like news clicks to model user interest. However, news click may not necessarily reflect user interests because users may click a news due to the attraction of its title but feel

    更新日期:2021-01-13
  • AI- and HPC-enabled Lead Generation for SARS-CoV-2: Models and Processes to Extract Druglike Molecules Contained in Natural Language Text
    arXiv.cs.IR Pub Date : 2021-01-12
    Zhi Hong; J. Gregory Pauloski; Logan Ward; Kyle Chard; Ben Blaiszik; Ian Foster

    Researchers worldwide are seeking to repurpose existing drugs or discover new drugs to counter the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A promising source of candidates for such studies is molecules that have been reported in the scientific literature to be drug-like in the context of coronavirus research. We report here on a project that leverages both human

    更新日期:2021-01-13
  • Toward Effective Automated Content Analysis via Crowdsourcing
    arXiv.cs.IR Pub Date : 2021-01-12
    Jiele Wu; Chau-Wai Wong; Xinyan Zhao; Xianpeng Liu

    Many computer scientists use the aggregated answers of online workers to represent ground truth. Prior work has shown that aggregation methods such as majority voting are effective for measuring relatively objective features. For subjective features such as semantic connotation, online workers, known for optimizing their hourly earnings, tend to deteriorate in the quality of their responses as they

    更新日期:2021-01-13
  • Measuring Recommender System Effects with Simulated Users
    arXiv.cs.IR Pub Date : 2021-01-12
    Sirui Yao; Yoni Halpern; Nithum Thain; Xuezhi Wang; Kang Lee; Flavien Prost; Ed H. Chi; Jilin Chen; Alex Beutel

    Imagine a food recommender system -- how would we check if it is \emph{causing} and fostering unhealthy eating habits or merely reflecting users' interests? How much of a user's experience over time with a recommender is caused by the recommender system's choices and biases, and how much is based on the user's preferences and biases? Popularity bias and filter bubbles are two of the most well-studied

    更新日期:2021-01-13
  • Locality Sensitive Hashing for Efficient Similar Polygon Retrieval
    arXiv.cs.IR Pub Date : 2021-01-12
    Haim Kaplan; Jay Tenenbaum

    Locality Sensitive Hashing (LSH) is an effective method of indexing a set of items to support efficient nearest neighbors queries in high-dimensional spaces. The basic idea of LSH is that similar items should produce hash collisions with higher probability than dissimilar items. We study LSH for (not necessarily convex) polygons, and use it to give efficient data structures for similar shape retrieval

    更新日期:2021-01-13
  • Quantum Mathematics in Artificial Intelligence
    arXiv.cs.IR Pub Date : 2021-01-12
    Dominic Widdows; Kirsty Kitto; Trevor Cohen

    In the decade since 2010, successes in artificial intelligence have been at the forefront of computer science and technology, and vector space models have solidified a position at the forefront of artificial intelligence. At the same time, quantum computers have become much more powerful, and announcements of major advances are frequently in the news. The mathematical techniques underlying both these

    更新日期:2021-01-13
  • Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction
    arXiv.cs.IR Pub Date : 2021-01-11
    Yanqiao Zhu; Yichen Xu; Feng Yu; Qiang Liu; Shu Wu; Liang Wang

    Click-through rate (CTR) prediction, which aims to predict the probability that whether of a user will click on an item, is an essential task for many online applications. Due to the nature of data sparsity and high dimensionality in CTR prediction, a key to making effective prediction is to model high-order feature interactions among feature fields. To explicitly model high-order feature interactions

    更新日期:2021-01-12
  • Transfer Learning and Augmentation for Word Sense Disambiguation
    arXiv.cs.IR Pub Date : 2021-01-10
    Harsh Kohli

    Many downstream NLP tasks have shown significant improvement through continual pre-training, transfer learning and multi-task learning. State-of-the-art approaches in Word Sense Disambiguation today benefit from some of these approaches in conjunction with information sources such as semantic relationships and gloss definitions contained within WordNet. Our work builds upon these systems and uses data

    更新日期:2021-01-12
  • Towards Long-term Fairness in Recommendation
    arXiv.cs.IR Pub Date : 2021-01-10
    Yingqiang Ge; Shuchang Liu; Ruoyuan Gao; Yikun Xian; Yunqi Li; Xiangyu Zhao; Changhua Pei; Fei Sun; Junfeng Ge; Wenwu Ou; Yongfeng Zhang

    As Recommender Systems (RS) influence more and more people in their daily life, the issue of fairness in recommendation is becoming more and more important. Most of the prior approaches to fairness-aware recommendation have been situated in a static or one-shot setting, where the protected groups of items are fixed, and the model provides a one-time fairness solution based on fairness-constrained optimization

    更新日期:2021-01-12
  • Context-Aware Target Apps Selection and Recommendation for Enhancing Personal Mobile Assistants
    arXiv.cs.IR Pub Date : 2021-01-09
    Mohammad Aliannejadi; Hamed Zamani; Fabio Crestani; W. Bruce Croft

    Users install many apps on their smartphones, raising issues related to information overload for users and resource management for devices. Moreover, the recent increase in the use of personal assistants has made mobile devices even more pervasive in users' lives. This paper addresses two research problems that are vital for developing effective personal mobile assistants: target apps selection and

    更新日期:2021-01-12
  • Generate Natural Language Explanations for Recommendation
    arXiv.cs.IR Pub Date : 2021-01-09
    Hanxiong Chen; Xu Chen; Shaoyun Shi; Yongfeng Zhang

    Providing personalized explanations for recommendations can help users to understand the underlying insight of the recommendation results, which is helpful to the effectiveness, transparency, persuasiveness and trustworthiness of recommender systems. Current explainable recommendation models mostly generate textual explanations based on pre-defined sentence templates. However, the expressiveness power

    更新日期:2021-01-12
  • Selection of Optimal Parameters in the Fast K-Word Proximity Search Based on Multi-component Key Indexes
    arXiv.cs.IR Pub Date : 2021-01-09
    Alexander B. Veretennikov

    Proximity full-text search is commonly implemented in contemporary full-text search systems. Let us assume that the search query is a list of words. It is natural to consider a document as relevant if the queried words are near each other in the document. The proximity factor is even more significant for the case where the query consists of frequently occurring words. Proximity full-text search requires

    更新日期:2021-01-12
  • An Unsupervised Normalization Algorithm for Noisy Text: A Case Study for Information Retrieval and Stance Detection
    arXiv.cs.IR Pub Date : 2021-01-09
    Anurag Roy; Shalmoli Ghosh; Kripabandhu Ghosh; Saptarshi Ghosh

    A large fraction of textual data available today contains various types of 'noise', such as OCR noise in digitized documents, noise due to informal writing style of users on microblogging sites, and so on. To enable tasks such as search/retrieval and classification over all the available data, we need robust algorithms for text normalization, i.e., for cleaning different kinds of noise in the text

    更新日期:2021-01-12
  • Evaluating Deep Learning Approaches for Covid19 Fake News Detection
    arXiv.cs.IR Pub Date : 2021-01-11
    Apurva Wani; Isha Joshi; Snehal Khandve; Vedangi Wagh; Raviraj Joshi

    Social media platforms like Facebook, Twitter, and Instagram have enabled connection and communication on a large scale. It has revolutionized the rate at which information is shared and enhanced its reach. However, another side of the coin dictates an alarming story. These platforms have led to an increase in the creation and spread of fake news. The fake news has not only influenced people in the

    更新日期:2021-01-12
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