commoncrawl_citations_annotated_2018.csv

cc_project_author,post_title,cc_project_url,cc_project_category,post_date,keywords,abstract,cc_author_affiliation,cc_class,cc_snippet,cc_dataset_used,cc_derived_dataset_about,cc_derived_dataset_used,cc_derived_dataset_cited
"Mostafa Abdou, Artur Kulmizev, Vinit Ravishankar, Lasha Abzianidze, Johan Bos – University of Groningen, The Netherlands; University of Copenhagen, Denmark; University of Oslo, Norway;",What can we learn from Semantic Tagging?,https://arxiv.org/abs/1808.09716,papers,20180101Z00:00:00,,,"University of Groningen, The Netherlands; University of Copenhagen, Denmark; University of Oslo, Norway;","nlp/semantics, nlp/word-embeddings, nlp/semantic-tagging",,,,GloVe-word-embeddings,
"Ameeta Agrawal, Aijun An, Manos Papagelis – York University, Toronto, Canada",Learning emotion-enriched word representations,https://www.aclweb.org/anthology/C18-1081,papers,20180101Z00:00:00,,"Most word representation learning methods are based on the distributional hypothesis in linguistics, according to which words that are used and occur in the same contexts tend to possess similar meanings. As a consequence, emotionally dissimilar words, such as “happy” and “sad” occurring in similar contexts would purport more similar meaning than emotionally similar words, such as “happy” and “joy”. This complication leads to rather undesirable outcome in predictive tasks that relate to affect (emotional state), such as emotion classification and emotion similarity. In order to address this limitation, we propose a novel method of obtaining emotion-enriched word representations, which projects emotionally similar words into neighboring spaces and emotionally dissimilar ones far apart. The proposed approach leverages distant supervision to automatically obtain a large training dataset of text documents and two recurrent neural network architectures for learning the emotion-enriched representations. Through extensive evaluation on two tasks, including emotion classification and emotion similarity, we demonstrate that the proposed representations outperform several competitive general-purpose and affective word representations.","York University, Toronto, Canada","nlp/word-embeddings, nlp/emotion-detection, nlp/sentiment-analysis",,,,GloVe-word-embeddings,
"Manar Alohaly, Hassan Takabi, Eduardo Blanco – University of North Texas, USA",A Deep Learning Approach for Extracting Attributes of ABAC Policies,http://doi.acm.org/10.1145/3205977.3205984,papers,20180101Z00:00:00,"access control policy, attribute-based access control, deep learning, natural language processing, policy authoring, relation extraction",,"University of North Texas, USA","nlp/machine-translation, computer-security/access-restrictions",,,,,
"Milad Alshomary, Michael Völske, Tristan Licht, Henning Wachsmuth, Benno Stein, Matthias Hagen, Martin Potthast – Paderborn University, Germany; Bauhaus-Universität Weimar, Germany; Martin-Luther-Universität Halle-Wittenberg, Germany; Leipzig University, Germany",Wikipedia text reuse: within and without,https://link.springer.com/chapter/10.1007/978-3-030-15712-8_49,papers,20180101Z00:00:00,,"We study text reuse related to Wikipedia at scale by compiling the first corpus of text reuse cases within Wikipedia as well as without (i.e., reuse of Wikipedia text in a sample of the Common Crawl). To discover reuse beyond verbatim copy and paste, we employ state-of-the-art text reuse detection technology, scaling it for the first time to process the entire Wikipedia as part of a distributed retrieval pipeline. We further report on a pilot analysis of the 100 million reuse cases inside, and the 1.6 million reuse cases outside Wikipedia that we discovered. Text reuse inside Wikipedia gives rise to new tasks such as article template induction, fixing quality flaws, or complementing Wikipedia’s ontology. Text reuse outside Wikipedia yields a tangible metric for the emerging field of quantifying Wikipedia’s influence on the web. To foster future research into these tasks, and for reproducibility’s sake, the Wikipedia text reuse corpus and the retrieval pipeline are made freely available.","Paderborn University, Germany; Bauhaus-Universität Weimar, Germany; Martin-Luther-Universität Halle-Wittenberg, Germany; Leipzig University, Germany","web-mining, ir/duplicate-detection","To foster research into Wikipedia textreuse, we compiled the first Wikipedia text reuse corpus, obtained from comparingthe entire Wikipedia to itself as well as to a 10\%-sample of the Common Crawl.",,,,
"Andrei Amatuni, Estelle He, Elika Bergelson – Duke University",Preserved Structure Across Vector Space Representations,https://arxiv.org/abs/1802.00840,papers,20180101Z00:00:00,,,Duke University,"nlp/semantics, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Khaled Ammar, Frank McSherry, Semih Salihoglu, Manas Joglekar – University of Waterloo, Canada; ETH Zürich, Switzerland; Google, Inc.",Distributed Evaluation of Subgraph Queries Using Worstcase Optimal LowMemory Dataflows,https://arxiv.org/pdf/1802.03760.pdf,papers,20180101Z00:00:00,,,"University of Waterloo, Canada; ETH Zürich, Switzerland; Google, Inc.",graph-processing,,,,WDC-hyperlinkgraph,
"Khaled Ammar, Frank McSherry, Semih Salihoglu, Manas Joglekar – University of Waterloo, Canada; ETH Zürich, Switzerland; Google, Inc.",Distributed evaluation of subgraph queries using worst-case optimal low-memory dataflows,https://dl.acm.org/citation.cfm?id=3199520,papers,20180101Z00:00:00,,,"University of Waterloo, Canada; ETH Zürich, Switzerland; Google, Inc.",graph-processing,,,,WDC-hyperlinkgraph,
"Rohan Anil, Gabriel Pereyra, Alexandre Passos, Robert Ormandi, George E. Dahl, Geoffrey E. Hinton – Google; Google Brain; Google DeepMind",Large scale distributed neural network training through online distillation,https://arxiv.org/abs/1804.03235,papers,20180101Z00:00:00,,,Google; Google Brain; Google DeepMind,nlp/neural-networks,,CC-MAIN-2017-26,,,
"Sajjad Arshad, Seyed Ali Mirheidari, Tobias Lauinger, Bruno Crispo, Engin Kirda, William Robertson – Northeastern University, Boston, MA, USA; University of Trento, Trento, Italy",Large-Scale Analysis of Style Injection by Relative Path Overwrite,https://doi.org/10.1145/3178876.3186090,papers,20180101Z00:00:00,"relative path overwrite, scriptless attack, style injection",,"Northeastern University, Boston, MA, USA; University of Trento, Trento, Italy","web-science, computer-security/web-application-security","We extract pages using relative-path stylesheets from the Common Crawl dataset [9], automatically test if style directives can be injected using RPO, and determine whether they are interpreted by the browser. [...] For finding the initial seed set of candidate pages with relative-path stylesheets, we leverage the Common Crawl from August 2016, which contains more than 1.6 billion pages. By using an existing dataset, we can quickly identify candidate pages without creating any web crawl traffic. We use a Java HTML parser to filter any pages containing only inline CSS or stylesheets referenced by absolute URLs, leaving us with over 203 million pages on nearly 6 million sites.",CC-MAIN-2016-36,,,
"Mikel Artetxe, Gorka Labaka, Eneko Agirre – University of the Basque Country, Spain",Generalizing and improving bilingual word embedding mappings with a multi-step framework of linear transformations,https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16935/16781,papers,20180101Z00:00:00,,,"University of the Basque Country, Spain","nlp/semantics, nlp/word-embeddings, nlp/bilingual-word-embeddings",,,,,
"Mikel Artetxe, Gorka Labaka, Eneko Agirre – University of the Basque Country, Spain",A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings,https://arxiv.org/abs/1805.06297,papers,20180101Z00:00:00,,,"University of the Basque Country, Spain","nlp/semantics, nlp/word-embeddings, nlp/bilingual-word-embeddings",,,,WMT-16-translation-task-common-crawl-corpus,
"Mikel Artetxe, Gorka Labaka, Iñigo Lopez-Gazpio, Eneko Agirre – University of the Basque Country, Spain",Uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrinsic evaluation,https://arxiv.org/abs/1809.02094,papers,20180101Z00:00:00,,,"University of the Basque Country, Spain","nlp/semantics, nlp/word-embeddings",,,,"GloVe-word-embeddings, fastText-word-embeddings",
"Mikel Artetxe, Holger Schwenk – University of the Basque Country, Spain; Facebook AI Research",Margin-based parallel corpus mining with multilingual sentence embeddings,https://arxiv.org/abs/1811.01136,papers,20180101Z00:00:00,,,"University of the Basque Country, Spain; Facebook AI Research","cc-cited-not-used, nlp/word-embeddings, nlp/sentence-embeddings, nlp/parallel-corpus",,,,,
"Parnia Bahar, Christopher Brix, Hermann Ney – RWTH Aachen University, Germany",Towards two-dimensional sequence to sequence model in neural machine translation,https://arxiv.org/abs/1810.03975,papers,20180101Z00:00:00,,,"RWTH Aachen University, Germany",nlp/machine-translation,,,,WMT-16-translation-task-common-crawl-corpus,
"Krisztian Balog – University of Stavanger, Norway",Entity-oriented search,https://link.springer.com/content/pdf/10.1007/978-3-319-93935-3.pdf,papers,20180101Z00:00:00,,,"University of Stavanger, Norway","information-retrieval, nlp/named-entity-recognition, linked data","Common CrawlCommon Crawl5is a nonprofit organization that regularly crawlsthe Web and makes the data publicly available. The datasets are hosted on AmazonS3 as part of the Amazon Public Datasets program.6As of May 2017, the crawlcontains 2.96 billion web pages and over 250 TB of uncompressed content (inWARC format). The Web Data Commons project7extracts structured data fromthe Common Crawl and makes those publicly available (e.g., the Hyperlink GraphDataset and the Web Table Corpus).",CC-MAIN-2017-22,,,
"Luciano Barbosa, Valter Crescenzi, Xin Luna Dong, Paolo Merialdo, Federico Piai, Disheng Qiu, Yanyan Shen, Divesh Srivastava – Universidade Federal de Pernambuco, Brazil; Roma Tre University, Italy; Amazon; Wanderio; Shanghai Jiao Tong University; AT&T Labs – Research",Big Data Integration for Product Specifications.,http://sites.computer.org/debull/A18june/A18JUN-CD.pdf#page=73,papers,20180101Z00:00:00,,,"Universidade Federal de Pernambuco, Brazil; Roma Tre University, Italy; Amazon; Wanderio; Shanghai Jiao Tong University; AT&T Labs – Research","ir/information-extraction, ir/data-integration","About 68\% of the sources discovered by our approach were not present in Common Crawl. Only 20\% of our sources contained fewer pages than the same sources in Common Crawl, and a very small fraction of the pages in these sources were product pages: on a sample set of 12 websites where Common Crawl presented more pages than in our dataset, we evaluated that only 0.8\% of the pages were product pages.",,,,
"Luciano Barbosa, Valter Crescenzi, Xin Luna Dong, Paolo Merialdo, Federico Piai, Disheng Qiu, Yanyan Shen, Divesh Srivastava – Universidade Federal de Pernambuco, Brazil; Roma Tre University, Italy; Amazon; Wanderio; Shanghai Jiao Tong University; AT&T Labs – Research",Lessons Learned and Research Agenda for Big Data Integration of Product Specifications (Discussion Paper),http://ceur-ws.org/Vol-2161/paper29.pdf,papers,20180101Z00:00:00,,,"Universidade Federal de Pernambuco, Brazil; Roma Tre University, Italy; Amazon; Wanderio; Shanghai Jiao Tong University; AT&T Labs – Research","ir/information-extraction, ir/data-integration","Building a Benchmark Product Dataset – We compared the contents of our dataset with pages in Common Crawl, an open repository of web crawl data. About 68\% of the sources discovered by our approach were not present in Common Crawl. Only 20\% of our sources contained fewer pages than the same sources in Common Crawl, and a very small fraction of the pages in these sources were product pages: on a sample set of 12 websites where Common Crawl presented more pages than in our dataset, we evaluated that only 0.8\% of the pages were product pages.",,,,
"Michail Batikas, Jörg Claussen, Christian Peukert – LMU Munich, Germany; UCP – Católica Lisbon School of Business and Economics, Lisboa, Portugal",Follow The Money: Online Piracy and Self-Regulation in the Advertising Industry,http://www.cesifo-group.de/DocDL/cesifo1_wp6852.pdf,papers,20180101Z00:00:00,,,"LMU Munich, Germany; UCP – Católica Lisbon School of Business and Economics, Lisboa, Portugal",web-science,"We obtain archived versions of the HTML source code of all URLs for each domain in our gross sample from Common Crawl, a project that has crawled billions of webpages periodically since summer 2013.",,,,
"Leilani Battle, Peitong Duan, Zachery Miranda, Dana Mukusheva, Remco Chang, Michael Stonebraker – University of Washington, Seattle, WA, USA; Massachusetts Institute of Technology, Cambridge, MA, USA; Tufts University, Medford, MA, USA",Beagle: Automated Extraction and Interpretation of Visualizations from the Web,https://dl.acm.org/citation.cfm?id=3174168,papers,20180101Z00:00:00,,"``How common is interactive visualization on the web?'' ``What is the most popular visualization design?'' ``How prevalent are pie charts really?'' These questions intimate the role of interactive visualization in the real (online) world. In this paper, we present our approach (and findings) to answering these questions. First, we introduce Beagle, which mines the web for SVG-based visualizations and automatically classifies them by type (i.e., bar, pie, etc.). With Beagle, we extract over 41,000 visualizations across five different tools and repositories, and classify them with 85\% accuracy, across 24 visualization types. Given this visualization collection, we study usage across tools. We find that most visualizations fall under four types: bar charts, line charts, scatter charts, and geographic maps. Though controversial, pie charts are relatively rare for the visualization tools that were studied. Our findings also suggest that the total visualization types supported by a given tool could factor into its ease of use. However this effect appears to be mitigated by providing a variety of diverse expert visualization examples to users.","University of Washington, Seattle, WA, USA; Massachusetts Institute of Technology, Cambridge, MA, USA; Tufts University, Medford, MA, USA","web-science, web-crawling","As found with other web crawling projects, such as the Common Crawl¹, our web crawls represent a specific point in time for the websites [...]",,,,
"Luigi Bellomarini, Ruslan R Fayzrakhmanov, Georg Gottlob, Andrey Kravchenko, Eleonora Laurenza, Yavor Nenov, Stephane Reissfelder, Emanuel Sallinger, Evgeny Sherkhonov, Lianlong Wu – University of Oxford, United Kingdom; Banca d’Italia, Italy; TU Wien, Austria",Data Science with Vadalog: Bridging Machine Learning and Reasoning,https://arxiv.org/abs/1807.08712,papers,20180101Z00:00:00,,,"University of Oxford, United Kingdom; Banca d’Italia, Italy; TU Wien, Austria","ai/semantic-reasoning, ai/machine-learning","Enterprises increasingly depend on intelligent information systems that operationalise corporate knowledge as a unified source across system boundaries. [...] To maintain their competitive edge, companies need to incorporate multiple heterogeneous sources of information, including [...] external streams of unstructured data (e.g., news and social media feeds, and Common Crawl¹), [...]",,,,
"Luisa Bentivogli, Mauro Cettolo, Marcello Federico, Federmann Christian – FBK, Trento, Italy; Amazon AI, East Palo Alto, CA, USA, Microsoft Cloud+AI, Redmond, WA, USA",Machine Translation Human Evaluation: an investigation of evaluation based on Post-Editing and its relation with Direct Assessment,https://workshop2018.iwslt.org/downloads/Proceedings_IWSLT_2018.pdf#page=77,papers,20180101Z00:00:00,,,"FBK, Trento, Italy; Amazon AI, East Palo Alto, CA, USA, Microsoft Cloud+AI, Redmond, WA, USA",nlp/machine-translation,,,,WMT-16-translation-task-common-crawl-corpus,
"Janek Bevendorff, Benno Stein, Matthias Hagen, Martin Potthast – Bauhaus-Universität Weimar, Germany; Leipzig University, Germany",Elastic ChatNoir: Search Engine for the ClueWeb and the Common Crawl,https://doi.org/10.1007/978-3-319-76941-7_83,papers,20180101Z00:00:00,,,"Bauhaus-Universität Weimar, Germany; Leipzig University, Germany",information-retrieval/search-engine,,CC-MAIN-2015-11,,,
"Paolo Boldi, Andrea Marino, Massimo Santini, Sebastiano Vigna – Università degli Studi di Milano, Italy",BUbiNG: Massive crawling for the masses,https://dl.acm.org/citation.cfm?id=3160017,papers,20180101Z00:00:00,,,"Università degli Studi di Milano, Italy","web-crawling, web-science/hyperlinkgraph",,,,,WDC-hyperlinkgraph
"Fabienne Braune, Alex Fraser, Barry Haddow – University of Edinburgh",D1. 2: Report on Improving Translation with Monolingual Data,http://www.himl.eu/files/D1.2_Using_Non_Parallel.pdf,papers,20180101Z00:00:00,,,University of Edinburgh,nlp/machine-translation,,,,,
"Tomáš Brychcín, Tomáš Hercig, Josef Steinberger, Michal Konkol – University of West Bohemia, Czech Republic",UWB at SemEval-2018 Task 10: Capturing Discriminative Attributes from Word Distributions,http://www.aclweb.org/anthology/S18-1153,papers,20180101Z00:00:00,,,"University of West Bohemia, Czech Republic","nlp/semantics, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Michael Cafarella, Alon Halevy, Hongrae Lee, Jayant Madhavan, Cong Yu, Daisy Zhe Wang, Eugene Wu – Google Inc.; University of Michigan, USA; Megagon Labs; University of Florida, USA; Columbia University, USA",Ten years of webtables,https://dl.acm.org/citation.cfm?id=3275614,papers,20180101Z00:00:00,,,"Google Inc.; University of Michigan, USA; Megagon Labs; University of Florida, USA; Columbia University, USA","semantic web, web tables, web-mining","Several researchers produced web tables from the public Common Crawl [1, 24, 15], thereby making them available to a broad audience outside the large Web companies.",,,,"WDCWebTables, DresdenWebTableCorpus"
"Casey Casalnuovo, Kenji Sagae, Prem Devanbu – University of California, Davis, USA",Studying the Difference Between Natural and Programming Language Corpora,https://link.springer.com/article/10.1007/s10664-018-9669-7,papers,20180101Z00:00:00,,,"University of California, Davis, USA","nlp/corpus-construction, nlp/text-corpora, programming-languages, nlp/syntax","The Germanand Spanish corpora were selected from a sample of files from the unlabeled datasets from the ConLL 2017 Shared Task (Ginter et al, 2017), which consist of web text obtained from CommonCrawl.⁸ Like the 1 billion token English corpus, we selected a random subsample to make these corpora size comparable with our other corpora. In this sample, we excluded files from the Wikipedia translations, as we observed Wikipedia formatting mixed in with some of the files.",,,conll-2017-shared-task,
"Xinghan Chen, Mingxing Zhang, Zheng Wang, Lin Zuo, Bo Li, Yang Yang – University of Electronic Science and Technology of China (UESTC), Chengdu, PR China",Leveraging Unpaired Out-of-Domain Data for Image Captioning,https://www.sciencedirect.com/science/article/abs/pii/S0167865518309358,papers,20180101Z00:00:00,,,"University of Electronic Science and Technology of China (UESTC), Chengdu, PR China","nlp/text-generation, ai/image-classification, nlp/image-captioning, ai/deep-learning",,,,,
"Zewen Chi, Heyan Huang, Jiangui Chen, Hao Wu, Ran Wei – Beijing Institute of Technology, China",Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets,http://www.aclweb.org/anthology/S18-1046,papers,20180101Z00:00:00,,,"Beijing Institute of Technology, China","nlp, nlp/sentiment-analysis, nlp/emotion-detection, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Mara Chinea-Rios, Alvaro Peris, Francisco Casacuberta – Universitat d'Alacant, Spain",Are Automatic Metrics Robust and Reliable in Specific Machine Translation Tasks?,http://rua.ua.es/dspace/handle/10045/76022,papers,20180101Z00:00:00,,,"Universitat d'Alacant, Spain",nlp/machine-translation,"In our setup, we trained a PB-SMT and a NMT system on the same data, from a general corpus extracted from websites (Common Crawl).",,,,
"Shamil Chollampatt, Hwee Tou Ng – NUS Graduate School for Integrative Sciences and Engineering; Department of Computer Science, National University of Singapore",A multilayer convolutional encoder-decoder neural network for grammatical error correction,https://arxiv.org/abs/1801.08831,papers,20180101Z00:00:00,,,"NUS Graduate School for Integrative Sciences and Engineering; Department of Computer Science, National University of Singapore","nlp/grammatical-error-correction, nlp/word-embeddings, nlp/language-model","We also make use of the larger English corpora from Wikipedia (1.78B words) for pre-training the word embeddings, and a subset of the Common Crawl corpus (94B words) for training the language model for rescoring.",,,,
"Kenneth Clarkson, Anna Lisa Gentile, Daniel Gruhl, Petar Ristoski, Joseph Terdiman, Steve Welch – IBM Research Almaden, San Jose, USA",User-Centric Ontology Population,https://link.springer.com/chapter/10.1007/978-3-319-93417-4_8,papers,20180101Z00:00:00,,,"IBM Research Almaden, San Jose, USA","semantic web, cc-cited-not-used, ontology extraction",,,,,
"Trevor Cohen, Dominic Widdows – University of Washington, Seattle, USA; Grab, Inc., Seattle, WA, USA",Bringing Order to Neural Word Embeddings with Embeddings Augmented by Random Permutations (EARP),http://www.aclweb.org/anthology/K18-1045,papers,20180101Z00:00:00,,,"University of Washington, Seattle, USA; Grab, Inc., Seattle, WA, USA","nlp/word-embeddings, cc-cited-not-used",,,,,
"Alexis Conneau, Douwe Kiela – Facebook Artificial Intelligence Research",SentEval: An evaluation toolkit for universal sentence representations,https://arxiv.org/abs/1803.05449,papers,20180101Z00:00:00,,,Facebook Artificial Intelligence Research,"nlp/word-embeddings, nlp/sentence-embeddings, nlp/evaluation",,,,"GloVe-word-embeddings, fastText-word-embeddings",
"Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R Bowman, Holger Schwenk, Veselin Stoyanov – Facebook AI Research, USA; New York University, USA",XNLI: Evaluating Cross-lingual Sentence Representations,https://arxiv.org/abs/1809.05053,papers,20180101Z00:00:00,,,"Facebook AI Research, USA; New York University, USA","nlp/word-embeddings, nlp/sentence-embeddings",,,,fasttext-word-embeddings,
"Michael Conover, Matthew Hayes, Scott Blackburn, Pete Skomoroch, Sam Shah – Workday, Inc., San Francisco, CA, USA",Pangloss: Fast Entity Linking in Noisy Text Environments,https://dl.acm.org/citation.cfm?id=3219899,papers,20180101Z00:00:00,,,"Workday, Inc., San Francisco, CA, USA",ir/information-extraction,"The Common Crawl datasets represents a sample of web crawl data containing raw web page data, metadata and text extracts overseen by a 501(c)(3) nonprofit of the same name. Facilitating ease of access for industrial practitioners, the dataset is hosted for free on Amazon Web Services’ Public Data Set repository in addition to academic hosts the world over. As part of a batch Hadoop job run on a monthly basis we filter the Common Crawl data (∼70TB) down to records which contain at least one hyperlink that points to English Wikipedia. This corpus has proven particularly valuable as a source of signal for associating tokens with knowledge base entries in the context of domain-specific, messy natural language.",,,,
"Andreiwid Sheffer Correa, Pär-Ola Zander, Flavio Soares Correa da Silva – University of Sao Paulo, Sao Paulo, Brazil; Aalborg University, Aalborg, Denmark",Investigating open data portals automatically: a methodology and some illustrations,https://dl.acm.org/citation.cfm?id=3209292,papers,20180101Z00:00:00,,,"University of Sao Paulo, Sao Paulo, Brazil; Aalborg University, Aalborg, Denmark","open data, information retrieval",,,,,
"J Shane Culpepper, Fernando Diaz, Mark D. Smucker – ACM",Research Frontiers in Information Retrieval: Report from the Third Strategic Workshop on Information Retrieval in Lorne (SWIRL 2018),http://doi.acm.org/10.1145/3274784.3274788,papers,20180101Z00:00:00,,,ACM,"cc-cited-not-used, information-retrieval",,,,,
"Alexander Czech – TU Wien, Austria","An Approach to Geotag a Web Sized Corpus of Documents with Addresses in Randstad, Netherlands",https://doi.org/10.3929/ethz-b-000225615,papers,20180101Z00:00:00,,,"TU Wien, Austria",ir/geotagging,Common Crawl is a non-profit organization that provides raw web crawling data on a monthly basis. Their archives contain over 3.16 billion URLs with over 260 TiB of uncompressed content.,,,,
"Berkan Demirel, Ramazan Gokberk Cinbis, Nazli Ikizler-Cinbis – HAVELSAN Inc. Ankara, Turkey; Middle East Technical University Ankara, Turkey; Hacettepe University Ankara, Turkey",Zero-Shot Object Detection by Hybrid Region Embedding,https://arxiv.org/abs/1805.06157,papers,20180101Z00:00:00,,,"HAVELSAN Inc. Ankara, Turkey; Middle East Technical University Ankara, Turkey; Hacettepe University Ankara, Turkey","ai/computer-vision, ai/pattern-recognition, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Pavel Denisov, Ngoc Thang Vu, Marc Ferras Font – University of Stuttgart, Germany",Unsupervised Domain Adaptation by Adversarial Learning for Robust Speech Recognition,https://arxiv.org/abs/1807.11284,papers,20180101Z00:00:00,,,"University of Stuttgart, Germany","nlp, speech-recognition","..., 197 millions words of Italian Deduplicated CommonCrawl Text are used to build Italian language model.",,,,
"Sunipa Dev, Safia Hassan, Jeff M Phillips – University of Utah",Absolute Orientation for Word Embedding Alignment,https://arxiv.org/abs/1806.01330,papers,20180101Z00:00:00,,,University of Utah,"nlp/semantics, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Sergey Edunov, Myle Ott, Michael Auli, David Grangier – Facebook AI Research, USA; Google Brain, Mountain View, CA, USA",Understanding Back-Translation at Scale,https://arxiv.org/abs/1808.09381,papers,20180101Z00:00:00,,,"Facebook AI Research, USA; Google Brain, Mountain View, CA, USA",nlp/machine-translation,,,,,
"Julia Efremova, Ian Endres, Isaac Vidas, Ofer Melnik – HERE Technologies, Amsterdam, The Netherlands",A Geo-Tagging Framework for Address Extraction from Web Pages,https://link.springer.com/chapter/10.1007/978-3-319-95786-9_22,papers,20180101Z00:00:00,,,"HERE Technologies, Amsterdam, The Netherlands",semantic-web/microformats,"Common Crawl is a public corpus, mostly stored on Amazon Web Services³. A subset of the CommonCrawl dataset has schema information in the microdata format",,,,
"Samer El Zant, Katia Jaffrès-Runser, Klaus M. Frahm, Dima L. Shepelyansky – Université de Toulouse, France",Interactions and influence of world painters from the reduced Google matrix of Wikipedia networks,https://ieeexplore.ieee.org/abstract/document/8449078,papers,20180101Z00:00:00,,"This paper concentrates on extracting painting art history knowledge from the network structure of Wikipedia. Therefore, we construct theoretical networks of webpages representing the hyper-linked structure of articles of seven Wikipedia language editions. These seven networks are analyzed to extract the most influential painters in each edition using Google matrix theory. Importance of webpages of over 3000 painters is measured using the PageRank algorithm. The most influential painters are enlisted and their ties are studied with the reduced Google matrix analysis. The reduced Google matrix is a powerful method that captures both direct and hidden interactions between a subset of selected nodes taking into account the indirect links between these nodes via the remaining part of large global network. This method originates from the scattering theory of nuclear and mesoscopic physics and field of quantum chaos. In this paper, we show that it is possible to extract from the components of the reduced Google matrix meaningful information on the ties between these painters. For instance, our analysis groups together painters that belong to the same painting movement and shows meaningful ties between painters of different movements. We also determine the influence of painters on world countries using link sensitivity between Wikipedia articles of painters and countries. The reduced Google matrix approach allows to obtain a balanced view of various cultural opinions of Wikipedia language editions. The world countries with the largest number of top painters of selected seven Wikipedia editions are found to be Italy, France, and Russia. We argue that this approach gives meaningful information about art and that it could be a part of extensive network analysis on human knowledge and cultures.","Université de Toulouse, France","web-science/hyperlinkgraph, graph-processing, cc-cited-not-used",,,,,
"Cristina Espana-Bonet, Juliane Stiller, Sophie Henning – Universität des Saarlandes, Germany; Humboldt-Universität zu Berlin, Germany",M1. 2--Corpora for the Machine Translation Engines,https://www.clubs-project.eu/assets/publications/project/M1.2_MTcorpora_v4.0.pdf,papers,20180101Z00:00:00,,,"Universität des Saarlandes, Germany; Humboldt-Universität zu Berlin, Germany","nlp/machine-translation, nlp/corpora",,,,,WMT-13-translation-task-common-crawl-corpus
"Diego Esteves, Aniketh Janardhan Reddy, Piyush Chawla, Jens Lehmann – University of Bonn, Germany; University of Ohio, USA; Carnegie Mellon University, Pittsburgh, USA;",Belittling the Source: Trustworthiness Indicators to Obfuscate Fake News on the Web,https://arxiv.org/abs/1809.00494,papers,20180101Z00:00:00,,,"University of Bonn, Germany; University of Ohio, USA; Carnegie Mellon University, Pittsburgh, USA;","nlp, text classification, content credibility, information retrieval",PageRankCC: PageRank information computed through the CommonCrawl Corpus,,,,
"Stefano Faralli, Els Lefever, Simone Paolo Ponzetto – University of Mannheim, Germany; Ghent University, Belgium",MIsA: Multilingual IsA Extraction from Corpora,https://biblio.ugent.be/publication/8562721,papers,20180101Z00:00:00,,,"University of Mannheim, Germany; Ghent University, Belgium","nlp/semantics, data-mining, hypernymy",,,,,WDC-WebIsADb
"Ruslan R. Fayzrakhmanov, Emanuel Sallinger, Ben Spencer, Tim Furche, Georg Gottlob – University of Oxford, Oxford, United Kingdom",Browserless web data extraction: challenges and opportunities,https://dl.acm.org/citation.cfm?id=3186008,papers,20180101Z00:00:00,,,"University of Oxford, Oxford, United Kingdom","information retrieval, web-crawling, web-scraping, web-mining","The random sites were chosen by randomly sampling URLs from the Common Crawl [10] search index dataset, which includes around 3 billion web pages.",,,,
"Agostino Funel – ENEA, Italy",Analysis of the Web Graph Aggregated by Host and Pay-Level Domain,https://arxiv.org/abs/1802.05435,papers,20180101Z00:00:00,,,"ENEA, Italy",web-science/hyperlinkgraph,,"hyperlinkgraph/cc-main-2017-aug-sep-oct/hostgraph, hyperlinkgraph/cc-main-2017-aug-sep-oct/domaingraph",,,
"Andres Garcia, Jose Manuel Gomez-Perez – expertsystem.com, Madrid, Spain",Not just about size-A Study on the Role of Distributed Word Representations in the Analysis of Scientific Publications,https://arxiv.org/abs/1804.01772,papers,20180101Z00:00:00,,,"expertsystem.com, Madrid, Spain",nlp/word-embeddings,,,,"fastText-word-embeddings, GloVe-word-embeddings",
"Andres Garcia, Jose Manuel Gomez-Perez – expertsystem.com, Madrid, Spain",Not just about size-A Study on the Role of Distributed Word Representations in the Analysis of Scientific Publications,http://ceur-ws.org/Vol-2106/paper3.pdf,papers,20180101Z00:00:00,,,"expertsystem.com, Madrid, Spain",nlp/word-embeddings,,,,"fastText-word-embeddings, GloVe-word-embeddings",
"Nikhil Garg, Londa Schiebinger, Dan Jurafsky, James Zou – Stanford University, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA",Word embeddings quantify 100 years of gender and ethnic stereotypes,https://www.pnas.org/content/115/16/E3635.short,papers,20180101Z00:00:00,,,"Stanford University, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA","nlp/semantics, nlp/word-embeddings, ai/ethics-of-machine-learning, ai/machine-learning",,,,GloVe-word-embeddings,
"Majid Ghasemi-Gol, Pedro Szekely – University of Southern California; Information Science Institute",TabVec: Table Vectors for Classification of Web Tables,https://arxiv.org/abs/1802.06290,papers,20180101Z00:00:00,,,University of Southern California; Information Science Institute,"web-tables, information-extraction",[...] we use a random sample of July 2015 Common Crawl (WCC) as a generic domain to compare our system with the state of the art systems,CC-MAIN-2015-32,,,"WDCWebTables, DresdenWebTableCorpus"
"Michael Glass, Alfio Gliozzo – IBM Research AI",Discovering Implicit Knowledge with Unary Relations,http://www.aclweb.org/anthology/P18-1147,papers,20180101Z00:00:00,,,IBM Research AI,ai/knowledge-base,,,,,
"Michael Glass, Alfio Gliozzo – Knowledge Induction and Reasoning Group, IBM Research AINew YorkUSA",A Dataset for Web-Scale Knowledge Base Population,https://link.springer.com/chapter/10.1007/978-3-319-93417-4_17,papers,20180101Z00:00:00,,,"Knowledge Induction and Reasoning Group, IBM Research AINew YorkUSA","ai/semantic-reasoning, ai/knowledge-base","We introduce and release CC-DBP, a web-scale dataset for training and benchmarking KBP systems. The dataset is based on Common Crawl as the corpus and DBpedia as the target knowledge base [...]",CC-MAIN-2017-26,CC-DBP,,
"Michael Glass, Alfio Gliozzo, Oktie Hassanzadeh, Nandana Mihindukulasooriya, Gaetano Rossiello – IBM Research AI, New York, USA; Universidad Politcnica de Madrid, Spain; University of Bari, Italy",Inducing implicit relations from text using distantly supervised deep nets,https://link.springer.com/chapter/10.1007/978-3-030-00671-6_3,papers,20180101Z00:00:00,,,"IBM Research AI, New York, USA; Universidad Politcnica de Madrid, Spain; University of Bari, Italy","ai/knowledge-base, ai/deep-learning, semantic web",,,,CC-DBP,
"Pranav Goel, Yoichi Matsuyama, Michael Madaio, Justine Cassell – Indian Institute of Technology (BHU), India; Carnegie Mellon University","“I think it might help if we multiply, and not add”: Detecting Indirectness in Conversation",http://articulab.hcii.cs.cmu.edu/wordpress/wp-content/uploads/2018/04/Goel-IWSDS2018_camera-ready_13Mar.pdf,papers,20180101Z00:00:00,,,"Indian Institute of Technology (BHU), India; Carnegie Mellon University","nlp/dialogue-systems, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Viktor Golem, Mladen Karan, Jan Šnajder – University of Zagreb, Croatia",Combining Shallow and Deep Learning for Aggressive Text Detection,www.aclweb.org/anthology/W18-4422,papers,20180101Z00:00:00,,,"University of Zagreb, Croatia","nlp/text-classification, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Paul Gooding, Melissa Terras, Linda Berube – University of East Anglia, United Kingdom; University of Edinburgh, United Kingdom",Legal Deposit Web Archives and the Digital Humanities: A Universe of Lost Opportunity?,http://eprints.gla.ac.uk/168229/,papers,20180101Z00:00:00,,,"University of East Anglia, United Kingdom; University of Edinburgh, United Kingdom",web-archiving/legal-aspects,"Restricted deposit library access requires researchers to look elsewhere for portable web data: by undertaking their own web crawls, or by utilising datasets from Common Crawl (http://commoncrawl.org/) and the Internet Archive (https://archive.org). Both organisations provide vital services to researchers, and both innovate in areas that would traditionally fall under the deposit libraries’ purview. They support their mission by exploring the boundaries of copyright, including exceptions for non-commercial text and data mining (Intellectual Property Office, 2014). This contrast between risk-enabled independent organisations and deposit libraries, described by interviewees as risk averse, challenges library/DH collaboration models such as BL Labs (http://labs.bl.uk) and Library of Congress Labs (https://labs.loc.gov).",,,,
"Rajendra Banjade, Nabin Maharjan, Dipesh Gautam, Frank Adrasik, Arthur C. Graesser, Vasile Rus – University of Memphis, USA",Pooling Word Vector Representations Across Models,https://www.springer.com/de/book/9783319771151,papers,20180101Z00:00:00,,,"University of Memphis, USA","nlp/word-embeddings, nlp/semantics",,,,GloVe-word-embeddings,
"Gabriel Grand, Idan Asher Blank, Francisco Pereira, Evelina Fedorenko – Harvard University; Massachusetts Institute of Technology; Siemens Healthineers; Massachusetts General Hospital; Harvard Medical School","Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings",https://arxiv.org/abs/1802.01241,papers,20180101Z00:00:00,,,Harvard University; Massachusetts Institute of Technology; Siemens Healthineers; Massachusetts General Hospital; Harvard Medical School,"nlp/semantics, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Edouard Grave, Piotr Bojanowski, Prakhar Gupta, Armand Joulin, Tomas Mikolov – Facebook AI Research; École polytechnique fédérale de Lausanne EPFL, Switzerland",Learning word vectors for 157 languages,https://www.aclweb.org/anthology/L18-1550,papers,20180101Z00:00:00,,"Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredient to the successful application of these representations is to train t hem on very large corpora, and use these pre-trained models in downstream tasks. In this paper, we describe how we trained such high qualit y word representations for 157 languages. We used two sources of data to train these models: the free online encyclopedia Wikip edia and data from the common crawl project. We also introduce three new word analogy datasets to evaluate these word vectors, for Fren ch, Hindi and Polish. Finally, we evaluate our pre-trained word vectors on 10 languages for which evaluation datasets exists, sho wing very strong performance compared to previous models.","Facebook AI Research; École polytechnique fédérale de Lausanne EPFL, Switzerland",nlp/word-embeddings,"The common crawl is a non profit organization which crawls the web and makes the resulting data publicly available. This large scale corpus was previously used to estimate n-gram language models (Buck et al., 2014) or to learn English word vectors (Pennington et al., 2014). To the best of our knowledge, it was not used yet to learn word vectors for a large set of languages. The data is distributed either as raw HTML pages, or as WET files which contain the extracted text data, converted to UTF-8. We decided to use the extracted text data, as it is much smaller in size, and easier to process (no need to remove HTML). We downloaded the May 2017 crawl, corresponding to roughly 24 terabytes of raw text data.",CC-MAIN-2017-22 (WET),fastText-word-embeddings,,
"Roman Grundkiewicz, Marcin Junczys-Dowmunt – University of Edinburgh, United Kingdom; Microsoft",Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation,https://arxiv.org/abs/1804.05945,papers,20180101Z00:00:00,,,"University of Edinburgh, United Kingdom; Microsoft","nlp/machine-translation, nlp/grammatical-error-correction",,,,Ngrams-LMs-2013,
"Amir Hazem, Emmanuel Morin – Université de Nantes, France",Leveraging Meta-Embeddings for Bilingual Lexicon Extraction from Specialized Comparable Corpora,http://www.aclweb.org/anthology/C18-1080,papers,20180101Z00:00:00,,,"Université de Nantes, France","nlp/machine-translation, nlp/lexikon, nlp/dictionary-creation",,,,,
"Michael A. Hedderich, Dietrich Klakow – Saarland University, Saarbrücken, Germany",Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data,https://arxiv.org/abs/1807.00745,papers,20180101Z00:00:00,,,"Saarland University, Saarbrücken, Germany","nlp/word-embeddings, ai/neural-networks",,,,GloVe-word-embeddings,
"Lena Hettinger, Alexander Dallmann, Albin Zehe, Thomas Niebler, Andreas Hotho – University of Würzburg, Germany",ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings,http://www.aclweb.org/anthology/S18-1134,papers,20180101Z00:00:00,,,"University of Würzburg, Germany","nlp/semantics, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Lena Hettinger, Alexander Dallmann, Albin Zehe, Thomas Niebler, Andreas Hotho – University of Würzburg, Germany",ClaiRE at SemEval-2018 Task 7-Extended Version,https://arxiv.org/abs/1804.05825,papers,20180101Z00:00:00,,,"University of Würzburg, Germany","nlp/semantics, nlp/word-embeddings","we employ a publicly available set of 300-dimensional word embeddings trained with GloVe (Pennington et al., 2014) on the Common Crawl data",,,,
"Jiaji Huang, Yi Li, Wei Ping, Liang Huang – Baidu Research, Sunnyvale, CA, USA; School of EECS, Oregon State University, Corvallis, OR, USA",Large Margin Neural Language Model,https://arxiv.org/abs/1808.08987,papers,20180101Z00:00:00,,,"Baidu Research, Sunnyvale, CA, USA; School of EECS, Oregon State University, Corvallis, OR, USA","nlp/language-model, nlp/machine-translation",,,,WMT-16-translation-task-common-crawl-corpus,
"Balázs Indig – MTA-PPKE Magyar Nyelvtechnológiai Kutatócsoport, Hungaria",Közös crawlnak is egy korpusz a vége-Korpuszépítés a CommonCrawl .hu domainjából,http://real.mtak.hu/73329/1/crawl.pdf,papers,20180101Z00:00:00,,,"MTA-PPKE Magyar Nyelvtechnológiai Kutatócsoport, Hungaria",web-science,,CC-MAIN-2017-47,,,
"Mohit Iyyer, John Wieting, Kevin Gimpel, Luke Zettlemoyer – Allen Institute of Artificial Intelligence, Seattle, United States; UMass Amherst, United States; Carnegie Mellon University, Pittsburgh, PA, USA; Toyota Technological Institute at Chicago, IL, USA; University of Washington, Seattle, WA, USA",Adversarial example generation with syntactically controlled paraphrase networks,https://arxiv.org/abs/1804.06059,papers,20180101Z00:00:00,,,"Allen Institute of Artificial Intelligence, Seattle, United States; UMass Amherst, United States; Carnegie Mellon University, Pittsburgh, PA, USA; Toyota Technological Institute at Chicago, IL, USA; University of Washington, Seattle, WA, USA","nlp/machine-translation, nlp/sentence-paraphrase, nlp/sentence-embeddings",,,"WMT-16-translation-task-common-crawl-corpus, patent",,
"Armand Joulin, Piotr Bojanowski, Tomas Mikolov, Hervé Jégou, Edouard Grave – Facebook AI Research",Loss in translation: Learning bilingual word mapping with a retrieval criterion,https://www.aclweb.org/anthology/papers/D/D18/D18-1330/,papers,20180101Z00:00:00,,,Facebook AI Research,"nlp/word-embeddings, nlp/bilingual-word-embeddings",,,,fastText-word-embeddings,
"Marcin Junczys-Dowmunt, Roman Grundkiewicz, Shubha Guha, Kenneth Heafield – University of Edinburgh, United Kingdom; Microsoft",Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task,https://arxiv.org/abs/1804.05940,papers,20180101Z00:00:00,,,"University of Edinburgh, United Kingdom; Microsoft","nlp/machine-translation, nlp/grammatical-error-correction",,,,,
"David Jurgens, Srijan Kumar, Raine Hoover, Dan McFarland, Dan Jurafsky – University of Michigan, USA; Stanford University, USA",Measuring the evolution of a scientific field through citation frames,https://doi.org/10.1162/tacl_a_00028,papers,20180101Z00:00:00,,,"University of Michigan, USA; Stanford University, USA","nlp/word-embeddings, nlp/text-analysis, nlp/citation-analysis",,,,,GloVe-word-embeddings
"Tomer Kaftan, Magdalena Balazinska, Alvin Cheung, Johannes Gehrke – University of Washington; Microsoft",Cuttlefish: A Lightweight Primitive for Adaptive Query Processing,https://arxiv.org/abs/1802.09180,papers,20180101Z00:00:00,,,University of Washington; Microsoft,"information retrieval, regular expression matching, query planning, SQL processing",... to search through a contiguously-stored sample of approximately 256 thousand internet web pages collected by the Common Crawl project.,,,,
"Alexander Kagoshima, Kai Londenberg, Fang Xu – Searchmetrics GmbH",Determination of content score,https://patents.google.com/patent/US20180121430A1/en,papers,20180101Z00:00:00,,,Searchmetrics GmbH,"patent, cc-cited-not-used","The crawler module [310] may automatically crawl a network and acquire contents from one or more resources in the network, acquire the contents from an open repository of web crawl data such as CommonCrawl.org.",,,,
"Ajinkya Gorakhnath Kale, Thrivikrama Taula, Amit Srivastava, Sanjika Hewavitharana – eBay Inc.",Methods and systems for query segmentation,https://patents.google.com/patent/US20180329999A1/en,papers,20180101Z00:00:00,,,eBay Inc.,"ir/query-segmentation, nlp/word-embeddings, patent",,,,,GloVe-word-embeddings
"Kokas Károly, Drótos László – Országos Széchényi Könyvtár, Hungary; SZTE Klebelsberg Könyvtár, Hungary",Webarchiválás és a történeti kutatások / Web Archiving and Historical Research,http://ojs.elte.hu/index.php/digitalisbolcseszet/article/view/129,papers,20180101Z00:00:00,,,"Országos Széchényi Könyvtár, Hungary; SZTE Klebelsberg Könyvtár, Hungary","web-archiving, cc-cited-not-used",,,,,
"Issa M. Khalil, Bei Guan, Mohamed Nabeel, Ting Yu – Qatar Computing Research Institute, Doha, Qatar",A domain is only as good as its buddies: detecting stealthy malicious domains via graph inference,https://dl.acm.org/citation.cfm?id=3176329,papers,20180101Z00:00:00,,,"Qatar Computing Research Institute, Doha, Qatar","computer-security/malicious-domain-detection, computer-security/internet-security, graph-processing",,,,,
"Huda Khayrallah, Brian Thompson, Kevin Duh, Philipp Koehn – Johns Hopkins University, USA",Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine Translation,https://www.aclweb.org/anthology/papers/W/W18/W18-2705/,papers,20180101Z00:00:00,,,"Johns Hopkins University, USA",nlp/machine-translation,,,,WMT-16-translation-task-common-crawl-corpus,
"Douwe Kiela, Changhan Wang, Kyunghyun Cho – Facebook AI Research, USA; New York University, USA; CIFAR Global Scholar, Canada",Dynamic meta-embeddings for improved sentence representations,https://www.aclweb.org/anthology/D18-1176,papers,20180101Z00:00:00,,"While one of the first steps in many NLP systems is selecting what pre-trained word embeddings to use, we argue that such a step is better left for neural networks to figure out by themselves. To that end, we introduce dynamic meta-embeddings, a simple yet effective method for the supervised learning of embedding ensembles, which leads to state-of-the-art performance within the same model class on a variety of tasks. We subsequently show how the technique can be used to shed new light on the usage of word embeddings in NLP systems.","Facebook AI Research, USA; New York University, USA; CIFAR Global Scholar, Canada","nlp/sentence-embeddings, nlp/word-embeddings",,,,"GloVe-word-embeddings, fastText-word-embeddings",
"Johannes Kiesel, Florian Kneist, Milad Alshomary, Benno Stein, Matthias Hagen, Martin Potthast – Paderborn University, Germany; Bauhaus-Universität Weimar, Germany; Martin-Luther-Universität Halle-Wittenberg, Germany; Leipzig University, Germany; Ulm University, Germany",Reproducible Web Corpora: Interactive Archiving with Automatic Quality Assessment,https://dl.acm.org/citation.cfm?id=3239574,papers,20180101Z00:00:00,,,"Paderborn University, Germany; Bauhaus-Universität Weimar, Germany; Martin-Luther-Universität Halle-Wittenberg, Germany; Leipzig University, Germany; Ulm University, Germany","web-mining, nlp/web-as-corpus","To build a solid benchmark dataset for web reproduction quality assessment, we carefully sampledweb pages with the goal of representing a wide cross-section of the different types and genres of webpages found on the web. As a population of web pages to draw a sample from, we resort to the recentbillion-page Common Crawl 2017-04 [36]. From there, we primarily sampled pages from most ofthe well-known sites—as defined by the website’s Alexa traffic rank [1]⁶—to ensure that our sampleencompasses pages using the most recent web technologies and design standards. Moreover, pagesfrom a number of less well-known sites have been included. Altogether, the Webis Web Archive 17 comprises 10,000 web pages.",CC-MAIN-2017-04,,,
"Daesik Kim, Seonhoon Kim, Nojun Kwak – Seoul National University, South Korea; V.DO Inc., South Korea; Naver Corporation, South Korea",Textbook Question Answering with Knowledge Graph Understanding and Unsupervised Open-set Text Comprehension,https://arxiv.org/abs/1811.00232,papers,20180101Z00:00:00,,,"Seoul National University, South Korea; V.DO Inc., South Korea; Naver Corporation, South Korea","nlp/question-answering, nlp/word-embeddings, nlp/knowledge-graph, nlp/text-comprehension",,,,GloVe,
"Shun Kiyono, Jun Suzuki, Kentaro Inui – Tohoku University, Japan; Center for Advanced Intelligence Project, Japan",Mixture of Expert/Imitator Networks: Scalable Semi-supervised Learning Framework,https://arxiv.org/abs/1810.05788,papers,20180101Z00:00:00,,,"Tohoku University, Japan; Center for Advanced Intelligence Project, Japan","cc-cited-not-used, nlp/text-classification, ai/deep-learning, ai/neural-networks",,,,,
"Rebecca Knowles, Philipp Koehn – Johns Hopkins University, USA",Context and Copying in Neural Machine Translation,http://www.aclweb.org/anthology/D18-1339,papers,20180101Z00:00:00,,,"Johns Hopkins University, USA",nlp/machine-translation,,,,WMT-16-translation-task-common-crawl-corpus,
"Jacob Krantz, Jugal Kalita – Gonzaga University, USA; University of Colorado, USA",Abstractive Summarization Using Attentive Neural Techniques,https://arxiv.org/abs/1810.08838,papers,20180101Z00:00:00,,,"Gonzaga University, USA; University of Colorado, USA","nlp/text-summarization, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Dmitry Kravchenko, Lidia Pivovarova – Ben-Gurion University of the Negev, Israel; University of Helsinki, Finland",DL Team at SemEval-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings,http://www.aclweb.org/anthology/S18-1025,papers,20180101Z00:00:00,,,"Ben-Gurion University of the Negev, Israel; University of Helsinki, Finland",nlp/sentiment-analysis,,,,GloVe-word-embeddings,
"Artur Kulmizev – University of Groningen, The Netherlands",Multilingual word embeddings and their utility in cross-lingual learning,http://hdl.handle.net/10810/29083,papers,20180101Z00:00:00,,,"University of Groningen, The Netherlands","nlp/semantics, nlp/word-embeddings, cc-cited-not-used",,,,,
"Artur Kulmizev, Mostafa Abdou, Vinit Ravishankar, Malvina Nissim – University of Groningen, The Netherlands; Institute of Formal and Applied Linguistics Charles University in Prague, Czech Republic",Discriminator at SemEval-2018 Task 10: Minimally Supervised Discrimination,http://www.aclweb.org/anthology/S18-1167,papers,20180101Z00:00:00,,,"University of Groningen, The Netherlands; Institute of Formal and Applied Linguistics Charles University in Prague, Czech Republic","nlp/semantics, nlp/word-embeddings",,,,GloVe-word-embeddings,
"José Lages, Dima L. Shepelyansky, Andrei Zinovyev – Université de Franche-Comté, Besançon, France",Inferring hidden causal relations between pathway members using reduced Google matrix of directed biological networks,https://doi.org/10.1371/journal.pone.0190812,papers,20180101Z00:00:00,,,"Université de Franche-Comté, Besançon, France","cc-cited-not-used, graph-processing, web-science/hyperlinkgraph, network analysis, biochemistry, proteine structure","At present directed networks of real systems can be very large (about 4.2 millions for the English Wikipedia edition in 2013 [18] or 3.5 billion web pages for a publicly accessible web crawl that was gathered by the Common Crawl Foundation in 2012 [53: Meusel R, Vigna S, Lehmberg O, Bizer C. The graph structure in the web—analyzed on different aggregation levels. J. Web Sci. 2015;1:33.]).",,,,
"Oliver Lehmberg, Oktie Hassanzadeh – University of Mannheim, Germany; IBM Research, Yorktown Heights, New York, USA",Ontology Augmentation Through Matching with Web Tables,http://disi.unitn.it/~pavel/om2018/papers/om2018_LTpaper4.pdf,papers,20180101Z00:00:00,,,"University of Mannheim, Germany; IBM Research, Yorktown Heights, New York, USA","semantic web, ontology extraction, web tables",We perform an empirical study of the performance of this approach in using Web Tables extracted from the Common Crawl to augment the properties in DBpedia ontology.,,,WDCWebTables,
"Tao Li, Lei Lin, Minsoo Choi, Kaiming Fu, Siyuan Gong, Jian Wang – Purdue University, Indiana, USA",Youtube av 50k: an annotated corpus for comments in autonomous vehicles,https://arxiv.org/abs/1807.11227,papers,20180101Z00:00:00,,,"Purdue University, Indiana, USA","cc-cited-not-used, nlp/corpus-construction, nlp/opinion-mining, nlp/sentiment-analysis",,,,,
"Paul Pu Liang, Ziyin Liu, Amir Zadeh, Louis-Philippe Morency – Carnegie Mellon University",Multimodal Language Analysis with Recurrent Multistage Fusion: Supplementary Material,https://arxiv.org/abs/1808.03920,papers,20180101Z00:00:00,,,Carnegie Mellon University,"nlp/multi-modality, nlp/language-model","We used 300 dimensional Glove word embeddings trained on 840 billion tokens from the common crawl dataset (Pennington et al., 2014).",,,GloVe-word-embeddings,
"Xiaojing Liao, Sumayah Alrwais, Kan Yuan, Luyi Xing, XiaoFeng Wang, Shuang Hao, Raheem Beyah – Indiana University Bloomington, USA; King Saud University, Saudi Arabia; University of Texas at Dallas, USA; Georgia Institute of Technology, USA","Cloud repository as a malicious service: challenge, identification and implication",https://cybersecurity.springeropen.com/articles/10.1186/s42400-018-0015-6,papers,20180101Z00:00:00,,,"Indiana University Bloomington, USA; King Saud University, Saudi Arabia; University of Texas at Dallas, USA; Georgia Institute of Technology, USA","computer-security/malicious-hosting-service, computer-security/internet-security","[...], we developed BarFinder, a scanner that automatically detects Bars through inspecting the topological relations between websites and the cloud bucket they use, in an attempt to capture Bars based on the external features of the websites they serve. [...] Running the scanner over all the data collected by the Common Crawl (Crawl 2015), which indexed five billion web pages, for those associated with all major cloud storage providers (including Amazon S3, Cloudfront, Google Drive, etc.), we found around 1 million sites utilizing 6885 repositories hosted on these clouds. [...] We built the site list with the help of Common Crawl (Crawl 2015), a public big data project that crawls about 5 billion webpages each month through a large-scale Hadoop-based crawler and maintains lists of the crawled websites and their embedded links. Searching the Common Crawl (Crawl 2015) dataset, collected in February 2015, for the websites loading content from the 400 clean and malicious buckets identified above, we found 141,149 websites, were used by our crawler. [...] We further developed a tool in Python to recover cloud URLs from the web content gathered by Common Crawl.",CC-MAIN-2015-11,,,
"Dan Liu, Junhua Liu, Wu Guo, Shifu Xiong, Zhiqiang Ma, Rui Song, Chongliang Wu, Quan Liu – University of Science and Technology of China, China; IFLYTEK Co. LTD.",The USTC-NEL Speech Translation system at IWSLT 2018,https://arxiv.org/abs/1812.02455,papers,20180101Z00:00:00,,,"University of Science and Technology of China, China; IFLYTEK Co. LTD.",nlp/machine-translation,,,,WMT-16-translation-task-common-crawl-corpus,
"Bingbin Liu, Serena Yeung, Edward Chou, De-An Huang, Li Fei-Fei, Juan Carlos Niebles – Stanford University, USA; Google Cloud AI, Mountain View, USA",Temporal Modular Networks for Retrieving Complex Compositional Activities in Videos,http://openaccess.thecvf.com/content_ECCV_2018/html/Bingbin_Liu_Temporal_Modular_Networks_ECCV_2018_paper.html,papers,20180101Z00:00:00,,,"Stanford University, USA; Google Cloud AI, Mountain View, USA","ai/computer-vision, ir/video-retrieval, ai/action-recognition, nlp/word-embeddings",,,,,
"Chi-kiu Lo, Michel Simard, Darlene Stewart, Samuel Larkin, Cyril Goutte, Patrick Littell – National Research Council, Canada",Accurate semantic textual similarity for cleaning noisy parallel corpora using semantic machine translation evaluation metric: The NRC supervised submissions to the Parallel Corpus Filtering task,http://www.aclweb.org/anthology/W18-6481,papers,20180101Z00:00:00,,,"National Research Council, Canada","cc-cited-not-used, nlp/machine-translation, nlp/corpus-construction",,,,,
"Colin Lockard, Xin Luna Dong, Arash Einolghozati, Prashant Shiralkar – amazon.com",CERES: Distantly Supervised Relation Extraction from the Semi-Structured Web,https://arxiv.org/abs/1804.04635,papers,20180101Z00:00:00,,,amazon.com,"ir/information-extraction, ir/relation-extraction","The CommonCrawl corpus consists of monthly snapshots of pages from millions of websites [1] on the Web. We started with a few well-known sites, including rottentomatoes.com, boxofficemojo.com, and themoviedb.org. Based on a Wikipedia list of the largest global film industries by admissions, box office, and number of productions⁸, we then issued Google searches for terms corresponding to these countries, such as “Nigerian film database” and recorded resulting sites that had detail pages related to movies. We also issued a few additional searches related to specific genres we thought may not be well-represented in mainstream sites, including “animated film database” and “documentary film database”. After compiling our list of sites, we then checked CommonCrawl⁹ and kept all sites with more than one hundred pages available. Our final list contains a broad mix of movie sites, including sites based around national film industries, genres, film music, and screen size. Most are in English, but the set also includes sites in Czech, Danish, Icelandic, Italian, Indonesian, and Slovak. ⁸https://en.wikipedia.org/wiki/Film_industry ⁹For each site, we scanned the CommonCrawl indices for all monthly scrapes prior to January 2018 and downloaded all pages for the site from the scrape with the largest number of unique webpages. Note that these scrapes do not necessarily obtain all pages present on a site, so the retrieved pages represent only a subset of the full site.",CC-MAIN-201[3-7]-*,,,
"Gaurav Maheshwari, Priyansh Trivedi, Denis Lukovnikov, Nilesh Chakraborty, Asja Fischer, Jens Lehmann – University of Bonn, Germany; Ruhr University, Bochum, Germany",Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs,https://arxiv.org/abs/1811.01118,papers,20180101Z00:00:00,,,"University of Bonn, Germany; Ruhr University, Bochum, Germany","information retrieval, nlp/question-answering, nlp/knowledge-graph, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Jose L. Martinez-Rodriguez, Aidan Hogan, Ivan Lopez-Arevalo – Cinvestav Tamaulipas, Ciudad Victoria, Mexico; University of Chile, Chile",Information extraction meets the Semantic Web: A survey,https://content.iospress.com/articles/semantic-web/sw180333,papers,20180101Z00:00:00,,,"Cinvestav Tamaulipas, Ciudad Victoria, Mexico; University of Chile, Chile","cc-cited-not-used, semantic web, linked data, information extraction",,,,,
"Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, Richard Socher – Salesforce Research",The natural language decathlon: Multitask learning as question answering,https://arxiv.org/abs/1806.08730,papers,20180101Z00:00:00,,,Salesforce Research,"nlp/question-answering, nlp/machine-translation, nlp/text-summarization, nlp/sentiment-analysis, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Bryan McCann, Caiming Xiong, Richard Socher – Salesforce.com, Inc.",Natural language processing using context-specific word vectors,https://patents.google.com/patent/US20180373682A1/en,papers,20180101Z00:00:00,,,"Salesforce.com, Inc.","nlp/word-embeddings, patent",,,,,GloVe-word-embeddings
"Bryan McCann, Caiming Xiong, Richard Socher – Salesforce.com, Inc.",Natural language processing using a neural network,https://patents.google.com/patent/US20180349359A1/en,papers,20180101Z00:00:00,,,"Salesforce.com, Inc.","nlp/word-embeddings, patent",,,,,GloVe-word-embeddings
"Evert Meijers, Antoine Peris – Delft University of Technology, The Netherlands","Using toponym co-occurrences to measure relationships between places: review, application and evaluation",https://www.tandfonline.com/doi/abs/10.1080/12265934.2018.1497526,papers,20180101Z00:00:00,,,"Delft University of Technology, The Netherlands","nlp, coocurrences, toponymy, urban system, place name disambiguation, semantic relatedness","We innovate by exploiting a so far unparalleled amount of data, namely the billions of web pages contained in the commoncrawl web archive, and by applying the method also to small places that tend to be ignored by other methods. [...] we use the March 2017 data. The Common Crawl data comes in three formats, of which the WET format is most useful for the co-occurrence method as it only contains extracted plain text.",,,,
"Hardik Meisheri, Lipika Dey – TCS Research, New Delhi, India",TCS Research at SemEval-2018 Task 1: Learning Robust Representations using Multi-Attention Architecture,http://www.aclweb.org/anthology/S18-1043,papers,20180101Z00:00:00,,,"TCS Research, New Delhi, India",nlp/sentiment-analysis,,,,GloVe-word-embeddings,
"Todor Mihaylov, Peter Clark, Tushar Khot, Ashish Sabharwal – Allen Institute for Artificial Intelligence, Seattle, USA; Heidelberg University, Germany",Can a suit of armor conduct electricity? a new dataset for open book question answering,https://www.aclweb.org/anthology/D18-1260,papers,20180101Z00:00:00,,"We present a new kind of question answering dataset, OpenBookQA, modeled after open book exams for assessing human understanding of a subject. The open book that comes with our questions is a set of 1326 elementary level science facts. Roughly 6000 questions probe an understanding of these facts and their application to novel situations. This requires combining an open book fact (e.g., metals conduct electricity) with broad common knowledge (e.g., a suit of armor is made of metal) obtained from other sources. While existing QA datasets over documents or knowledge bases, being generally self-contained, focus on linguistic understanding, OpenBookQA probes a deeper understanding of both the topic{---}in the context of common knowledge{---}and the language it is expressed in. Human performance on OpenBookQA is close to 92{\%}, but many state-of-the-art pre-trained QA methods perform surprisingly poorly, worse than several simple neural baselines we develop. Our oracle experiments designed to circumvent the knowledge retrieval bottleneck demonstrate the value of both the open book and additional facts. We leave it as a challenge to solve the retrieval problem in this multi-hop setting and to close the large gap to human performance.","Allen Institute for Artificial Intelligence, Seattle, USA; Heidelberg University, Germany","nlp/question-answering, nlp/word-embeddings, nlp/corpus-construction","For all experiments we used= 300GloVe(Penningtonet al., 2014) embeddings pre-trained on 840B tokens fromCommon Crawl(https://nlp.stanford.edu/projects/glove/).",,,GloVe-word-embeddings,
"Sewon Min, Victor Zhong, Richard Socher, Caiming Xiong – Seoul National University, South Korea; Salesforce Research",Efficient and Robust Question Answering from Minimal Context over Documents,https://arxiv.org/abs/1805.08092,papers,20180101Z00:00:00,,,"Seoul National University, South Korea; Salesforce Research","nlp/question-answering, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Bahman Mirheidari, Daniel Blackburn, Traci Walker, Annalena Venneri, Markus Reuber, Heidi Christensen – University of Sheffield, United Kingdom; Royal Hallamshire Hospital, United Kingdom",Detecting signs of dementia using word vector representations,https://www.isca-speech.org/archive/Interspeech_2018/pdfs/1764.pdf,papers,20180101Z00:00:00,,,"University of Sheffield, United Kingdom; Royal Hallamshire Hospital, United Kingdom","nlp/word-embeddings, nlp/speech-recognition, nlp/clinical-application, dementia detection",,,,GloVe-word-embeddings,
"Alistair Moffat, Matthias Petri – University of Melbourne, Australia",Index compression using byte-aligned ANS coding and two-dimensional contexts,https://dl.acm.org/citation.cfm?id=3159663,papers,20180101Z00:00:00,,"We examine approaches used for block-based inverted index compression, such as the OptPFOR mechanism, in which fixed-length blocks of postings data are compressed independently of each other. Building on previous work in which asymmetric numeral systems (ANS) entropy coding is used to represent each block, we explore a number of enhancements: (i) the use of two-dimensional conditioning contexts, with two aggregate parameters used in each block to categorize the distribution of symbol values that underlies the ANS approach, rather than just one; (ii) the use of a byte-friendly strategic mapping from symbols to ANS codeword buckets; and (iii) the use of a context merging process to combine similar probability distributions. Collectively, these improvements yield superior compression for index data, outperforming the reference point set by the Interp mechanism, and hence representing a significant step forward. We describe experiments using the 426 GiB gov2 collection and a new large collection of publicly-available news articles to demonstrate that claim, and provide query evaluation throughput rates compared to other block-based mechanisms.","University of Melbourne, Australia","information-retrieval/search-engine, information-retrieval/inverted-index","The second pair of test files are derived from publicly available web-sourced news articles² [²http://commoncrawl.org/2016/10/news-dataset-available/], taking English language news sources (as identified by Apache Tika) from 01/09/2016 up until and including 28/02/2017, that is, a six month crawl period that contains 7,508,082 documents.",CC-NEWS,,,
"Nkwebi Motlogelwa, Edwin Thuma, Tebo Leburu-Dingalo – University of Botswana, Botswana",Merging search results generated by multiple query variants using data fusion,http://ceur-ws.org/Vol-2125/paper_194.pdf,papers,20180101Z00:00:00,,,"University of Botswana, Botswana","ir/multilingual-information-retrieval, ir/biomedical-information-extraction, ir/query-expansion",,,,CLEF-eHealth-2018-IR-task,
"Mathieu Nassif, Christoph Treude, Martin Robillard – McGill University School of Computer Science, Montreal, Quebec, Canada",Automatically Categorizing Software Technologies,https://ieeexplore.ieee.org/abstract/document/8359344,papers,20180101Z00:00:00,,"Informal language and the absence of a standard taxonomy for software technologies make it difficult to reliably analyze technology trends on discussion forums and other on-line venues. We propose an automated approach called Witt for the categorization of software technology (an expanded version of the hypernym discovery problem). Witt takes as input a phrase describing a software technology or concept and returns a general category that describes it (e.g., integrated development environment), along with attributes that further qualify it (commercial, php, etc.). By extension, the approach enables the dynamic creation of lists of all technologies of a given type (e.g., web application frameworks). Our approach relies on Stack Overflow and Wikipedia, and involves numerous original domain adaptations and a new solution to the problem of normalizing automatically-detected hypernyms. We compared Witt with six independent taxonomy tools and found that, when applied to software terms, Witt demonstrated better coverage than all evaluated alternate solutions, without a corresponding degradation in false positive rate.","McGill University School of Computer Science, Montreal, Quebec, Canada","nlp/semantics, ontology extraction, ir/information-extraction","All these approaches work by mining large text corpora. Among the latest such techniques is the WebIsA Database [32] from the Web Data Commons project, which extracts hypernyms from CommonCrawl,¹ a corpusof over 2.1 billion web pages. In contrast to these previous works, our method onlyrequires Stack Overflow tag information data and targeted Wikipedia searches. It creates a structure that links a single term to an attributed category that describes the term.",,,,WDC-WebIsADb
"Rosa Navarrete, Sergio Luján Mora – Universidad de Alicante, Spain",A Quantitative Analysis of the Use of Microdata for Semantic Annotations on Educational Resources,http://rua.ua.es/dspace/handle/10045/73711,papers,20180101Z00:00:00,,,"Universidad de Alicante, Spain","semantic web, structured data, microdata","This quantitative analysis was conducted on datasets extracted from the Common Crawl Corpus [17], as it is the largest corpus of web crawl. The datasets containing structured data were extracted by the Web Data Commons (WDC) project [18] and are available for public use. Two datasets were considered: the first, from December 2014, with 2.01 billion pages, of which 620 million pages correspond to structured data; and the second, from November 2015, with 1.77 billion pages, of which 541 million pages correspond to structured data.",,,,WebDataCommons
"Matteo Negri, Marco Turchi, Rajen Chatterjee, Nicola Bertoldi – Fondazione Bruno Kessler, Trento, Italy; University of Trento, Italy",eSCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing,https://arxiv.org/abs/1803.07274,papers,20180101Z00:00:00,,,"Fondazione Bruno Kessler, Trento, Italy; University of Trento, Italy",nlp/machine-translation,"A widely used resource, described in (Junczys-Dowmunt and Grundkiewicz, 2016), was included in the training set of the winning (and almost all) submissions to the last two English–German rounds of the APE task at WMT (IT domain). It consists of 4.3 million instances created by first filtering a subset of IT-related sentences from the German Common Crawl corpus⁶, and then by using two English–German and German–English PBMT systems trained on in-domain IT corpora for a round-trip translation of the selected sentences (De → En → De).",,,WMT-13-translation-task-common-crawl-corpus,
"Dávid Márk Nemeskey, András Kornai – HAS Institute of Computer Science, Budapest, Hungary",Emergency vocabulary,https://link.springer.com/article/10.1007%2Fs10796-018-9843-x,papers,20180101Z00:00:00,,,"HAS Institute of Computer Science, Budapest, Hungary","nlp/vocabulary-extraction, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Phuc Nguyen, Khai Nguyen, Ryutaro Ichise, Hideaki Takeda – SOKENDAI (The Graduate University for Advanced Studies) Shonan Village, Hayama, Kanagawa, Japan; National Institute of Informatics, Tokyo, Japan",EmbNum: Semantic labeling for numerical values with deep metric learning,https://arxiv.org/abs/1807.01367,papers,20180101Z00:00:00,,,"SOKENDAI (The Graduate University for Advanced Studies) Shonan Village, Hayama, Kanagawa, Japan; National Institute of Informatics, Tokyo, Japan",,"In a study of Lehmberg et al., 233 million tables were extracted from the July 2015 version of the Common Crawl [...]",,,,WDCWebTables
"Xing Niu, Michael Denkowski, Marine Carpuat – University of Maryland; Amazon.com, Inc.",Bi-Directional Neural Machine Translation with Synthetic Parallel Data,https://arxiv.org/pdf/1805.11213.pdf,papers,20180101Z00:00:00,,,"University of Maryland; Amazon.com, Inc.",nlp/machine-translation,,,,,
"Takuya Ohshima, Motomichi Toyama – Keio University, Yokohama, Kanagawa, Japan",SDC: structured data collection by yourself,https://dl.acm.org/citation.cfm?id=3200849,papers,20180101Z00:00:00,,,"Keio University, Yokohama, Kanagawa, Japan","web-crawling, semantic web, structured data",,,,,WebDataCommons
"Myle Ott, Michael Auli, David Granger, Marc'Aurelio Ranzato – Facebook AI Research, USA",Analyzing uncertainty in neural machine translation,https://arxiv.org/abs/1803.00047,papers,20180101Z00:00:00,,,"Facebook AI Research, USA","cc-cited-not-used, nlp/machine-translation",,,,,
"Abel L. Peirson Peirson, E. Meltem Tolunay – Stanford University, USA",Dank Learning: Generating Memes Using Deep Neural Networks,https://arxiv.org/abs/1806.04510,papers,20180101Z00:00:00,,,"Stanford University, USA","nlp/text-generation, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Christian S. Perone, Roberto Silveira, Thomas S. Paula – Universitat Politècnica de Catalunya, Barcelona, Spain",Evaluation of sentence embeddings in downstream and linguistic probing tasks,https://arxiv.org/abs/1806.06259,papers,20180101Z00:00:00,,,"Universitat Politècnica de Catalunya, Barcelona, Spain","nlp/word-embeddings, nlp/sentence-embeddings",,,,"fasttext-word-embeddings, GloVe-word-embeddings",
"Matthias Petri, Alistair Moffat – University of Melbourne, Australia",Compact inverted index storage using general-purpose compression libraries,http://dx.doi.org/10.1002/spe.2556,papers,20180101Z00:00:00,"index compression, inverted index, web search","Efficient storage of large inverted indexes is one of the key technologies that support current web search services. Here we re-examine mechanisms for representing document-level inverted indexes and within-document term frequencies, including comparing specialized methods developed for this task against recent fast implementations of general-purpose adaptive compression techniques. Experiments with the Gov2-URL collection and a large collection of crawled news stories show that standard compression libraries can provide compression effectiveness as good as or better than previous methods, with decoding rates only moderately slower than reference implementations of those tailored approaches. This surprising outcome means that high-performance index compression can be achieved without requiring the use of specialized implementations.","University of Melbourne, Australia","information-retrieval/search-engine, information-retrieval/inverted-index","We also develop (and make freely available) a new IR test collection based on the News sub-collection of the Common Crawl∗∗. The News sub-collection provides daily crawls of news websites in many languages. We refer to this collection as CC-NEWS-URL. We provide all scripts to download the freely available source WARC files from Amazon AWS and process them using Apache Tika and Apache Lucene in a consistent manner. The resulting consistency enables researchers to perform experiments on exactly the collection in their experiments, and improves comparability of results between different rounds of experimentation. For example, the number of terms reported for the GOV2-URL collection ranges from 18 million up to 48 million, preventing fair and direct comparison between results reported in different papers. The number of WARC files in CC-NEWS-URL increases each day, and hence we specify the collection using: (1) a date range; and (2) a language filter. For example, in this work, we utilize the CC-NEWS-20160901-2017028-EN collection which uses all English language news sources (as identified by Apache Tika) from 01/09/2016 up until and including 28/02/2017, that is, a six month crawl period that contains 7,508,082 documents, 26,240,031 unique terms and 4,457,492,131 postings. Currently the CC-NEWS-URL collection grows by roughly 50,000 English documents per day. This exact parsing can be reproduced by the scripts provided at https://github.com/mpetri/rlz-invidx and https://github.com/mpetri/TikaLuceneWarc, with raw postings lists stored in the popular “ds2i” format††. Document identifiers are again reassigned in URL order. We also explored a date-ordered collection based on the same source data, and obtained – method-for-method – uniformly weaker compression outcomes than for URL-sorted, in part because many of the URLs contain dates encoded in them anyway.",CC-NEWS,,,
"Mohammad Taher Pilehvar, Dimitri Kartsaklis, Victor Prokhorov, Nigel Collier – University of Cambridge, United Kingdom",Card-660: Cambridge Rare Word Dataset-a Reliable Benchmark for Infrequent Word Representation Models,https://arxiv.org/abs/1808.09308,papers,20180101Z00:00:00,,,"University of Cambridge, United Kingdom","linguistics, nlp/semantics, nlp/word-embeddings, lexicography",,,,GloVe-word-embeddings,
"Shrimai Prabhumoye, Yulia Tsvetkov, Ruslan Salakhutdinov, Alan W Black – Carnegie Mellon University, Pittsburgh, PA, USA",Style Transfer Through Back-Translation,https://arxiv.org/abs/1804.09000,papers,20180101Z00:00:00,,,"Carnegie Mellon University, Pittsburgh, PA, USA",nlp/machine-translation,,,,WMT-13-translation-task-common-crawl-corpus,
"Roy Raanani, Russell Levy, Micha Yochanan Beakstone, Dominik Facher – Affectlayer Inc",Analyzing conversations to automatically identify product feature requests,https://patents.google.com/patent/US20180183930A1/en,papers,20180101Z00:00:00,,,Affectlayer Inc,"nlp/text-corpora, cc-cited-not-used, patent","At the same time, natural language processing (NLP) approaches to both topic modeling and world-knowledge modeling, have become much more efficient due to the availability of large, freely accessible natural language corpora (e.g., CommonCrawl), ...",,,,
"Roy Raanani, Russell Levy, Micha Yochanan Breadstone – Affectlayer Inc",Automatic generation of playlists from conversations,https://patents.google.com/patent/US20180046710A1/en,papers,20180101Z00:00:00,,,Affectlayer Inc,"nlp/text-corpora, cc-cited-not-used, patent","At the same time, natural language processing (NLP) approaches to both topic modeling and world-knowledge modeling, have become much more efficient due to the availability of large, freely accessible natural language corpora (e.g., CommonCrawl), ...",,,,
"Roy Raanani, Russell Levy, Micha Yochanan Breakstone – Affectlayer Inc",Coordinating voice calls between representatives and customers to influence an outcome of the call,https://patents.google.com/patent/US9900436B2/en,papers,20180101Z00:00:00,,,Affectlayer Inc,"nlp/text-corpora, cc-cited-not-used, patent","At the same time, natural language processing (NLP) approaches to both topic modeling and world-knowledge modeling, have become much more efficient due to the availability of large, freely accessible natural language corpora (e.g., CommonCrawl), ...",,,,
"Roy Raanani, Russell Levy, Micha Yochanan Breakstone – Affectlayer Inc",Modeling voice calls to improve an outcome of a call between a representative and a customer,https://patents.google.com/patent/US20180309873A1/en,papers,20180101Z00:00:00,,,Affectlayer Inc,"nlp/text-corpora, cc-cited-not-used, patent","At the same time, natural language processing (NLP) approaches to both topic modeling and study world-knowledge modeling, have become much more efficient due to the availability of large, freely accessible natural language corpora (e.g., CommonCrawl), ...",,,,
"Roy Raanani, Russell Levy, Micha Yochanan Breakstone, Dominik Facher – Affectlayer Inc",Analyzing conversations to automatically identify action items,https://patents.google.com/patent/US20180122383A1/en,papers,20180101Z00:00:00,,,Affectlayer Inc,"nlp/text-corpora, cc-cited-not-used, patent","At the same time, natural language processing (NLP) approaches to both topic modeling and world-knowledge modeling, have become much more efficient due to the availability of large, freely accessible natural language corpora (e.g., CommonCrawl), ...",,,,
"Roy Raanani, Russell Levy, Micha Yochanan Breakstone, Dominik Facher – Affectlayer Inc",Analyzing conversations to automatically identify customer pain points,https://patents.google.com/patent/US20180181561A1/en,papers,20180101Z00:00:00,,,Affectlayer Inc,"nlp/text-corpora, cc-cited-not-used, patent","At the same time, natural language processing (NLP) approaches to both topic modeling and world-knowledge modeling, have become much more efficient due to the availability of large, freely accessible natural language corpora (e.g., CommonCrawl), ...",,,,
"Roy Raanani, Russell Levy, Micha Yochanan Breakstone, Dominik Facher – Affectlayer Inc",Analyzing conversations to automatically identify product features that resonate with customers,https://patents.google.com/patent/US20180183930A1/en,papers,20180101Z00:00:00,,,Affectlayer Inc,"nlp/text-corpora, cc-cited-not-used, patent","At the same time, natural language processing (NLP) approaches to both topic modeling and world-knowledge modeling, have become much more efficient due to the availability of large, freely accessible natural language corpora (e.g., CommonCrawl), ...",,,,
"Roy Raanani, Russell Levy, Dominik Facher, Micha Yochanan Breakstone – Affectlayer Inc",Automatic pattern recognition in conversations,http://www.freepatentsonline.com/10110743.html,papers,20180101Z00:00:00,,,Affectlayer Inc,"nlp/text-corpora, cc-cited-not-used, patent","At the same time, natural language processing (NLP) approaches to both topic modeling and world-knowledge modeling, have become much more efficient due to the availability of large, freely accessible natural language corpora (e.g., CommonCrawl), ...",,,,
"Roy Raanani, Russell Levy, Dominik Facher, Micha Yochanan Breakstone – Affectlayer Inc",Analyzing conversations to automatically identify deals at risk,https://patents.google.com/patent/US10133999B2/en,papers,20180101Z00:00:00,,,Affectlayer Inc,"nlp/text-corpora, cc-cited-not-used, patent","At the same time, natural language processing (NLP) approaches to both topic modeling and world-knowledge modeling, have become much more efficient due to the availability of large, freely accessible natural language corpora (e.g., CommonCrawl), ...",,,,
"Jonathan Raiman, John Miller – Baidu USA LLC",Global normalized reader systems and methods,https://patents.google.com/patent/US20180300312A1/en,papers,20180101Z00:00:00,,,Baidu USA LLC,"nlp/question-answering, nlp/word-embeddings, patent","In embodiments, the 300 dimensional 8.4B token Common Crawl GloVe vectors were used. Words missing from the Common Crawl vocabulary were set to zero.",,,GloVe-word-embeddings,
"Martin Raison, Pierre-Emmanuel Mazaré, Rajarshi Das, Antoine Bordes – Facebook AI Research, Paris, France; University of Massachusetts, Amherst, USA",Weaver: Deep Co-Encoding of Questions and Documents for Machine Reading,https://arxiv.org/abs/1804.10490,papers,20180101Z00:00:00,,,"Facebook AI Research, Paris, France; University of Massachusetts, Amherst, USA","nlp/question-answering, nlp/word-embeddings, information retrieval",,,,fastText-word-embeddings,
"Petar Ristoski, Petar Petrovski, Peter Mika, Heiko Paulheim – University of Mannheim, Germany; Yahoo Labs, London, United Kingdom",A machine learning approach for product matching and categorization,https://content.iospress.com/articles/semantic-web/sw300,papers,20180101Z00:00:00,,,"University of Mannheim, Germany; Yahoo Labs, London, United Kingdom","semantic web, information extraction, microdata, linked data, data integration",,,,WDC-triples,
"Alexey Romanov, Chaitanya Shivade – University of Massachusetts Lowell, USA; IBM Almaden Research Center, San Jose, CA, USA",Lessons from Natural Language Inference in the Clinical Domain,https://arxiv.org/abs/1808.06752,papers,20180101Z00:00:00,,,"University of Massachusetts Lowell, USA; IBM Almaden Research Center, San Jose, CA, USA","nlp, natural language inference",,,,"GloVe-word-embeddings, fastText-word-embeddings",
"Amir Rosenfeld, Shimon Ullman – Weizmann Institute of Science, Rehovot, Israel",Action Classification via Concepts and Attributes,https://ieeexplore.ieee.org/abstract/document/8546184,papers,20180101Z00:00:00,,,"Weizmann Institute of Science, Rehovot, Israel","nlp/word-embeddings, ai/computer-vision, image-classification",,,,GloVe-word-embeddings,
"Nick Rossenbach, Jan Rosendahl, Yunsu Kim, Miguel Graça, Aman Gokrani, Hermann Ney – RWTH Aachen University, Germany",The RWTH Aachen University filtering system for the WMT 2018 parallel corpus filtering task,https://www.aclweb.org/anthology/W18-6487,papers,20180101Z00:00:00,,,"RWTH Aachen University, Germany","nlp/machine-translation, nlp/corpus-construction",,,,WMT-16-translation-task-common-crawl-corpus,
"Dwaipayan Roy, Debasis Ganguly, Sumit Bhatia, Srikanta Bedathur, Mandar Mitra – Indian Statistical Institute, Kolkata, India; IBM Research, Dublin, Ireland, Dublin, Ireland; IBM Research, Delhi, India, Delhi, India; Indian Institute of Technology, Delhi, Delhi, India",Using Word Embeddings for Information Retrieval: How Collection and Term Normalization Choices Affect Performance,https://dl.acm.org/citation.cfm?id=3269277,papers,20180101Z00:00:00,,,"Indian Statistical Institute, Kolkata, India; IBM Research, Dublin, Ireland, Dublin, Ireland; IBM Research, Delhi, India, Delhi, India; Indian Institute of Technology, Delhi, Delhi, India","cc-cited-not-used, nlp/word-embeddings, information-retrieval/term-normalization","In future, we plan to solidify these observations [...] as well asexperiment using large datasets (e.g. Common Crawl).",,,,
"Ethan M. Rudd, Richard Harang, Joshua Saxe – Sophos Group PLC, VA, USA",MEADE: Towards a Malicious Email Attachment Detection Engine,https://arxiv.org/abs/1804.08162,papers,20180101Z00:00:00,,"alicious email attachments are a growing delivery vector for malware. While machine learning has been successfully applied to portable executable (PE) malware detection, we ask, can we extend similar ap- proaches to detect malware across heterogeneous file types commonly found in email attachments? In this paper, we explore the feasibility of applying machine learning as a static countermeasure to detect several types of malicious email attachments including Microsoft Office documents and Zip archives. To this end, we collected a dataset of over 5 million malicious/benign Microsoft Office documents from VirusTotal for evaluation as well as a dataset of benign Microsoft Office documents from the Common Crawl corpus, which we use to provide more realistic estimates of thresholds for false positive rates on in-the-wild data. We also collected a dataset of approximately 500k malicious/benign Zip archives, which we scraped using the VirusTotal service, on which we performed a separate evaluation. We analyze predictive performance of several classifiers on each of the VirusTotal datasets using a 70/30 train/test split on first seen time, evaluating feature and classifier types that have been applied successfully in commercial antimalware products and R&D contexts. Using deep neural networks and gradient boosted decision trees, we are able to obtain ROC curves with >0.99 AUC on both Microsoft Office document and Zip archive datasets. Discussion of deployment viability in various antimalware contexts is provided.","Sophos Group PLC, VA, USA","web-science, computer-security/email-security",,,,,
"Maciej Rybinski, William Miller, Javier Del Ser, Miren Nekane Bilbao, José F. Aldana-Montes – University of Málaga, Spain; Anami Precision, San Sebastián, Spain; TECNALIA, Bizkaia, Spain; Basque Center for Applied Mathematics (BCAM), Bizkaia, Spain; University of the Basque Country (UPV/EHU), Bilbao, Spain",On the Design and Tuning of Machine Learning Models for Language Toxicity Classification in Online Platforms,https://link.springer.com/chapter/10.1007/978-3-319-99626-4_29,papers,20180101Z00:00:00,,,"University of Málaga, Spain; Anami Precision, San Sebastián, Spain; TECNALIA, Bizkaia, Spain; Basque Center for Applied Mathematics (BCAM), Bizkaia, Spain; University of the Basque Country (UPV/EHU), Bilbao, Spain","nlp/text-classification, nlp/sentiment-analysis, nlp/word-embeddings, ai/deep-learning",,,,GloVe-word-embeddings,
"Shadi Saleh, Pavel Pecina – Charles University, Czech Republic",CUNI team: CLEF eHealth Consumer Health Search Task 2018,http://ceur-ws.org/Vol-2125/paper_201.pdf,papers,20180101Z00:00:00,,,"Charles University, Czech Republic","ir/multilingual-information-retrieval, ir/biomedical-information-extraction, nlp/machine-translation","Document collection in the CLEF 2018 consumer health search task is created using CommonCrawl platform¹. First, the query set (described in Section 2.2) is submitted to Microsoft Bing APIs, and a list of domains is extracted from the top retrieved results. This list is extended by adding reliable health websites, at the end clefehealth2018_B (which we use in this work) contained 1,653 sites, after excluding non-medical websites such as news websites. After preparing the domain list, these domains are crawled and provided as an indexed collection to the participants.",,,CLEF-eHealth-2018-IR-task,
"Enrico Santus, Chris Biemann, Emmanuele Chersoni – Massachussetts Institute of Technology, USA; Universität Hamburg, Germany; Aix-Marseille University, France","BomJi at SemEval-2018 Task 10: Combining Vector-, Pattern-and Graph-based Information to Identify Discriminative Attributes",https://arxiv.org/abs/1804.11251,papers,20180101Z00:00:00,,,"Massachussetts Institute of Technology, USA; Universität Hamburg, Germany; Aix-Marseille University, France",nlp/semantics,"Thirteen features related to word and word-feature frequency were calculated on the basis of the information extracted from a corpus of 3.2B words, corresponding to about 20\% of the Common Crawl.",??,,GloVe-word-embeddings,
Prathusha Kameswara Sarma – University of Wisconsin-Madison,Learning Word Embeddings for Data Sparse and Sentiment Rich Data Sets,http://www.aclweb.org/anthology/N18-4007,papers,20180101Z00:00:00,,,University of Wisconsin-Madison,"nlp/semantics, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Prathusha K Sarma, YIngyu Liang, William A Sethares – University of Wisconsin-Madison",Domain Adapted Word Embeddings for Improved Sentiment Classification,https://arxiv.org/abs/1805.04576,papers,20180101Z00:00:00,,,University of Wisconsin-Madison,"nlp/sentiment-analysis, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Prathusha K Sarma, William Sethares – University of Wisconsin-Madison","Simple Algorithms For Sentiment Analysis On Sentiment Rich, Data Poor Domains.",http://www.aclweb.org/anthology/C18-1290,papers,20180101Z00:00:00,,,University of Wisconsin-Madison,nlp/sentiment-analysis,,,,,GloVe-word-embeddings
"Shigehiko Schamoni, Julian Hitschler, Stefan Riezler – Heidelberg University, Germany",A dataset and reranking method for multimodal MT of user-generated image captions,https://amtaweb.org/wp-content/uploads/2018/03/AMTA_2018_Proceedings_Research_Track.pdf#page=146,papers,20180101Z00:00:00,,,"Heidelberg University, Germany",nlp/machine-translation,,,,WMT-13-translation-task-common-crawl-corpus,
"Julian Schamper, Jan Rosendahl, Parnia Bahar, Yunsu Kim, Arne Nix, Hermann Ney – RWTH Aachen University, Germany",The RWTH Aachen University supervised machine translation systems for WMT 2018,https://www.aclweb.org/anthology/W18-6426,papers,20180101Z00:00:00,,,"RWTH Aachen University, Germany",nlp/machine-translation,,,,WMT-16-translation-task-common-crawl-corpus,
"Sebastian Schelter, Jérôme Kunegis – Technical University Berlin, Germany; University of Namur, Belgium",On the Ubiquity of Web Tracking: Insights from a Billion-Page Web Crawl,http://dx.doi.org/10.1561/106.00000014,papers,20180101Z00:00:00,,,"Technical University Berlin, Germany; University of Namur, Belgium",web-science/tracking,,,tracking-the-trackers,,
Holger Schwenk – Facebook AI Research,Filtering and Mining Parallel Data in a Joint Multilingual Space,http://arxiv.org/abs/1805.09822,papers,20180101Z00:00:00,,,Facebook AI Research,nlp/machine-translation,,,,WMT-13-translation-task-common-crawl-corpus,
"Jurica Ševa, Mario Sänger, Ulf Leser – Humboldt-Universität zu Berlin, Germany",WBI at CLEF eHealth 2018 Task 1: Language-independent ICD-10 coding using multi-lingual embeddings and recurrent neural networks,http://ceur-ws.org/Vol-2125/paper_118.pdf,papers,20180101Z00:00:00,,,"Humboldt-Universität zu Berlin, Germany","ir/multilingual-information-retrieval, ir/biomedical-information-extraction, nlp/machine-translation, nlp/word-embeddings",,,,CLEF-eHealth-2018-IR-task,
"Cory Shain, Richard Futrell, Marten van Schijndel, Edward Gibson, William Schuler – Ohio State University; MIT; Johns Hopkins University",Evidence of semantic processing difficulty in naturalistic reading,https://vansky.github.io/assets/pdf/shain_etal-2018-cuny.pdf,papers,20180101Z00:00:00,,,Ohio State University; MIT; Johns Hopkins University,"nlp, psycholinguistics",[...] using GloVe vectors [20] pretrained on the 840B word Common Crawl dataset [...],,,GloVe-word-embeddings,
"Gabi Shalev, Yossi Adi, Joseph Keshet – Bar-Ilan University, Israel",Out-of-distribution detection using multiple semantic label representations,http://papers.nips.cc/paper/7967-out-of-distribution-detection-using-multiple-semantic-label-representations,papers,20180101Z00:00:00,,,"Bar-Ilan University, Israel","nlp/semantics, nlp/word-embeddings, ai/neural-networks, ai/computer-vision, nlp/speech-recognition",,,,GloVe-word-embeddings,
"Sistla Sai Shravani, Niraj Kumar Jha, Rajlaksmi Guha – IT Kharagpur, India",A Machine Learning Approach to Correlate Emotional Intelligence and Happiness Based on Twitter Data,http://hci2018.bcs.org/prelim_proceedings/papers/Work-in-Progress%20Track/BHCI-2018_paper_115.pdf,papers,20180101Z00:00:00,,,"IT Kharagpur, India","nlp/sentiment-analysis, nlp/word-embeddings",,,,fastText-word-embeddings,
"Umutcan Şimşek, Dieter Fensel – University of Innsbruck, Austria",Intent Generation for Goal-Oriented Dialogue Systems based on Schema.org Annotations,https://arxiv.org/abs/1807.01292,papers,20180101Z00:00:00,,,"University of Innsbruck, Austria","nlp/dialogue-systems, semantic web, microformats",,,,GloVe-word-embeddings,
"Ravinder Singh, Marina Levina, Nelson Jiao, Asha Saini – DELL EMC",Using open data to predict market movements,https://education.emc.com/content/dam/dell-emc/documents/en-us/2017KS_Ravinder-Using_Open_Data_to_Predict_Market_Movements.pdf,papers,20180101Z00:00:00,,,DELL EMC,"market research, nlp, information retrieval","We found that The Register articles for specific vendors extracted from the common crawl data set are highly correlated with our reading of General Purpose Magic Quadrant position movements in time. [...] The Figure 11 : Common Crawl Data Processing Flow Diagram shows a broad overview of the steps involved in the analysis of common crawl data. Going from the bottom up it shows how the data is extracted, processed and visualized. The amount of data in each phase becomes more streamlined and, hence, the reduction in size of the data being worked on. We start with the crawl data, extract the pages of interest int o a private storage bucket, and then process it to remove unwanted words/tags. At the end, visualization tools are used to graphically display the results. These can be used to publish standard reports or customized by users to support their own analysis.",,,,
"Peter Andrew Miller Smith, Samuel Leeman-Munk, Angi Shelton, Bradford W Mott, Eric Wiebe, James Lester – North Carolina State University, Raleigh, NC, USA; SAS Institute Inc., Cary, NC, USA",A multimodal assessment framework for integrating student writing and drawing in elementary science learning,https://ieeexplore.ieee.org/abstract/document/8274912/,papers,20180101Z00:00:00,,,"North Carolina State University, Raleigh, NC, USA; SAS Institute Inc., Cary, NC, USA","nlp/word-embeddings, nlp/semantics, education, tutoring systems, student writing",,,,,
"Luca Soldaini – Georgetown University, USA",The Knowledge and Language Gap in Medical Information Seeking,https://search.proquest.com/openview/e669cd1478b33d52fa4cc71e8393c639/1,papers,20180101Z00:00:00,,,"Georgetown University, USA","ir/multilingual-information-retrieval, ir/biomedical-information-retrieval",,,,,CLEF-eHealth-2018-IR-task
"Linfeng Song, Zhiguo Wang, Mo Yu, Yue Zhang, Radu Florian, Daniel Gildea – University of Rochester, Rochester, NY, USA; IBM T.J. Watson Research Center, Yorktown Heights, NY, USA; School of Engineering, Westlake University, China",Exploring graph-structured passage representation for multi-hop reading comprehension with graph neural networks,https://arxiv.org/abs/1809.02040,papers,20180101Z00:00:00,,,"University of Rochester, Rochester, NY, USA; IBM T.J. Watson Research Center, Yorktown Heights, NY, USA; School of Engineering, Westlake University, China","nlp/word-embeddings, nlp/machine-reading, nlp/coreference-resolution, nlp/question-answering",,,,GloVe-word-embeddings,
"Samuel Spaulding, Huili Chen, Safinah Ali, Michael Kulinski, Cynthia Breazeal – Massachusetts Institute of Technology, Cambridge, MA, USA",A social robot system for modeling children's word pronunciation: socially interactive agents track,https://dl.acm.org/citation.cfm?id=3237946,papers,20180101Z00:00:00,,,"Massachusetts Institute of Technology, Cambridge, MA, USA","computer-vision, nlp/word-embeddings",,,,,GloVe-word-embeddings
"Christian Stab, Johannes Daxenberger, Chris Stahlhut, Tristan Miller, Benjamin Schiller, Christopher Tauchmann, Steffen Eger, Iryna Gurevych – Ubiquitous Knowledge Processing Lab, Department of Computer Science, Technische Universität Darmstadt, Germany",ArgumenText: Searching for Arguments in Heterogeneous Sources,http://www.aclweb.org/anthology/N18-5005,papers,20180101Z00:00:00,,,"Ubiquitous Knowledge Processing Lab, Department of Computer Science, Technische Universität Darmstadt, Germany",nlp/argument-mining,"we build upon the English part of CommonCrawl, [...] we followed Habernal et al. (2016) for de-duplication, boiler-plate removal using jusText (Pomikálek, 2011), andlanguage detection.² This left us with 400 million heterogeneous plain-text documents in English, with an overall size of 683 GiB.",,,,
"Felix Stahlberg, Adria de Gispert, Bill Byrne – University of Cambridge, United Kingdom; SDL Research, Cambridge, United Kingdom",The University of Cambridge's Machine Translation Systems for WMT18,https://arxiv.org/abs/1808.09465,papers,20180101Z00:00:00,,,"University of Cambridge, United Kingdom; SDL Research, Cambridge, United Kingdom",nlp/machine-translation,,,,WMT-13-translation-task-common-crawl-corpus,
"Chris Stahlhut – Ubiquitous Knowledge Processing Lab TU Darmstadt, Germany",Searching Arguments in German with ArgumenText,http://ceur-ws.org/Vol-2167/short7.pdf,papers,20180101Z00:00:00,,,"Ubiquitous Knowledge Processing Lab TU Darmstadt, Germany",nlp/argument-mining,,,,,
"Stergios Stergiou, Dipen Rughwani, Kostas Tsioutsiouliklis – Yahoo Research, Sunnyvale, CA, USA; Google & Yahoo Research, Mountain View, CA, USA",Shortcutting Label Propagation for Distributed Connected Components,https://dl.acm.org/citation.cfm?id=3159696,papers,20180101Z00:00:00,,,"Yahoo Research, Sunnyvale, CA, USA; Google & Yahoo Research, Mountain View, CA, USA",graph processing,,,,,
"Hanna Suominen, Liadh Kelly, Lorraine Goeuriot, Aurélie Névéol, Lionel Ramadier, Aude Robert, Evangelos Kanoulas, Rene Spijker, Leif Azzopardi, Dan Li, others – University of Turku, Turku, Finland; The Australian National University (ANU), Australia; Commonwealth Scientific and Industrial Research Organisation (CSIRO), University of Canberra, Canberra, Australia; Maynooth University, Maynooth, Ireland; Univ. Grenoble Alpes, CNRS, Grenoble, France; Université Paris-Saclay, Orsay, France; INSERM, France; University of Amsterdam, Amsterdam, Netherlands; Cochrane Netherlands and UMC Utrecht; Julius Center for Health Sciences and Primary Care, Utrecht, Netherlands; University of Strathclyde, Glasgow, UK; Queensland University of Technology, Brisbane, Australia; Vienna University of Technology, Vienna, Austria; Qatar Computing Research Institute, Doha, Qatar",Overview of the CLEF ehealth evaluation lab 2018,https://link.springer.com/chapter/10.1007/978-3-319-98932-7_26,papers,20180101Z00:00:00,,,"University of Turku, Turku, Finland; The Australian National University (ANU), Australia; Commonwealth Scientific and Industrial Research Organisation (CSIRO), University of Canberra, Canberra, Australia; Maynooth University, Maynooth, Ireland; Univ. Grenoble Alpes, CNRS, Grenoble, France; Université Paris-Saclay, Orsay, France; INSERM, France; University of Amsterdam, Amsterdam, Netherlands; Cochrane Netherlands and UMC Utrecht; Julius Center for Health Sciences and Primary Care, Utrecht, Netherlands; University of Strathclyde, Glasgow, UK; Queensland University of Technology, Brisbane, Australia; Vienna University of Technology, Vienna, Austria; Qatar Computing Research Institute, Doha, Qatar","ir/search-engine-evaluation, nlp/corpus-construction","This year we introduced clefehealth2018 corpus. This was crated by compiling Web pages of selected domains acquired from the CommonCrawl¹¹. An initial list of Websites was identified for acquisition. The list was built by submitting the CLEF 2018 base queries to the Microsoft Bing APIs (through the Azure Cognitive Services) repeatedly over a period of few weeks¹², and acquiring the URLs of the retrieved results. The domains of the URLs were then included in the list, except some domains that were excluded for decency reasons (e.g. pornhub.com). The list was further augmented by including a number of known reliable health Websites and other known unreliable health Websites, from lists previously compiled by health institutions and agencies. The corpus was divided into folders, by domain name. Each folder contained a file for each Webpage from the domain available in the CommonCrawl dump. In total, 2,021 domains were requested from the CommonCrawl dump of 2018-09¹³. Of the 2,021 domains in total, 1,903 were successfully acquired. The remaining domains were discarded due to errors, corrupted or incomplete data returned by the CommonCrawl API (a total of ten retries were attempted for each domain before giving up on a domain). Of the 1,903 crawled domains, 84 were not available in the CommonCrawl dump, and for these, a folder in the corpus exists and represents the domain that was requested; however, the folder is empty, meaning that it was not available in the dump. Note that .pdf documents were excluded from the data acquired from CommonCrawl. A complete list of domains and size of the crawl data for each domain is available at https://github.com/CLEFeHealth/CLEFeHealth2018IRtask/ blob/master/clef2018collection_listofdomains.txt. The full collection, clefehealth2018¹⁴, it contains 5,535,120 Web pages and its uncompressed size is about 480GB. In addition to the full collection, an alternative corpus named clefehealth2018_B¹⁵ was created by manually removing a number of domains that were not strictly health-related (e.g., news Websites). This subset contains 1,653 domains and its size is about 294GB, uncompressed.",CC-MAIN-2018-09,CLEF-eHealth-2018-IR-task,,
"Shabnam Tafreshi, Mona Diab – George Washington University",Emotion Detection and Classification in a Multigenre Corpus with Joint Multi-Task Deep Learning,http://www.aclweb.org/anthology/C18-1246,papers,20180101Z00:00:00,,,George Washington University,"nlp/emotion-detection, nlp/word-embeddings","Our results indicate that common crawl corpus with 2 million words, trained using fastText model has the most word coverage among these genres.",,,"GloVe-word-embeddings, fastText-word-embeddings",
"Nicolas Tempelmeier, Elena Demidova, Stefan Dietze – Leibniz Universität Hannover, Germany",Inferring missing categorical information in noisy and sparse web markup,https://arxiv.org/abs/1803.00446,papers,20180101Z00:00:00,,,"Leibniz Universität Hannover, Germany","semantic web, linked data",,,,WDC-triples,
"Brian Thompson, Huda Khayrallah, Antonios Anastasopoulos, Arya McCarthy, Kevin Duh, Rebecca Marvin, Paul McNamee, Jeremy Gwinnup, Tim Anderson, Philipp Koehn – Johns Hopkins University, USA; University of Notre Dame, France; Air Force Research Laboratory, USA",Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation,https://arxiv.org/abs/1809.05218,papers,20180101Z00:00:00,,,"Johns Hopkins University, USA; University of Notre Dame, France; Air Force Research Laboratory, USA",nlp/machine-translation,,,,WMT-16-translation-task-common-crawl-corpus,
"Henry S. Thompson, Jian Tong – University of Edinburgh, United Kingdom",Can Common Crawl reliably track persistent identifier (PID) use over time?,https://arxiv.org/abs/1802.01424,papers,20180101Z00:00:00,,,"University of Edinburgh, United Kingdom",web-science,,,,,
"Swapna Buccapatnam Tirumala, Ashish Jagmohan, Elham Khabiri, Ta-Hsin Li, Matthew Daniel Riemer, Vadim Sheinin, Aditya Vempaty – International Business Machines Corp.",Facilitating mapping of control policies to regulatory documents,https://patents.google.com/patent/US20180137107A1/en,papers,20180101Z00:00:00,,,International Business Machines Corp.,"patent, cc-cited-not-used","The global corpora [203] can comprise a general internet-based collection of texts derived from various sources (e.g., GUTENBERG®, REUTERS®, COMMON CRAWL®, and/or GOOGLE NEWS®).",,,,
"Maksim Tkachenko, Chong Cher Chia, Hady Lauw – Singapore Management University, Singapore",Searching for the X-Factor: Exploring Corpus Subjectivity for Word Embeddings,http://www.aclweb.org/anthology/P18-1112,papers,20180101Z00:00:00,,,"Singapore Management University, Singapore","nlp/sentiment-analysis, nlp/word-embeddings, cc-cited-not-used",,,,,
"Marcus Tober, Daniela Neumann – Searchmetrics GmbH",Creation and optimization of resource contents,https://patents.google.com/patent/US20180096067A1/en,papers,20180101Z00:00:00,,,Searchmetrics GmbH,"patent, cc-cited-not-used","The crawler module [310] may automatically crawl a network and acquire contents from one or more resources in the network, acquire the contents from an open repository of web crawl data such as CommonCrawl.org.",,,,
"Melanie Tosik, Antonio Mallia, Kedar Gangopadhyay – New York University",Debunking Fake News One Feature at a Time,https://arxiv.org/abs/1808.02831,papers,20180101Z00:00:00,,,New York University,"nlp, text classification",Cosine similarity between averaged headline/body Common Crawl vectors,,,?? GloVe-word-embeddings,
"Ke Tran, Yonatan Bisk – University of Amsterdam; University of Washington",Inducing Grammars with and for Neural Machine Translation,https://arxiv.org/abs/1805.10850,papers,20180101Z00:00:00,,,University of Amsterdam; University of Washington,"nlp/maschine-translation, nlp/syntax, nlp/grammar-learning, nlp/dependency-grammar",,,,,
"Trieu H Trinh, Quoc V Le – Google Brain",A Simple Method for Commonsense Reasoning,https://arxiv.org/abs/1806.02847,papers,20180101Z00:00:00,,"Commonsense reasoning is a long-standing challenge for deep learning. For example, it is difficult to use neural networks to tackle the Winograd Schema dataset [ 1]. In this paper, we present a simple method for commonsense reasoning with neural networks, using unsupervised learning. Key to our method is the use of language models, trained on a massive amount of unlabled data, to score multiple choice questions posed by commonsense reasoning tests. On both Pronoun Disambiguation and Winograd Schema challenges, our models outperform previous state-of-the-art methods by a large margin, without using expensive annotated knowledge bases or hand-engineered features. We train an array of large RNN language models that operate at word or character level on LM-1-Billion, CommonCrawl, SQuAD, Gutenberg Books, and a customized corpus for this task and show that diversity of training data plays an important role in test performance. Further analysis also shows that our system successfully discovers important features of the context that decide the correct answer, indicating a good grasp of commonsense knowledge.",Google Brain,"ai/deep-learning, nlp/language-model","In particular, we aggregate documents from the CommonCrawl dataset that has the most overlapping n-grams with the questions. [...] We name this dataset STORIES since most of the constituent documents take the form of a story with long chain of coherent events.",,CC-Stories,,
"Dmitry Ustalov, Alexander Panchenko, Chris Biemann, Simone Paolo Ponzetto – University of Mannheim, Germany; University of Hamburg, Germany; Skolkovo Institute of Science and Technology, Moskva, Russia",Watset: local-global graph clustering with applications in sense and frame induction,https://arxiv.org/abs/1808.06696,papers,20180101Z00:00:00,,,"University of Mannheim, Germany; University of Hamburg, Germany; Skolkovo Institute of Science and Technology, Moskva, Russia","nlp/dependency-parsing, nlp/semantics, nlp/synonymy, nlp/frames-semantics, graph-clustering, web-mining","For the evaluation purposes, we operate on the intersection of triples from DepCC and FrameNet.",,,depcc,
"Dmitry Ustalov, Alexander Panchenko, Chris Biemann, Simone Paolo Ponzetto – University of Mannheim, Germany; University of Hamburg, Germany",Unsupervised sense-aware hypernymy extraction,https://arxiv.org/abs/1809.06223,papers,20180101Z00:00:00,,,"University of Mannheim, Germany; University of Hamburg, Germany","nlp/semantics, nlp/hypernymy, web-mining",,,,,WDC-WebIsADb
"Dmitry Ustalov, Alexander Panchenko, Andrei Kutuzov, Chris Biemann, Simone Paolo Ponzetto – University of Mannheim, Germany; University of Hamburg, Germany; University of Oslo, Norway",Unsupervised semantic frame induction using triclustering,https://arxiv.org/abs/1805.04715,papers,20180101Z00:00:00,,,"University of Mannheim, Germany; University of Hamburg, Germany; University of Oslo, Norway","nlp/dependency-parsing, nlp/semantics, nlp/synonymy, nlp/frames-semantics, graph-clustering, web-mining","In our evaluation, we use triple frequencies from the DepCC dataset (Panchenkoet al., 2018) , which is a dependency-parsed version of the Common Crawl corpus, and the standard 300-dimensional word embeddings model trained on the Google News corpus (Mikolovet al., 2013). [...] For the evaluation purposes, we operate on the intersection of triples from DepCC and FrameNet.",,,depcc,
"Hal Varian – National Bureau of Economic Research, Cambridge, MA, USA","Artificial intelligence, economics, and industrial organization",https://www.nber.org/papers/w24839,papers,20180101Z00:00:00,,"Machine learning (ML) and artificial intelligence (AI) have been around for many years. However, in the last 5 years, remarkable progress has been made using multilayered neural networks in diverse areas such as image recognition, speech recognition, and machine translation. AI is a general purpose technology that is likely to impact many industries. In this chapter I consider how machine learning availability might affect the industrial organization of both firms that provide AI services and industries that adopt AI technology. My intent is not to provide an extensive overview of this rapidly-evolving area, but instead to provide a short summary of some of the forces at work and to describe some possible areas for future research.","National Bureau of Economic Research, Cambridge, MA, USA",economy,,,,,
"Vivek Vinayan, Kumar M Anand, K P Soman – Amrita School of Engineering, India",AmritaNLP at SemEval-2018 Task 10: Capturing discriminative attributes using convolution neural network over global vector representation.,http://www.aclweb.org/anthology/S18-1166,papers,20180101Z00:00:00,,,"Amrita School of Engineering, India","nlp/semantics, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Yogarshi Vyas, Xing Niu, Marine Carpuat – Department of Computer Science, University of Maryland",Identifying Semantic Divergences in Parallel Text without Annotations,https://arxiv.org/abs/1803.11112,papers,20180101Z00:00:00,,,"Department of Computer Science, University of Maryland",nlp/machine-translation,,,,{?Ngrams-LMs-2013},
"Changhan Wang, Kyunghyun Cho, Douwe Kiela – Facebook AI Research; New York University",Code-Switched Named Entity Recognition with Embedding Attention,http://www.aclweb.org/anthology/W18-3221,papers,20180101Z00:00:00,,,Facebook AI Research; New York University,"nlp/named-entity-recognition, nlp/word-embeddings",,,,fastText-word-embeddings,
"Renzhi Wang, Mizuho Iwaihara – Graduate School of Information, Production and Systems, Waseda University Japan",Detection of mergeable Wikipedia articles based on overlapping topics,db-event.jpn.org/deim2018/data/papers/157.pdf,papers,20180101Z00:00:00,,,"Graduate School of Information, Production and Systems, Waseda University Japan","nlp/word-embeddings, ir/duplicate-detection",,,,GloVe-word-embeddings,
"Mingxuan Wang, Jun Xie, Zhixing Tan, Jinsong Su, Deyi Xiong, Chao Bian – Mobile Internet Group, Tencent Technology Co., Ltd; Xiamen University, China; Soochow University, China",Neural Machine Translation with Decoding History Enhanced Attention,https://www.aclweb.org/anthology/C18-1124,papers,20180101Z00:00:00,,,"Mobile Internet Group, Tencent Technology Co., Ltd; Xiamen University, China; Soochow University, China","nlp/machine-translation, cc-cited-not-used",,,,,
"Zhuxiaona Wei, Thuan Nguyen, Iat Chan, Kenny M Liou, Helin Wang, Houchang Lu – Baidu USA LLC",Systems and methods for improved user interface,https://patents.google.com/patent/US20180011688A1/en,papers,20180101Z00:00:00,,,Baidu USA LLC,"patent, ir/user-interface","For English, in embodiments, the language model is a Kneser-Ney smoothed 5-gram model with pruning that is trained using the KenLM toolkit on cleaned text from the Common Crawl Repository. The vocabulary is the most frequently used 400,000 words from 250 million lines of text, which produces a language model with about 850 million n-grams.",,,,
"John Wieting, Kevin Gimpel – Carnegie Mellon University, Pittsburgh, PA, USA; Toyota Technological Institute at Chicago, IL, USA",Paranmt-50m: Pushing the limits of paraphrastic sentence embeddings with millions of machine translations,http://www.aclweb.org/anthology/P18-1042,papers,20180101Z00:00:00,,,"Carnegie Mellon University, Pittsburgh, PA, USA; Toyota Technological Institute at Chicago, IL, USA","nlp/machine-translation, nlp/sentence-paraphrase, nlp/sentence-embeddings",,,WMT-16-translation-task-common-crawl-corpus,,
"Genta Indra Winata, Chien-Sheng Wu, Andrea Madotto, Pascale Fung – Hong Kong University of Science and Technology, Hong Kong",Bilingual Character Representation for Efficiently Addressing Out-of-Vocabulary Words in Code-Switching Named Entity Recognition,https://arxiv.org/abs/1805.12061,papers,20180101Z00:00:00,,,"Hong Kong University of Science and Technology, Hong Kong","nlp/named-entity-recognition, nlp/word-embeddings",,,,fastText-word-embeddings,
"Ziang Xie, Guillaume Genthial, Stanley Xie, Andrew Ng, Dan Jurafsky – Stanford University, USA",Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction,http://www.aclweb.org/anthology/N18-1057,papers,20180101Z00:00:00,,,"Stanford University, USA","nlp/machine-translation, nlp/grammatical-error-correction",,,,Ngrams-LMs-2013,
"Hao Xiong, Zhongjun He, Xiaoguang Hu, Hua Wu – Baidu Inc., China",Multi-channel encoder for neural machine translation,https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/16788,papers,20180101Z00:00:00,,,"Baidu Inc., China",nlp/machine-translation,,,,WMT-16-translation-task-common-crawl-corpus,
"Steven Xu, Andrew Bennett, Doris Hoogeveen, Jey Han Lau, Timothy Baldwin – University of Melbourne, Australia","Preferred Answer Selection in Stack Overflow: Better Text Representations... and Metadata, Metadata, Metadata",https://www.aclweb.org/anthology/W18-6119,papers,20180101Z00:00:00,,,"University of Melbourne, Australia","information retrieval, nlp/question-answering, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Hua Yang, Teresa Gonçalves – University of Èvora, Portugal; ZhongYuan University of Technology, Zhengzhou, China",Improving personalized consumer health search: notebook for ehealth at clef 2018,http://ceur-ws.org/Vol-2125/paper_195.pdf,papers,20180101Z00:00:00,,,"University of Èvora, Portugal; ZhongYuan University of Technology, Zhengzhou, China","ir/multilingual-information-retrieval, ir/biomedical-information-extraction, ir/query-expansion, ir/learning-to-rank, nlp/word-embeddings",,,,CLEF-eHealth-2018-IR-task,
"Thanos Yannakis, Pavlos Fafalios, Yannis Tzitzikas – University of Crete, Greece; Leibniz University of Hannover, Germany",Heuristics-based Query Reordering for Federated Queries in SPARQL 1.1 and SPARQL-LD,http://ceur-ws.org/Vol-2110/paper7.pdf,papers,20180101Z00:00:00,,,"University of Crete, Greece; Leibniz University of Hannover, Germany","semantic web, linked data, SparQL",,,,WebDataCommons,
"Evi Yulianti, Ruey-Cheng Chen, Falk Scholer, W Bruce Croft, Mark Sanderson – RMIT University, Melbourne, Australia; SEEK Ltd., Melbourne, Australia",Ranking Documents by Answer-Passage Quality,http://marksanderson.org/publications/my_papers/SIGIR2018a.pdf,papers,20180101Z00:00:00,,,"RMIT University, Melbourne, Australia; SEEK Ltd., Melbourne, Australia","information retrieval, nlp/question-answering, cc-cited-not-used",,,,,
"Siwar Zayani, Nesrine Ksentini, Mohamed Tmar, Faiez Gargouri – University of Sfax, Tunisia",Miracl at clef 2018: Consumer health search task,http://ceur-ws.org/Vol-2125/paper_141.pdf,papers,20180101Z00:00:00,,,"University of Sfax, Tunisia","ir/multilingual-information-retrieval, ir/biomedical-information-extraction, ir/query-expansion",,,,CLEF-eHealth-2018-IR-task,
"Neil Zeghidour, Qiantong Xu, Vitaliy Liptchinsky, Nicolas Usunier, Gabriel Synnaeve, Ronan Collobert – Facebook A.I. Research, Paris, France; Facebook A.I. Research, New York & Menlo Park, USA; CoML, ENS/CNRS/EHESS/INRIA/PSL Research University, Paris, France",Fully convolutional speech recognition,https://arxiv.org/abs/1812.06864,papers,20180101Z00:00:00,,,"Facebook A.I. Research, Paris, France; Facebook A.I. Research, New York & Menlo Park, USA; CoML, ENS/CNRS/EHESS/INRIA/PSL Research University, Paris, France",nlp/speech-recognition,"(12k training hours AM, common crawl LM)",,,??,
"Rowan Zellers, Yonatan Bisk, Roy Schwartz, Yejin Choi – University of Washington, USA",Swag: A large-scale adversarial dataset for grounded commonsense inference,https://arxiv.org/abs/1808.05326,papers,20180101Z00:00:00,,,"University of Washington, USA","ai/reasoning, nlp/text-generation, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Meilin Zhan, Roger Levy – Massachusetts Institute of Technology, USA",Comparing Theories of Speaker Choice Using a Model of Classifier Production in Mandarin Chinese,http://www.aclweb.org/anthology/N18-1181,papers,20180101Z00:00:00,,,"Massachusetts Institute of Technology, USA","nlp/syntax, nlp/corpus-lingustics, nlp/paraphrasing",,,,,WMT-13-translation-task-common-crawl-corpus
"Yunming Zhang, Mengjiao Yang, Riyadh Baghdadi, Shoaib Kamil, Julian Shun, Saman P. Amarasinghe – MIT CSAIL; Adobe Research",GraphIt - A High-Performance DSL for Graph Analytics,http://arxiv.org/abs/1805.00923,papers,20180101Z00:00:00,,,MIT CSAIL; Adobe Research,graph-processing,,,,WDC-hyperlinkgraph,
"Pengqing Zhang, Yuexian Hou, Zhan Su, Yi Su – Tianjin University, China",Two-Step Multi-factor Attention Neural Network for Answer Selection,https://link.springer.com/chapter/10.1007/978-3-319-97304-3_50,papers,20180101Z00:00:00,,,"Tianjin University, China","nlp/answer-selection, ai/neural-networks, nlp/word-embeddings",,,,GloVe-word-embeddings,
"Ji Zhang, Leonard Tan, Xiaohui Tao, Xiaoyao Zheng, Yonglong Luo, Jerry Chun-Wei Lin – University of Southern Queensland, Australia; Anhui Normal University, Wuhu, China; Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China",SLIND: Identifying Stable Links in Online Social Networks,https://link.springer.com/chapter/10.1007/978-3-319-91458-9_54,papers,20180101Z00:00:00,,,"University of Southern Queensland, Australia; Anhui Normal University, Wuhu, China; Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China","web-science/hyperlinkgraph, web-science/social-networks","The dataset chosen for this study, as well as for the demo, was crawled from Facebook and obtained from the repositories of the Common Crawl (August 2016).",CC-MAIN-2016-36,,,
"Ji Zhang, Xiaohui Tao, Leonard Tan, Jerry Chun-Wei Lin, Hongzhou Li, Liang Chang – University of Southern Queensland, Toowoomba, Australia; Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China; Guilin University of Electronic Technology, Guilin, China; Guilin University of Electronic Technology, Guilin, China",On Link Stability Detection for Online Social Networks,https://link.springer.com/chapter/10.1007/978-3-319-98809-2_20,papers,20180101Z00:00:00,"link stability, graph theory, online social networks",,"University of Southern Queensland, Toowoomba, Australia; Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China; Guilin University of Electronic Technology, Guilin, China; Guilin University of Electronic Technology, Guilin, China","graph-processing, social networks","Since the social network we obtain from the repositories of common crawl contains missing links and partial information, stochastic estimations are …",,,,
"Biao Zhang, Deyi Xiong, Jinsong Su – Xiamen University, China; Soochow University, China",Neural Machine Translation with Deep Attention,https://ieeexplore.ieee.org/abstract/document/8493282,papers,20180101Z00:00:00,,,"Xiamen University, China; Soochow University, China",nlp/machine-translation,,,,,
"Biao Zhang, Deyi Xiong, Jinsong Su, Qian Lin, Huiji Zhang – Xiamen University, China; Soochow University, China; Xiamen Meiya Pico information Co., Ltd. Xiamen, China",Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated Recurrent Networks,https://arxiv.org/abs/1810.12546,papers,20180101Z00:00:00,,,"Xiamen University, China; Soochow University, China; Xiamen Meiya Pico information Co., Ltd. Xiamen, China","nlp/machine-translation, cc-cited-not-used",,,,,