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Ahad Rana – Common Crawl | Common Crawl – Building an open web-scale crawl using Hadoop | https://www.slideshare.net/hadoopusergroup/common-crawlpresentation | papers | 20100101Z00:00:00 | null | null | Common Crawl | web-crawling, big data, Hadoop | null | null | null | null | null |
Jeffrey Pennington, Richard Socher, Christopher D. Manning – Stanford University, California, USA | GloVe: Global vectors for word representation | https://aclanthology.org/D14-1162.pdf | papers | 20140101Z00:00:00 | null | null | Stanford University, California, USA | nlp/word-embeddings | We trained our model on five corpora of varying sizes: [...] and on 42 billion tokens of web data, from Common Crawl⁵ [⁵ To demonstrate the scalability of the model, we also trained it on a much larger sixth corpus, containing 840 billion tokens of web data, but in this case we did not lowercase the vocabulary, so the results are not directly comparable.]. | null | null | null | null |
Robert Meusel, Sebastiano Vigna, Oliver Lehmberg, Christian Bizer – University of Mannheim, Germany; Università degli Studi di Milano, Italy | The Graph Structure in the Web – Analyzed on Different Aggregation Levels | https://pdfs.semanticscholar.org/b5d5/88298e6845b4bfd40ea779ce21e628239ef3.pdf | papers | 20150101Z00:00:00 | null | null | University of Mannheim, Germany; Università degli Studi di Milano, Italy | web-science/hyperlinkgraph | null | null | null | null | null |
Alex Stolz, Martin Hepp – Universitaet der Bundeswehr Munich, Germany | Towards Crawling the Web for Structured Data: Pitfalls of Common Crawl for E-Commerce | http://ceur-ws.org/Vol-1426/paper-04.pdf | papers | 20150101Z00:00:00 | null | null | Universitaet der Bundeswehr Munich, Germany | nlp/corpus-representativeness, semantic web, microdata, e-commerce | null | null | null | null | null |
Julian Eberius, Maik Thiele, Katrin Braunschweig, Wolfgang Lehner – Technische Universität Dresden, Germany | Top-k Entity Augmentation Using Consistent Set Covering | https://www.semanticscholar.org/paper/Top-k-entity-augmentation-using-consistent-set-Eberius-Thiele/a554fe7c49837e2d2d995e00fd3b62a6ca5650f2 | papers | 20150101Z00:00:00 | null | null | Technische Universität Dresden, Germany | semantic web, web tables, web mining | To enable repeatability we publish the implementation², but also include the web table corpus used for the evaluation³. This corpus contains 100M Web tables extracted from a publicly available Web crawl⁴ [4: http://commoncrawl.org] | null | {DresdenWebTableCorpus} | null | null |
Ivan Habernal, Omnia Zayed, Iryna Gurevych – University of Darmstadt, Germany | C4Corpus: Multilingual Web-Size Corpus with Free License | http://www.lrec-conf.org/proceedings/lrec2016/pdf/388_Paper.pdf | papers | 20160101Z00:00:00 | null | null | University of Darmstadt, Germany | nlp/corpus-construction, legal/copyright, license/creative-commons, nlp/boilerplate-removal, ir/duplicate-detection | null | null | {DKPro-C4} | null | null |
Roland Schäfer – Freie Universität Berlin, Germany | CommonCOW: Massively Huge Web Corpora from CommonCrawl Data and a Method to Distribute them Freely under Restrictive EU Copyright Laws | http://rolandschaefer.net/?p=994 | papers | 20160101Z00:00:00 | null | null | Freie Universität Berlin, Germany | nlp/corpus-construction, legal/copyright | null | null | {CommonCOW} | null | null |
Roland Schäfer – Freie Universität Berlin, Germany | Accurate and Efficient General-purpose Boilerplate Detection for Crawled Web Corpora | https://doi.org/10.1007/s10579-016-9359-2 | papers | 20170101Z00:00:00 | null | null | Freie Universität Berlin, Germany | nlp/boilerplate-removal, nlp/web-as-corpus, nlp/corpus-construction | null | null | null | null | null |
Daniel Zeman, Martin Popel, Milan Straka, Jan Hajic, Joakim Nivre, Filip Ginter, Juhani Luotolahti, Sampo Pyysalo, Slav Petrov, Martin Potthast, Francis Tyers, Elena Badmaeva, Memduh Gokirmak, Anna Nedoluzhko, Silvie Cinkova, Jan Hajic jr., Jaroslava Hlavacova, Václava Kettnerová, Zdenka Uresova, Jenna Kanerva, Stina Ojala, Anna Missilä, Christopher D. Manning, Sebastian Schuster, Siva Reddy, Dima Taji, Nizar Habash, Herman Leung, Marie-Catherine de Marneffe, Manuela Sanguinetti, Maria Simi, Hiroshi Kanayama, Valeria dePaiva, Kira Droganova, Héctor Martínez Alonso, Çağrı Çöltekin, Umut Sulubacak, Hans Uszkoreit, Vivien Macketanz, Aljoscha Burchardt, Kim Harris, Katrin Marheinecke, Georg Rehm, Tolga Kayadelen, Mohammed Attia, Ali Elkahky, Zhuoran Yu, Emily Pitler, Saran Lertpradit, Michael Mandl, Jesse Kirchner, Hector Fernandez Alcalde, Jana Strnadová, Esha Banerjee, Ruli Manurung, Antonio Stella, Atsuko Shimada, Sookyoung Kwak, Gustavo Mendonca, Tatiana Lando, Rattima Nitisaroj, Josie Li – Charles University, Czech Republic; Uppsala University, Sweden; University of Turku, Finland; University of Cambridge; Google; Bauhaus-Universität Weimar, Germany; UiT The Arctic University of Norway; University of the Basque Country, Spain; Istanbul Technical University, Turkey; Stanford University; New York University Abu Dhabi; City University of Hong Kong; Ohio State University, USA; University of Turin, Italy; University of Pisa, Italy; IBM Research; Nuance Communications; INRIA – Paris 7, France; University of Tübingen, Germany; DFKI, Germany; text & form, Germany | CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies | http://www.aclweb.org/anthology/K/K17/K17-3001.pdf | papers | 20170101Z00:00:00 | null | null | Charles University, Czech Republic; Uppsala University, Sweden; University of Turku, Finland; University of Cambridge; Google; Bauhaus-Universität Weimar, Germany; UiT The Arctic University of Norway; University of the Basque Country, Spain; Istanbul Technical University, Turkey; Stanford University; New York University Abu Dhabi; City University of Hong Kong; Ohio State University, USA; University of Turin, Italy; University of Pisa, Italy; IBM Research; Nuance Communications; INRIA – Paris 7, France; University of Tübingen, Germany; DFKI, Germany; text & form, Germany | nlp/dependency-parsing, nlp/dependency-treebank, nlp/corpus-construction | The supporting raw data was gathered from CommonCrawl, which is a publicly available web crawl created and maintained by the non-profit CommonCrawl foundation.² The data is publicly available in the Amazon cloud both as raw HTML and as plain text. It is collected from a number of independent crawls from 2008 to 2017, and totals petabytes in size. We used cld2³ as the language detection engine because of its speed, available Python bindings and large coverage of languages. Language detection was carried out on the first 1024 bytes of each plaintext document. Deduplication was carried out using hashed document URLs, a simple strategy found in our tests to be effective for coarse duplicate removal. The data for each language was capped at 100,000 tokens per a single input file. | null | conll-2017-shared-task | null | null |
Abu Bakr Soliman, Kareem Eissa, Samhaa El-Beltagy – Nile University, Egypt | AraVec: A set of Arabic Word Embedding Models for use in Arabic NLP | https://www.researchgate.net/publication/319880027_AraVec_A_set_of_Arabic_Word_Embedding_Models_for_use_in_Arabic_NLP | papers | 20170101Z00:00:00 | null | null | Nile University, Egypt | nlp/word-embeddings | we have used a subset of the January 2017 crawl dump. The dump contains more than 3.14 billion web pages and about 250 Terabytes of uncompressed content. [...] We used WET files as we were only interested in plain text for building the distributed word representation models. Due to the size of the dump, which requires massive processing power and time for handling, we only used 30\% of the data contained in it. As this subset comprises about one billion web pages (written in multiple language), we believed that it was large enough to provide sufficient Arabic Web pages from which we can build a representative word embeddings model. Here it is important to note that the Common Crawl project does not provide any technique for identifying or selecting the language of web pages to download. So, we had to download data first, and then discard pages that were not written in Arabic. The Arabic detection phase was performed using some regex commands and some NLP techniques to distinguish Arabic from other languages. After the completion of this phase we succeeded in obtaining 4,379,697 Arabic web pages which were then segmented into more than 180,000,000 paragraphs/documents for building our models. | null | null | null | null |
Tommy Dean, Ali Pasha, Brian Clarke, Casey J. Butenhoff – Virginia Polytechnic Institute and State University, USA; Eastman Chemical Company; USA | Common Crawl Mining | http://hdl.handle.net/10919/77629 | papers | 20170101Z00:00:00 | null | null | Virginia Polytechnic Institute and State University, USA; Eastman Chemical Company; USA | information retrieval, market research, business intelligence | The main goal behind the Common Crawl Mining system is to improve Eastman Chemical Company’s ability to use timely knowledge of public concerns to inform key business decisions. It provides information to Eastman Chemical Company that is valuable for consumer chemical product marketing and strategy development. Eastman desired a system that provides insight into the current chemical landscape. Information about trends and sentiment towards chemicals over time is beneficial to their marketing and strategy departments. They wanted to be able to drill down to a particular time period and look at what people were writing about certain keywords. [...] The final Common Crawl Mining system is a search engine implemented using Elasticsearch. Relevant records are identified by first analyzing Common Crawl for Web Archive (WARC) files that have a high frequency of records from interesting domains. | null | null | null | null |
Yuheng Du, Alexander Herzog, Andre Luckow, Ramu Nerella, Christopher Gropp, Amy Apon – Clemson University, USA | Representativeness of latent dirichlet allocation topics estimated from data samples with application to common crawl | http://alexherzog.net/files/IEEE_BigData_2017_Representativeness_of_LDA.pdf | papers | 20170101Z00:00:00 | null | null | Clemson University, USA | nlp/topic-modeling, nlp/corpus-representativeness | Common Crawl is a massive multi-petabyte dataset hosted by Amazon. It contains archived HTML web page data from 2008 to date. Common Crawl has been widely used for text mining purposes. Using data extracted from Common Crawl has several advantages over a direct crawl of web data, among which is removing the likelihood of a user’s home IP address becoming blacklisted for accessing a given web site too frequently. However, Common Crawl is a data sample, and so questions arise about the quality of Common Crawl as a representative sample of the original data. We perform systematic tests on the similarity of topics estimated from Common Crawl compared to topics estimated from the full data of online forums. Our target is online discussions from a user forum for automotive enthusiasts, but our research strategy can be applied to other domains and samples to evaluate the representativeness of topic models. We show that topic proportions estimated from Common Crawl are not significantly different than those estimated on the full data. We also show that topics are similar in terms of their word compositions, and not worse than topic similarity estimated under true random sampling, which we simulate through a series of experiments. Our research will be of interest to analysts who wish to use Common Crawl to study topics of interest in user forum data, and analysts applying topic models to other data samples. | null | null | null | null |
Shalini Ghosh, Phillip Porras, Vinod Yegneswaran, Ken Nitz, Ariyam Das – CSL, SRI International, Menlo Park | ATOL: A Framework for Automated Analysis and Categorization of the Darkweb Ecosystem | https://www.aaai.org/ocs/index.php/WS/AAAIW17/paper/download/15205/14661 | papers | 20170101Z00:00:00 | null | null | CSL, SRI International, Menlo Park | web-science, information retrieval, nlp/text-classification | .onion references from [...] and an open repository of (non-onion) Web crawling data, called Common Crawl (Common Crawl Foundation 2016). | null | null | null | null |
Filip Ginter, Jan Hajič, Juhani Luotolahti, Milan Straka, Daniel Zeman – Charles University, Czech Republic; University of Turku, Finland | CoNLL 2017 Shared Task - Automatically Annotated Raw Texts and Word Embeddings | http://hdl.handle.net/11234/1-1989 | papers | 20170101Z00:00:00 | null | null | Charles University, Czech Republic; University of Turku, Finland | nlp/corpus-construction, nlp/word-embeddings, nlp/syntactic-annotations, nlp/dependency-parsing | Automatic segmentation, tokenization and morphological and syntactic annotations of raw texts in 45 languages, generated by UDPipe (http://ufal.mff.cuni.cz/udpipe), together with word embeddings of dimension 100 computed from lowercased texts by word2vec (https://code.google.com/archive/p/word2vec/). [...] Note that the CC BY-SA-NC 4.0 license applies to the automatically generated annotations and word embeddings, not to the underlying data, which may have different license and impose additional restrictions. | null | conll-2017-shared-task | null | null |
Jakub Kúdela, Irena Holubová, Ondřej Bojar – Charles University, Czech Republic | Extracting Parallel Paragraphs from Common Crawl | https://ufal.mff.cuni.cz/pbml/107/art-kudela-holubova-bojar.pdf | papers | 20170101Z00:00:00 | null | null | Charles University, Czech Republic | nlp/machine-translation, nlp/corpus-construction | null | null | null | null | null |
Amir Mehmood, Hafiz Muhammad Shafiq, Abdul Waheed – UET, Lahore, Pakistan | Understanding Regional Context of World Wide Web using Common Crawl Corpus | https://www.researchgate.net/publication/321489200_Understanding_Regional_Context_of_World_Wide_Web_using_Common_Crawl_Corpus | papers | 20170101Z00:00:00 | null | null | UET, Lahore, Pakistan | web-science, webometrics | null | null | null | null | null |
Alexander Panchenko, Eugen Ruppert, Stefano Faralli, Simone Paolo Ponzetto, Chris Biemann – University of Hamburg, Germany; University of Mannheim, Germany | Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl | http://arxiv.org/abs/1710.01779 | papers | 20170101Z00:00:00 | null | null | University of Hamburg, Germany; University of Mannheim, Germany | nlp/dependency-parsing, nlp/corpus-construction | null | null | depcc | null | null |
Ajinkya Kale, Thrivikrama Taula, Sanjika Hewavitharana, Amit Srivastava – eBay Inc. | Towards semantic query segmentation | https://arxiv.org/abs/1707.07835 | papers | 20170101Z00:00:00 | null | null | eBay Inc. | ir/query-segmentation, nlp/word-embeddings, patent | null | null | null | null | null |
Kjetil Bugge Kristoffersen – University of Oslo, Norway | Common crawled web corpora: constructing corpora from large amounts of web data | http://urn.nb.no/URN:NBN:no-60569 | papers | 20170101Z00:00:00 | null | null | University of Oslo, Norway | nlp/corpus-construction, nlp/web-as-corpus | null | null | null | null | null |
David Stuart – University of Wolverhampton, Wolverhampton, UK | Open bibliometrics and undiscovered public knowledge | https://doi.org/10.1108/OIR-07-2017-0209 | papers | 20170101Z00:00:00 | null | null | University of Wolverhampton, Wolverhampton, UK | web-science/webometrics | Whether altmetrics is really any more open than traditional citation analysis is a matter of debate, although services such as Common Crawl (http://commoncrawl.org), an open repository of web crawl data, provides the opportunity for more open webometrics, [...] | null | null | null | null |
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 | null | null | University of Groningen, The Netherlands; University of Copenhagen, Denmark; University of Oslo, Norway; | nlp/semantics, nlp/word-embeddings, nlp/semantic-tagging | null | null | null | null | null |
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 | null | null | York University, Toronto, Canada | nlp/word-embeddings, nlp/emotion-detection, nlp/sentiment-analysis | null | null | null | null | null |
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 | null | null | University of North Texas, USA | nlp/machine-translation, computer-security/access-restrictions | null | null | null | null | null |
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 | null | null | 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. | null | null | null | null |
Andrei Amatuni, Estelle He, Elika Bergelson – Duke University | Preserved Structure Across Vector Space Representations | https://arxiv.org/abs/1802.00840 | papers | 20180101Z00:00:00 | null | null | Duke University | nlp/semantics, nlp/word-embeddings | null | null | null | null | null |
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 | null | null | University of Waterloo, Canada; ETH Zürich, Switzerland; Google, Inc. | graph-processing | null | null | null | null | null |
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 | null | null | University of Waterloo, Canada; ETH Zürich, Switzerland; Google, Inc. | graph-processing | null | null | null | null | null |
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 | null | null | Google; Google Brain; Google DeepMind | nlp/neural-networks | null | null | null | null | null |
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 | null | null | 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. | null | null | null | null |
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 | null | null | University of the Basque Country, Spain | nlp/semantics, nlp/word-embeddings, nlp/bilingual-word-embeddings | null | null | null | null | null |
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 | null | null | University of the Basque Country, Spain | nlp/semantics, nlp/word-embeddings, nlp/bilingual-word-embeddings | null | null | null | null | null |
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 | null | null | University of the Basque Country, Spain | nlp/semantics, nlp/word-embeddings | null | null | null | null | null |
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 | null | null | University of the Basque Country, Spain; Facebook AI Research | cc-cited-not-used, nlp/word-embeddings, nlp/sentence-embeddings, nlp/parallel-corpus | null | null | null | null | null |
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 | null | null | RWTH Aachen University, Germany | nlp/machine-translation | null | null | null | null | null |
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 | null | null | 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). | null | null | null | null |
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 | null | null | 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. | null | null | null | null |
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 | null | null | 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. | null | null | null | null |
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 | null | null | 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. | null | null | null | null |
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 | null | null | 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 [...] | null | null | null | null |
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 | null | null | 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), [...] | null | null | null | null |
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 | null | null | FBK, Trento, Italy; Amazon AI, East Palo Alto, CA, USA, Microsoft Cloud+AI, Redmond, WA, USA | nlp/machine-translation | null | null | null | null | null |
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 | null | null | Bauhaus-Universität Weimar, Germany; Leipzig University, Germany | information-retrieval/search-engine | null | null | null | null | null |
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 | null | null | Università degli Studi di Milano, Italy | web-crawling, web-science/hyperlinkgraph | null | null | null | null | null |
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 | null | null | University of Edinburgh | nlp/machine-translation | null | null | null | null | null |
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 | null | null | University of West Bohemia, Czech Republic | nlp/semantics, nlp/word-embeddings | null | null | null | null | null |
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 | null | null | 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. | null | null | null | null |
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 | null | null | 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. | null | null | null | null |
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 | null | null | University of Electronic Science and Technology of China (UESTC), Chengdu, PR China | nlp/text-generation, ai/image-classification, nlp/image-captioning, ai/deep-learning | null | null | null | null | null |
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 | null | null | Beijing Institute of Technology, China | nlp, nlp/sentiment-analysis, nlp/emotion-detection, nlp/word-embeddings | null | null | null | null | null |
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 | null | null | 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). | null | null | null | null |
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 | null | null | 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. | null | null | null | null |
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 | null | null | IBM Research Almaden, San Jose, USA | semantic web, cc-cited-not-used, ontology extraction | null | null | null | null | null |
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 | null | null | University of Washington, Seattle, USA; Grab, Inc., Seattle, WA, USA | nlp/word-embeddings, cc-cited-not-used | null | null | null | null | null |
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 | null | null | Facebook Artificial Intelligence Research | nlp/word-embeddings, nlp/sentence-embeddings, nlp/evaluation | null | null | null | null | null |
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 | null | null | Facebook AI Research, USA; New York University, USA | nlp/word-embeddings, nlp/sentence-embeddings | null | null | null | null | null |
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 | null | null | 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. | null | null | null | null |
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 | null | null | University of Sao Paulo, Sao Paulo, Brazil; Aalborg University, Aalborg, Denmark | open data, information retrieval | null | null | null | null | null |
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 | null | null | ACM | cc-cited-not-used, information-retrieval | null | null | null | null | null |
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 | null | null | 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. | null | null | null | null |
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 | null | null | HAVELSAN Inc. Ankara, Turkey; Middle East Technical University Ankara, Turkey; Hacettepe University Ankara, Turkey | ai/computer-vision, ai/pattern-recognition, nlp/word-embeddings | null | null | null | null | null |
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 | null | null | University of Stuttgart, Germany | nlp, speech-recognition | ..., 197 millions words of Italian Deduplicated CommonCrawl Text are used to build Italian language model. | null | null | null | null |
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 | null | null | University of Utah | nlp/semantics, nlp/word-embeddings | null | null | null | null | null |
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 | null | null | Facebook AI Research, USA; Google Brain, Mountain View, CA, USA | nlp/machine-translation | null | null | null | null | null |
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 | null | null | 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 | null | null | null | null |
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 | null | null | Université de Toulouse, France | web-science/hyperlinkgraph, graph-processing, cc-cited-not-used | null | null | null | null | null |
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 | null | null | Universität des Saarlandes, Germany; Humboldt-Universität zu Berlin, Germany | nlp/machine-translation, nlp/corpora | null | null | null | null | null |
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 | null | null | 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 | null | null | null | null |
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 | null | null | University of Mannheim, Germany; Ghent University, Belgium | nlp/semantics, data-mining, hypernymy | null | null | null | null | null |
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 | null | null | 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. | null | null | null | null |
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 | null | null | ENEA, Italy | web-science/hyperlinkgraph | null | null | null | null | null |
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 | null | null | expertsystem.com, Madrid, Spain | nlp/word-embeddings | null | null | null | null | null |
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 | null | null | expertsystem.com, Madrid, Spain | nlp/word-embeddings | null | null | null | null | null |
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 | null | null | Stanford University, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA | nlp/semantics, nlp/word-embeddings, ai/ethics-of-machine-learning, ai/machine-learning | null | null | null | null | null |
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 | null | null | 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 | null | null | null | null |
Michael Glass, Alfio Gliozzo – IBM Research AI | Discovering Implicit Knowledge with Unary Relations | http://www.aclweb.org/anthology/P18-1147 | papers | 20180101Z00:00:00 | null | null | IBM Research AI | ai/knowledge-base | null | null | null | null | null |
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 | null | null | 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 [...] | null | CC-DBP | null | null |
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 | null | null | IBM Research AI, New York, USA; Universidad Politcnica de Madrid, Spain; University of Bari, Italy | ai/knowledge-base, ai/deep-learning, semantic web | null | null | null | null | null |
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 | null | null | Indian Institute of Technology (BHU), India; Carnegie Mellon University | nlp/dialogue-systems, nlp/word-embeddings | null | null | null | null | null |
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 | null | null | University of Zagreb, Croatia | nlp/text-classification, nlp/word-embeddings | null | null | null | null | null |
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 | null | null | 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). | null | null | null | null |
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 | null | null | University of Memphis, USA | nlp/word-embeddings, nlp/semantics | null | null | null | null | null |
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 | null | null | Harvard University; Massachusetts Institute of Technology; Siemens Healthineers; Massachusetts General Hospital; Harvard Medical School | nlp/semantics, nlp/word-embeddings | null | null | null | null | null |
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 | null | null | 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. | null | fastText-word-embeddings | null | null |
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 | null | null | University of Edinburgh, United Kingdom; Microsoft | nlp/machine-translation, nlp/grammatical-error-correction | null | null | null | null | null |
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 | null | null | Université de Nantes, France | nlp/machine-translation, nlp/lexikon, nlp/dictionary-creation | null | null | null | null | null |
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 | null | null | Saarland University, Saarbrücken, Germany | nlp/word-embeddings, ai/neural-networks | null | null | null | null | null |
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 | null | null | University of Würzburg, Germany | nlp/semantics, nlp/word-embeddings | null | null | null | null | null |
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 | null | null | 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 | null | null | null | null |
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 | null | null | Baidu Research, Sunnyvale, CA, USA; School of EECS, Oregon State University, Corvallis, OR, USA | nlp/language-model, nlp/machine-translation | null | null | null | null | null |
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 | null | null | MTA-PPKE Magyar Nyelvtechnológiai Kutatócsoport, Hungaria | web-science | null | null | null | null | null |
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 | null | null | 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 | null | null | WMT-16-translation-task-common-crawl-corpus, patent | null | null |
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 | null | null | Facebook AI Research | nlp/word-embeddings, nlp/bilingual-word-embeddings | null | null | null | null | null |
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 | null | null | University of Edinburgh, United Kingdom; Microsoft | nlp/machine-translation, nlp/grammatical-error-correction | null | null | null | null | null |
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 | null | null | University of Michigan, USA; Stanford University, USA | nlp/word-embeddings, nlp/text-analysis, nlp/citation-analysis | null | null | null | null | null |
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 | null | null | 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. | null | null | null | null |
Alexander Kagoshima, Kai Londenberg, Fang Xu – Searchmetrics GmbH | Determination of content score | https://patents.google.com/patent/US20180121430A1/en | papers | 20180101Z00:00:00 | null | null | 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. | null | null | null | null |
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 | null | null | eBay Inc. | ir/query-segmentation, nlp/word-embeddings, patent | null | null | null | null | null |
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 | null | null | Országos Széchényi Könyvtár, Hungary; SZTE Klebelsberg Könyvtár, Hungary | web-archiving, cc-cited-not-used | null | null | null | null | null |
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 | null | null | Qatar Computing Research Institute, Doha, Qatar | computer-security/malicious-domain-detection, computer-security/internet-security, graph-processing | null | null | null | null | null |
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 | null | null | Johns Hopkins University, USA | nlp/machine-translation | null | null | null | null | null |
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