18 Evaluation of Greek Word Embeddings

S Outsios, C Karatsalos, K Skianis, M Vazirgiannis

… wiki. The last model has been trained on Common Crawl and Wikipedia data using FastText based on CBOW model with position-weights (Grave et al., 2018), mentioned as cc+wiki. Category gr_def gr_neg1 0 cc.el.300 …


" Thy algorithm shalt not bear false witness": An Evaluation of Multiclass Debiasing Methods on Word Embeddings

T Schlender, G Spanakis - arXiv preprint arXiv:2010.16228, 2020

… However, surprisingly, the WEAT score measured in ConceptNet is the worst of all three. The GloVe embeddings seem to carry the most bias concerning the RNSB and MAC metrics, which is intuitive when considering the common crawl data it was trained on …


A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News

C Poot, A van Cranenburgh - arXiv preprint arXiv:2011.01615, 2020

… 5 Evaluation Before presenting our main benchmark results, we discuss the issue of coreference evaluation metrics. 6We use Fasttext common crawl embeddings, https://fasttext.cc/docs/en/crawl-vectors.html Page 6 …


V Iashin, E Rahtu - arXiv preprint arXiv:2005.08271, 2020

Page 1. IASHIN, RAHTU: A BETTER USE OF AUDIO-VISUAL CUES 1 A Better Use of Audio-Visual Cues: Dense Video Captioning with Bi-modal Transformer Vladimir Iashin vladimir.iashin@tuni.fi Esa Rahtu esa.rahtu@tuni.fi …


A brief tour to the NLP Sesame Street

… In addition to the strategy to verify fake news this research provided of a large corpus of news articles from Common Crawl named RealNews, as Grover needed a large corpus of news with metadata which was not available or …


A Call for More Rigor in Unsupervised Cross-lingual Learning

M Artetxe, S Ruder, D Yogatama, G Labaka, E Agirre - arXiv preprint arXiv …, 2020

… However, as of November 2019, Wikipedia exists in only 307 languages3 of which nearly half have less than 10,000 articles. While one could hope to overcome this by taking the entire web as a corpus, as …


A Character-Level BiGRU-Attention for Phishing Classification

L Yuan, Z Zeng, Y Lu, X Ou, T Feng - International Conference on Information and …, 2019

… In addition, Common Crawl that stored a great deal of websites is an open website for crawler learners. There are 800,000 websites provided as legitimate websites data … Phish urls. Legal urls. Data sources. Phish Tank. Common Crawl …


A Comprehensive Survey of Grammar Error Correction

Y Wang, Y Wang, J Liu, Z Liu - arXiv preprint arXiv:2005.06600, 2020

… Common Crawl. The Common Crawl corpus [10] is a repository of web crawl data which is open to everyone. It completes crawls monthly since 2011. • EVP … [28] Word-Level L1 Yes None Error Selection Wikipedia, 2014 Common Crawl …


U Naseem, I Razzak, SK Khan, M Prasad - arXiv preprint arXiv:2010.15036, 2020

Page 1. A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Models USMAN NASEEM∗, School of Computer Science, The University of Sydney, Australia …


A Cross-lingual Natural Language Processing Framework for Infodemic Management

R Pal, R Pandey, V Gautam, K Bhagat, T Sethi - arXiv preprint arXiv:2010.16357, 2020

… The algorithm effectively minimizes this function to learn meaningful vector representations. The version of Glove used for experimentation is the publicly available common Crawl (840B tokens, 2.2M vocab, cased, 300 dimension vectors) …


Page 1. A Deep Learning Approach to Interest Analysis Thomas van der Meer A thesis submitted for the degree of Master of Business Informatics Department of Information and Computing Sciences Utrecht University The …


A Deep Learning-Based Approach for Identifying the Medicinal Uses of Plant-Derived Natural Compounds. Front. Pharmacol. 11: 584875. doi: 10.3389/fphar …

S Yoo, HC Yang, S Lee, J Shin, S Min, E Lee, M Song… - Frontiers in Pharmacology …, 2020

… alphaisothiocyanatotoluene.” In this study, we used the pre-trained fastText model with Wikipedia and Common Crawl (Grave et al., 2018). The model additionally learned from the DrugBank indication and PubMed literature …


A deep search method to survey data portals in the whole web: toward a machine learning classification model

AS Correa, A Melo Jr, FSC da Silva - Government Information Quarterly, 2020

… Later, the same authors (AS Correa & da Silva, 2019) took advantage of the URL index of the Common Crawl project (an open repository of web crawl data) to survey potential data portals by searching the URL text strings …


A Deep-Learning-Based Blocking Technique for Entity Linkage

F Azzalini, M Renzi, L Tanca - International Conference on Database Systems for …, 2020

… attribute value \(t[A_{k}]\) is transformed into a real-valued vector \(\mathbf{v} (w)\). The fastText model we use is crawl-300d-2M-subword [3] where each word is represented as a 300-dimensional vector and the …


A Focused Study to Compare Arabic Pre-training Models on Newswire IE Tasks

W Lan, Y Chen, W Xu, A Ritter - arXiv preprint arXiv:2004.14519, 2020

… three times; (2) add the Arabic shuffled Os- car data (Ortiz Suárez et al., 2019), a large-scale multilingual dataset obtained by language identification and filtering of the Common Crawl corpus … XLM-Rbase CommonCrawl 55.6B …


A Framework for Word Embedding Based Automatic Text Summarization and Evaluation

TT Hailu, J Yu, TG Fantaye - Information, 2020

Text summarization is a process of producing a concise version of text (summary) from one or more information sources. If the generated summary preserves meaning of the original text, it will help the users to make fast and …


A German Language Voice Recognition System using DeepSpeech

… 1, pp. 517–520, 1992. [14] Christopher Cieri, David Miller and Kevin Walker, “The Fisher Corpus: a Resource for the Next Generations of Speech-to-Text,” LREC, 2004. [15] CommomCrawl, “English language model,” http://commoncrawl.org/, 2020 …


A Gradient Boosting-Seq2Seq System for Latin POS Tagging and Lemmatization

GGA Celano - LREC 2020 Workshop Language Resources and …

… prefixes, infixes, or suffixes to be weighted. Some models for Latin, such as the one based on texts from Common Crawl and Wikipedia, have already been computed and are freely available. 7 However, since the data released …


A graph based framework for structured prediction tasks in sanskrit

A Krishna, A Gupta, P Goyal, B Santra, P Satuluri - Computational Linguistics, 2020

Page 1. A Graph Based Framework for Structured Prediction Tasks in Sanskrit Amrith Krishna* University of Cambridge Bishal Santra Indian Institute of Technology Kharagpur Ashim Gupta† University of Utah Pavankumar Satuluri Chinmaya Vishwavidyapeeth …


A Graph-Theoretic Approach for the Detection of Phishing Webpages

CL Tan, KL Chiew, KSC Yong, J Abdullah, Y Sebastian - Computers & Security, 2020

JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main content Skip to article …


A Hybrid Approach for Aspect-Based Sentiment Analysis Using Deep Contextual Word Embeddings and Hierarchical Attention

MM Trusca, D Wassenberg, F Frasincar, R Dekker - arXiv preprint arXiv:2004.08673, 2020

R Dekker - Web Engineering: 20th International Conference, ICWE …

… The last two conditions for f are necessary to prevent overweighting of either rare or frequent co-occurrences. In this paper, we choose to use 300-dimension GloVe word embeddings trained on the Common Crawl (42 billion words)[14]. Word2vec …


A Large Scale Study on Health Information Retrieval for Laypersons

… 3.1 Description of Document Collection The consumer-oriented health search task uses a dataset called clefehealth2018 corpus, which was created by acquiring web pages from various health do- mains(websites) using the CommonCrawl platform1 …


A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal

D Gholipour Ghalandari, C Hokamp, J Glover, G Ifrim - arXiv, 2020

DG Ghalandari, C Hokamp, NT Pham, J Glover, G Ifrim - arXiv preprint arXiv …, 2020

… We also automatically extend these source articles by looking for related articles in the Common Crawl archive … Table 1: Example event summary and linked source ar- ticles from the Wikipedia Current Events Portal, and …


A Large-Scale Semi-Supervised Dataset for Offensive Language Identification

S Rosenthal, P Atanasova, G Karadzhov, M Zampieri… - arXiv preprint arXiv …, 2020

… The first layer of the LSTM model is an embedding layer, which we initialize with a concatenation of the GloVe 300-dimensional (Pennington et al., 2014) and FastText's Common Crawl 300dimensional embeddings (Grave et al., 2018). The Page 5 …


A Longitudinal Analysis of Job Skills for Entry-Level Data Analysts

T Dong, J Triche - Journal of Information Systems Education, 2020

… Therefore, we used the Common Crawl dataset to address this problem (http:// commoncrawl.org/). Common Crawl is a non-profit organization that builds and maintains an open repository of web crawl data that is, in essence, a copy of the Internet …


A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages

P Ortiz Suárez, L Romary, B Sagot - arXiv, 2020

PO Suárez, L Romary, B Sagot - arXiv preprint arXiv:2006.06202, 2020

… al., 2019), a freely available2 multilingual dataset obtained by performing language classification, filtering and cleaning of the whole Common Crawl corpus.3 … 1 https://commoncrawl.org 2 https://traces1.inria.fr/oscar/ 3Snapshot …


A Multilingual Evaluation for Online Hate Speech Detection

M Corazza, S Menini, E Cabrio, S Tonelli, S Villata - ACM Transactions on Internet …, 2020

… In particular, we use the Italian and German embeddings trained on Common Crawl and Wikipedia [33] with size 300 … English Fasttext Crawl embeddings: English embeddings trained by Fasttext9 on Common Crawl with an embedding size of 300 …


A Neural-based model to Predict the Future Natural Gas Market Price through Open-domain Event Extraction

… Strong baseline We feed the price and sentence embedding of filtered news using spaCy small English (Context tensor trained on [39], 300-d embedding vector) and large English model (trained on both [39] and Common Crawl …


A NOVEL APPROACH FOR NAMED ENTITY RECOGNITION ON HINDI LANGUAGE USING RESIDUAL BILSTM NETWORK

… It provides word embeddings for Hindi (and 157 other languages) and is based on the CBOW (Continuous Bag-of-Words) model. The CBOW model learns by predicting the current word based on its context, and it was trained …


R Dehkharghani, H Emami - arXiv preprint arXiv:2007.09495, 2020

Page 1. * Corresponding Author A novel approach to sentiment analysis in Persian using discourse and external semantic information *Rahim Dehkharghani, Faculty of Engineering, University of Bonab, Bonab, Iran rdehkharghani …


A Novel BGCapsule Network for Text Classification

AK Gangwar, V Ravi - arXiv preprint arXiv:2007.04302, 2020

… GloVe. We used GloVe [21] pretrained model. The GloVe model trained on 2.2 million vocabularies, 840 billion tokens of web data from Common Crawl. This Glove embedding projected each word to a 300-dimensional vector …


A novel reasoning mechanism for multi-label text classification

R Wang, R Ridley, W Qu, X Dai - Information Processing & Management


A Performance Comparison among Different Amounts of Context on Deep Learning Based Intent Classification Models

M Jung, J Kim, JY Jang, H Jung, S Shin - 2020 International Conference on …, 2020

… We also employ word embeddings trained on Common Crawl of fastText [13], a library for efficient text classification and representation learning. We apply a bidirectional LSTM (Bi-LSTM) network [6] to build an LSTM based intent classification model …


D Preuveneers, A Duda, W Joosen, M Korczynski

Page 1. A Practical Approach for Taking Down Avalanche Botnets Under Real-World Constraints Victor Le Pochat∗, Tim Van hamme∗, Sourena Maroofi§, Tom Van Goethem∗, Davy Preuveneers∗, Andrzej Duda§, Wouter Joosen …


A Practical Guide to Hybrid Natural Language Processing: Combining Neural Models and Knowledge Graphs for NLP

JM Gomez-Perez, R Denaux, A Garcia-Silva - 2020

Page 1. Jose Manuel Gomez-Perez Ronald Andres Garcia-Silva Denaux A to Practical Hybrid Natural Guide Language Processing Combining Neural Models and Knowledge Graphs for NLP Page 2. A Practical Guide …


A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks

AS Lin, S Rao, A Celikyilmaz, E Nouri, C Brockett… - arXiv preprint arXiv …, 2020

… We extract text recipes from Common Crawl,2 one of the largest web sources of text … CommonCrawl text-text recipe pairs We randomly choose 200 text-text recipes pairs (spanning 5 dishes) from the test … Table 3: Results for …


D Massiceti, V Kulharia, PK Dokania, N Siddharth… - arXiv preprint arXiv …, 2020

Page 1. A Revised Generative Evaluation of Visual Dialogue Daniela Massiceti Viveka Kulharia Puneet K. Dokania N. Siddharth Philip HS Torr University of Oxford {daniela, viveka, puneet, nsid, phst} @robots.ox.ac.uk Abstract …


Page 1. Japan Advanced Institute of Science and Technology JAIST Repository https://dspace.jaist.ac.jp/ Title A Study on Transformer-based Machine Comprehension with Curriculum Learning Author(s) BUI, MINH …


L Ma, WN Zhang, M Li, T Liu - arXiv preprint arXiv:2004.13818, 2020

Page 1. A Survey of Document Grounded Dialogue Systems (DGDS) LONGXUAN MA, Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology, China WEI-NAN ZHANG, Research Center …


A Survey on Contextual Embeddings

Q Liu, MJ Kusner, P Blunsom - arXiv preprint arXiv:2003.07278, 2020

… T5 introduces a new pre-training dataset, Colossal Clean Crawled Corpus by cleaning the web pages from Common Crawl … by training a Transformerbased masked language model on one hundred languages, using more …


A Systematic Study of Inner-Attention-Based Sentence Representations in Multilingual Neural Machine Translation

R Vázquez, A Raganato, M Creutz, J Tiedemann - Computational Linguistics, 2020

Page 1. Computational Linguistics Just Accepted MS. https://doi.org/10.1162/ COLI_a_00377 © Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license A Systematic Study of Inner-Attention-Based …


A Text Augmentation Approach using Similarity Measures based on Neural Sentence Embeddings for Emotion Classification on Microblogs

YK Shyang, JLS Yan - 2020 IEEE 2nd International Conference on Artificial …, 2020

… vectors. We used two InferSent models available, which were InferSent trained using GloVe on Common Crawl 840B (InferSent-GloVe) and InferSent trained using fastText on Common Crawl 600B (InferSentfastText). Four …


A Transformer-based Audio Captioning Model with Keyword Estimation

Y Koizumi, R Masumura, K Nishida, M Yasuda, S Saito - arXiv preprint arXiv …, 2020

… sions different. We use the bottleneck feature of VGGish [11] (Dx = 128) for audio embedding, and fastText [18] trained on the Common Crawl corpus (Dw = 300) for caption-word and keyword embedding, respectively. Since the …


B Carneiro, G Resce, Y Ma, G Pacillo, P Läderach - 2020

Page 1. A web analytics approach to map the influence and reach of CCAFS Working Paper No. 326 CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) Page 2. A web analytics …


A WRITTEN TEST FOR ARTIFICIAL GENERAL INTELLIGENCE

… If you input a few words, GPT-3 will write a completed thought or sentence. The model was trained on data from Common Crawl, a nonprofit that builds and maintains an open repository of web crawl data accessible to the public for free (common crawl.org) …


Accenture at CheckThat! 2020: If you say so: Post-hoc fact-checking of claims using transformer-based models

E Williams, P Rodrigues, V Novak - arXiv preprint arXiv:2009.02431, 2020

… BPE) instead of WordPiece. [23] The base-roberta model was pre-trained on 160GB of text extracted from BookCorpus, English Wikipedia, CC-News, OpenWebText, and Stories (a subset of CommonCrawl Data) [14]. At the time …


Accurate and Fast URL Phishing Detector: A Convolutional Neural Network Approach

W Wei, Q Ke, J Nowak, M Korytkowski, R Scherer… - Computer Networks, 2020

… The database downloaded during the article writing contained 10,604 records. To obtain legitimate websites, the second part of the training dataset was downloaded from the Common Crawl Foundation (http://commoncrawl.org/) …


Active Learning for Spreadsheet Cell Classification

J Gonsior, J Rehak, M Thiele, E Koci, M Günther…

… Another recent corpus is Fuse [4], which comprises 249, 376 unique spreadsheets, extracted from Common Crawl2. Each spreadsheet is accompanied by a JSON file, which includes NLP token extraction and metrics …


Adaptive GloVe and FastText Model for Hindi Word Embeddings

V Gaikwad, Y Haribhakta - Proceedings of the 7th ACM IKDD CoDS and 25th …, 2020

… developed using original GloVe model, FastText model (FastTextHin), Adaptive FastText model (AFM) (trained on Hindi monolingual corpus [11] and FastText embeddings published on the website [25] (FastTextWeb) …


ADD: Academic Disciplines Detector Based on Wikipedia

A Gjorgjevikj, K Mishev, D Trajanov - IEEE Access, 2020

… For representation of the textual data into fixed-length vector form using pre-trained text encoding models, in addition to the model files, word vectors trained with GloVe [38] on Common Crawl (840B tokens)6 and with FastText …


TP Nguyen, S Razniewski, G Weikum - arXiv preprint arXiv:2011.00905, 2020

Page 1. Advanced Semantics for Commonsense Knowledge Extraction Tuan-Phong Nguyen Max Planck Institute for Informatics tuanphong@mpi-inf.mpg.de Simon Razniewski Max Planck Institute for Informatics srazniew@mpi-inf.mpg.de …


Advanced Web Crawlers

JM Patel - Getting Structured Data from the Internet, 2020

… A good starting point is using common crawl's fork of Apache Nutch 1.x–based crawler. The codebase is open sourced and available on the GitHub repo (https://github.com/commoncrawl), and it's not only well …


Advancements in Deep Learning Theory and Applications: Perspective in 2020 and beyond

MN Saadat, M Shuaib - Advances and Applications in Deep Learning, 2020

The aim of this chapter is to introduce newcomers to deep learning, deep learning platforms, algorithms, applications, and open-source datasets. This chapter will give you a broad overview of the term deep learning, in context …


Page 1. Advancing Neural Language Modeling in Automatic Speech Recognition Von der Fakultät für Mathematik, Informatik und Naturwissenschaften der RWTH Aachen University zur Erlangung des akademischen Grades …


Adversarial Self-Supervised Data-Free Distillation for Text Classification

X Ma, Y Shen, G Fang, C Chen, C Jia, W Lu - arXiv preprint arXiv:2010.04883, 2020

… DeepFace (Taigman et al., 2014) is trained on user images under confidential policies for protecting users. Further, some datasets, like Common Crawl dataset used in GPT3 (Brown et al., 2020), contain nearly a trillion words and are difficult to transmit and store …


Adversarial Training for Large Neural Language Models

X Liu, H Cheng, P He, W Chen, Y Wang, H Poon, J Gao - arXiv preprint arXiv …, 2020

… For continual pre-training of RoBERTa, we use Wikipedia (13GB), OPENWEBTEXT (public Reddit content (Gokaslan and Cohen); 38GB), STORIES (a subset of CommonCrawl (Trinh and Le, 2018); 31GB). 2https://dumps.wikimedia.org/enwiki/ Page 5 …


Affective Conditioning on Hierarchical Attention Networks applied to Depression Detection from Transcribed Clinical Interviews

D Xezonaki, G Paraskevopoulos, A Potamianos…

… Next, for both corpora we tokenize the speaker turns by splitting them into words. We use 300D GloVe [31] pretrained word embeddings, trained on the Common Crawl corpus, to extract word representations. Implementation …


Affective Conditioning on Hierarchical Networks applied to Depression Detection from Transcribed Clinical Interviews

D Xezonaki, G Paraskevopoulos, A Potamianos… - arXiv preprint arXiv …, 2020


AgglutiFiT: Efficient Low-Resource Agglutinative Language Model Fine-Tuning

Z Li, X Li, J Sheng, W Slamu - IEEE Access, 2020

… test set. For crosslingual pre-training language models, we use the XLM − R model loaded from the torch.Hub that It is trained on 2.5TB of CommonCrawl data, in 17 languages and uses a large vocabulary size of 95K. XLM …


AI4Bharat-IndicNLP Corpus: Monolingual Corpora and Word Embeddings for Indic Languages

A Kunchukuttan, D Kakwani, S Golla, A Bhattacharyya… - arXiv preprint arXiv …, 2020

… FastText also provides embeddings trained on Wikipedia + CommonCrawl corpus … 1. We augmented our crawls with some data from other sources: Leipzig corpus (Goldhahn et al., 2012) (Tamil and Bengali), WMT NewsCrawl …


Algorithmic Bias: On the Implicit Biases of Social Technology

… This study found that parsing software trained on a dataset called “the common crawl”—an assemblage of 840 billion words collected by crawling the internet—resulted in the program producing “human-like semantic …


ALOD2Vec Matcher Results for OAEI 2020

… like DBpedia [8] – but instead on the whole Web: The dataset consists of hypernymy relations extracted from the Common Crawl3, a … 3 see http://commoncrawl.org/ 4 see http://webisa.webdatacommons.org/concept …


An AI-Based System for Formative and Summative Assessment in Data Science Courses

P Vittorini, S Menini, S Tonelli - International Journal of Artificial Intelligence in …, 2020

Massive open online courses (MOOCs) provide hundreds of students with teaching materials, assessment tools, and collaborative instruments. The assessment a.


An Analysis of Dataset Overlap on Winograd-Style Tasks

A Emami, A Trischler, K Suleman, JCK Cheung - arXiv preprint arXiv:2011.04767, 2020

… This is a form of data contamination. One of the earliest works that trained a language model on Common Crawl data identified and removed a training documents that overlapped with one of their evaluation datasets (Trinh and Le, 2018) …


Page 1. NMT Re-Ranking Using Sequence-Labeling for GEC Paper: An Approach to NMT Re-Ranking Using Sequence-Labeling for Grammatical Error Correction Bo Wang, Kaoru Hirota, Chang Liu, Yaping Dai † , and Zhiyang Jia …


An Effective Phishing Detection Model Based on Character Level Convolutional Neural Network from URL

A Aljofey, Q Jiang, Q Qu, M Huang, JP Niyigena - Electronics, 2020

Phishing is the easiest way to use cybercrime with the aim of enticing people to give accurate information such as account IDs, bank details, and passwords. This type of cyberattack is usually triggered by emails, instant messages, or …


An Empirical Investigation of Performances of Different Word Embedding Algorithms in Comment Clustering

E Dorani, N Duru, T Yıldız - 2019 Innovations in Intelligent Systems and …

… In this study, we used the Common Crawl (1.9 million words)1 and (2.0 million word)2 pre-trained word vectors for Glove and FastText, respectively … Such that we used the Common Crawl (1.9 million words) and (2.0 million word) …


An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training

K Arumae, Q Sun, P Bhatia - arXiv preprint arXiv:2010.00784, 2020

… We processed publicly available bio-medical and non-bio-medical corpora for pre-training our models. For non-bio-medical data, we use BookCorpus and English Wikipedia data, CommonCrawl Stories (Trinh and Le, 2018) …


An Empirical Study of Pre-trained Transformers for Arabic Information Extraction

W Lan, Y Chen, W Xu, A Ritter - Proceedings of the 2020 Conference on Empirical …, 2020

… XLM-Rlarge CommonCrawl 295B/55.6B/2.9B SentencePiece 250k/80k/ 14k yes large 550M … and the Gigaword portion three times; (2) adding the Arabic section of the Oscar corpus (Ortiz Suárez et al., 2019), a large-scale …


An Empirical Study of Transformer-Based Neural Language Model Adaptation

K Li, Z Liu, T He, H Huang, F Peng, D Povey… - ICASSP 2020-2020 IEEE …, 2020

… We denote the merged corpus as “Source1” in all tables. The second corpus is an English subset (10%) of the Common Crawl News (CCNews) [27], a news corpus contains articles published worldwide between September 2016 and February 2019 …


An Empirical Study on Explainable Prediction of Text Complexity: Preliminaries for Text Simplification

C Garbacea, M Guo, S Carton, Q Mei - arXiv preprint arXiv:2007.15823, 2020

… In our experiments we use the 12 layer XLNeT base pre-trained model on 2https://docs.fast.ai/text.html 4 Page 5. the English Wikipedia and the Books corpus (similar to BERT), and additionally also on Giga5 ClueWeb …


An Empirical Study on Release Engineering of Artificial Intelligence Products

M Xiu - arXiv preprint arXiv:2012.01403, 2020

… SPIRAL CelebA HQ 10 (Config: Network Structure) hub.Module Text Embedding Albert Wikipedia, BooksCorpus, Stories, CommonCrawl, Giga5, Clue Web 4 (Config: Model Size) (Data Pre-processing: Cased or Not) hub.Module …


An English–Swahili parallel corpus and its use for neural machine translation in the news domain

F Sánchez-Martınez, VM Sánchez-Cartagena…

… 9https://commoncrawl.github.io/ cc-crawl-statistics/plots/languages 10https://github.com/ CLD2Owners/cld2 300 Page 3. of crawling and, from the remaining 3 232, only 908 ended up containing data in both languages. Document alignment …


An Enhanced Sentiment Analysis Framework Based on Pre-Trained Word Embedding

EH Mohamed, MES Moussa, MH Haggag - International Journal of Computational …, 2020


An Evaluation Benchmark for Testing the Word Sense Disambiguation Capabilities of Machine Translation Systems

A Raganato, Y Scherrer, J Tiedemann - … of The 12th Language Resources and …, 2020

… Page 2. 3669 CS–EN DE–EN FI–EN FR–EN RU–EN Books GlobalVoices Europarl JW300 News-Comm. Tatoeba TED Talks EU Bookshop MultiUN Common Crawl Table 1: Corpora used to extract the MuCoW test suites. The …


An Evaluation Model for Auto-generated Cognitive Scripts

… Fig. 6. The Cinema Linear Cognitive Script Converted into Text. B. Computing the GloVe Similarity Ratio Threshold The proposed model uses GloVe vectors of 300 dimensions that are created by training Common Crawl (840B tokens …


G Aras, D Makaroglu, S Demir, A Cakir - arXiv preprint arXiv:2005.07692, 2020

Page 1. An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named Entity Recognition Gizem Arasa,, Didem Makaroglua,b, Seniz Demirc and Altan Cakirb aDemiroren Teknoloji AS, Istanbul, Turkey bDepartment …


An Exploration in L2 Word Embedding Alignment

… It is also run for Wikipedia Chinese fastText embeddings to Common Crawl English fastText embeddings to test the method on a large corpus of a different domain. All fastText embeddings are pretrained and downloaded from their website16 …


An Exploratory Approach to the Corpus Filtering Shared Task WMT20

… We make the number of lines taken by the wikipedia data be between 40-50% of the number of lines taken by the CommonCrawl data … entropy as defined in Equation 5. We find that the scores we got were ex- tremely …


An Interactive Network for End-to-End Review Helpfulness Modeling

J Du, L Zheng, J He, J Rong, H Wang, Y Zhang - Data Science and Engineering, 2020

Review helpfulness prediction aims to prioritize online reviews by quality. Existing methods largely combine review texts and star ratings for helpfulness.


An Iterative Knowledge Transfer NMT System for WMT20 News Translation Task

J Kim, S Park, S Kim, Y Choi - Proceedings of the Fifth Conference on Machine …, 2020

… Kyoto Free Translation Task 0.44M TED Talks 0.24M Monolingual Data (En) Europarl v10 2.29M News Commentary v15 0.6M News Crawl 23.35M News Discussions 63.51M Monolingual Data (Ja) News Crawl …


An Open-Domain Web Search Engine for Answering Comparative Questions

… pp. 91–96 (2017) 3. Bevendorff, J., Stein, B., Hagen, M., Potthast, M.: Elastic ChatNoir: Search Engine for the ClueWeb and the Common Crawl. In: Azzopardi, L., Hanbury, A., Pasi, G., Piwowarski, B. (eds.) Advances in Information Retrieval …


Analogical frames by constraint satisfaction

Page 1. Analogical Frames by Constraint Satisfaction A THESIS SUBMITTED IN FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Lance De Vine BMaths …


Page 1. Jordi Pascual Fontanilles Analysis of the communication of smart city features through social media MASTER'S THESIS Supervised by Dr. Antonio Moreno Ribas Computer Security Engineering and Artificial …


Analyzing Sustainability Reports Using Natural Language Processing

A Luccioni, E Bailor, N Duchene - arXiv preprint arXiv:2011.08073, 2020

… In fact, research in financial NLP has found that using general-purpose NLP models trained on corpora such as Wikipedia and the Common Crawl fail to capture domainspecific terms and concepts which are critical for a coherent …


Analyzing the Effect of Community Norms on Gender Bias

… We use word2vec to train embeddings on the comment data for each of the subreddit. For fasttext, we use 2 million word vectors trained with subword information on Common Crawl …


ANDI@ CONCRETEXT: Predicting concreteness in context for English and Italian using distributional models and behavioural norms

A Rotaru - Proceedings of the 7th evaluation campaign of Natural …, 2020

… Skip-gram (Google News – 100B) 21 21 21 21 GloVe (Common Crawl – 840B) 21 21 21 21 ConceptNet NumberBatch (ConceptNet + Skip-gram + GloVe) … abs(V(w) - V(c)) Behavioural norms (frequency, etc.) 20 20 20 20 …


Announcing CzEng 2.0 Parallel Corpus with over 2 Gigawords

T Kocmi, M Popel, O Bojar - arXiv preprint arXiv:2007.03006, 2020

… format. New parallel data come from Europarl (v10), News commentary, Wikititles, Commoncrawl, Paracrawl2, WikiMatrix (Schwenketal., 2019), and Tilde MODEL Corpus (EESC, EMA, Rapid; Rozis and Skadinš, 2017). We …


Anomaly detection with Generative Adversarial Networks and text patches

… We used the model containing two million word vectors trained on Common Crawl … The depressive data was similarly cleaned. After cleaning the data it was converted to word vectors using the FastText model trained on Common Crawl and Wikipedia [12] …


Another factor to consider is the user's perspective. Everyone uses web search. Using a search engine to look things up on the web is the most popular activity on the …

… Indexes that offer complete access do, however, exist. At the top of this list is Common Crawl,NOTE 12 a nonprofit project that aims to provide a web index for anyone who's interested … Common Crawl represents an important development …


Answering Comparative Questions with Arguments

A Bondarenko, A Panchenko, M Beloucif, C Biemann… - Datenbank-Spektrum, 2020

… analyzing Wikidata and DBpedia as additional sources of (structured) information besides the retrieval of sentences/documents from the Common Crawl … Ruppert E, Faralli S, Ponzetto SP, Biemann C (2018) Building a …


Answering Event-Related Questions over Long-term News Article Archives

… We can see that the actual time scope (January, 1988) of the first question is reflected relatively well by its distribution of relevant documents as generally 4 We use Glove [23] embeddings trained on the Common Crawl dataset with 300 dimensions. Page 6 …


Page 1. Escola Tècnica Superior d'Enginyeria Informàtica Universitat Politècnica de València Application of Machine Learning Techniques for Text Generation DEGREE FINAL WORK Degree in Computer Engineering Author: Salvador Martí Román …


Application of Machine Learning to Classify News Headlines


AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization

S Kulkarni, S Chammas, W Zhu, F Sha, E Ie - arXiv preprint arXiv:2010.12694, 2020

… is a nontrivial task in itself and there are several con1https://commoncrawl org … paragraphs, we use a pre-processed and cleaned version of the Common Crawl corpus (Raffel et al … We illustrate our approach us- ing Google's Natural …


AraWEAT: Multidimensional Analysis of Biases in Arabic Word Embeddings

A Lauscher, R Takieddin, SP Ponzetto, G Glavaš - arXiv preprint arXiv:2011.01575, 2020

… For FT, we investigate two models, one trained on the portions of Wikipedia and CommonCrawl corpora written in Modern Standard Arabic (MS) and the other on portions written in Egyptian Arabic.9 We evaluate the four variants …


ArchiMeDe@ DANKMEMES: A New Model Architecture for Meme Detection

… We fine-tune representations over the available meme textual data and use them as components of our end-to-end system. 1umberto-commoncrawl-cased-v1 in the HuggingFace's model hub (Wolf et al., 2019) Page 4. 2.3 Visual input …


Are All Good Word Vector Spaces Isomorphic?

I Vulić, S Ruder, A Søgaard - arXiv preprint arXiv:2004.04070, 2020

… 3Recent initiatives replace training on Wikipedia with training on larger CommonCrawl data (Grave et al., 2018; Conneau et al., 2020), but the large differences in corpora sizes between high-resource and low-resource languages are not removed …


Are All Languages Created Equal in Multilingual BERT?

S Wu, M Dredze - arXiv preprint arXiv:2005.09093, 2020

… (2019) train a multilingual masked language model (Devlin et al., 2019) on 2.5TB of CommonCrawl filtered data covering 100 languages and show it outperforms a Wikipedia-based model on low resource languages (Urdu …


Argumentative relation classification for argumentative dialogue systems

… With this setup, args outperformed ArgumenText in the category related. 3.2.2. ArgumenText The search engine ArgumenText [35] uses the English part of CommonCrawl2 to retrieve relevant documents … 2http://commoncrawl …


Argumentative Topology: Finding Loop (holes) in Logic

S Tymochko, Z New, L Bynum, E Purvine, T Doster… - arXiv preprint arXiv …, 2020

… Word embeddings are performed using two pretrained models: Word2Vec trained on the Google News dataset [12] and GloVe trained on Common Crawl [13]. We compute the persistence diagrams of the topological …


ArgumenText: Argument Classification and Clustering in a Generalized Search Scenario

J Daxenberger, B Schiller, C Stahlhut, E Kaiser… - Datenbank-Spektrum, 2020

… Full size image. For the public version of the ArgumenText search engine, we indexed more than 400 million English and German web pages from the CommonCrawl project and segmented all documents into sentences [21] …


Artificial Intelligence in mental health and the biases of language based models

I Straw, C Callison-Burch - PloS one, 2020

… 52]. As described by Pennington et al. GloVe embeddings were trained on text copora from Wikipedia data, Gigaword and web data from Common Crawl which built a vocabulary of 400,000 frequent words [57]. Word2Vec was …


ASAPPpy: a Python Framework for Portuguese STS?

… 20 Page 8. From those, CBOW Word2vec and GloVe, both with 300-dimensioned vectors, were selected; (ii) fastText.cc embeddings [9], which provide word vectors for 157 languages, trained on Common Crawl and Wikipedia using fastText …


Ascent of Pre-trained State-of-the-Art Language Models

K Nagda, A Mukherjee, M Shah, P Mulchandani… - Advanced Computing …, 2020

… 6.2 Dataset. Similar to BERT, XLNet was pre-trained using English Wikipedia dataset (13 GB of plain text), as well as CommonCrawl, Giga5 and ClueWeb 2012-B datasets [11]. The large variant of the model has a sequence and …


Aspect-Controlled Neural Argument Generation

B Schiller, J Daxenberger, I Gurevych - Training

… Consequently, the following preprocessing steps ultimately target retrieval and classification of sentences. To evaluate different data sources, we use a dump from Common-Crawl2 (CC) and Reddit comments3 …


Assessing Demographic Bias in Named Entity Recognition

… level confidence via the Constrained ForwardBackward algorithm [5]. Different versions of this model were trained on CoNLL 03 NER benchmark dataset [27] by utilizing varying embedding methods: (a) GloVe uses GloVe 840B …


Assessing Suitable Word Embedding Model for Malay Language through Intrinsic Evaluation

YT Phua, KH Yew, OM Foong, MYW Teow - 2020 International Conference on …, 2020

… This mode was trained on Common Crawl and Wikipedia [26], and pre-trained word vectors for 294 languages were trained on Wikipedia [22]. The results of the evaluation were 0.477 for Pearson correlation coefficient and 0.51 …


ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations

F Alva-Manchego, L Martin, A Bordes, C Scarton… - arXiv preprint arXiv …, 2020

… stopwords). We use the 50k most frequent words of the FastText word embeddings vo- cabulary (Bojanowski et al., 2016). This vo- cabulary was originally sorted with frequencies of words in the Common Crawl. This score …


Attention-based hierarchical recurrent neural networks for MOOC forum posts analysis

N Capuano, S Caballé, J Conesa, A Greco - Journal of Ambient Intelligence and …, 2020

… WikiNER corpora (Nivre et al. 2016) for Italian as well as the OntoNotes (Pradhan and Ramshaw 2017) and the Common Crawl Footnote 2 corpora for English. Text categorization model. Independently from the specific document …


Attention-based Model for Evaluating the Complexity of Sentences in English Language

D Schicchi, G Pilato, GL Bosco - 2020 IEEE 20th Mediterranean Electrotechnical …, 2020

… The output of the attention layer (context-vector) is given as input to a dense layer, which gives the probability that the sentence belongs to either hard-to-understand or easy-to- 1www.wikipedia.org 2www.commoncrawl …


Attribute Sentiment Scoring with Online Text Reviews: Accounting for Language Structure and Missing Attributes

… 10These embeddings have been trained on different corpus like Wikipedia dumps, Gigaword news dataset and web data from Common Crawl and have more than 5 billion unique tokens. Page 18. 17 lenging text and image classification problems (Wang et al …


Augmenting cross-domain knowledge bases using web tables

Page 1. Augmenting Cross-Domain Knowledge Bases Using Web Tables Inauguraldissertation zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften der Universität Mannheim vorgelegt von Yaser …


Author2Vec: A Framework for Generating User Embedding

X Wu, W Lin, Z Wang, E Rastorgueva - arXiv preprint arXiv:2003.11627, 2020

… the user. We used the Facebook FastText (https:// fasttext.cc/) pre-trained Word2Vec model: crawl-300d-2M, which is a model with 2 million word vectors trained on Common Crawl (600B to- kens). The baseline implementations …


Autoencoding Improves Pre-trained Word Embeddings

M Kaneko, D Bollegala - arXiv preprint arXiv:2010.13094, 2020

… 3M words learnt from the Google News corpus), GloVe2 (300-dimensional word embeddings for ca. 2.1M words learnt from the Common Crawl), and fastText3 (300dimensional embeddings for ca. 2M words learnt from the Common Crawl) …


Automated coding of implicit motives: A machine‑learning approach

… experiments we decided to use Facebook's FastText subword embeddings of 300 dimensions trained on Common Crawl (600 billion tokens).5 This is the set of pre-trained vectors that we used to derive word features from …


Automated Short Answer Grading: A Simple Solution for a Difficult Task

S Menini, S Tonelli, G De Gasperis, P Vittorini

… combining vectors representing both words and subwords. To generate these embeddings we start from the pre-computed Italian language model3 trained on Common Crawl and Wikipedia. The latter, in particular, is suitable …


Automatic Detection of Machine Generated Text: A Critical Survey

G Jawahar, M Abdul-Mageed, LVS Lakshmanan - arXiv preprint arXiv:2011.01314, 2020

… GPT-3 (Brown et al., 2020) fragments from CommonCrawl (570GB / 175B) three previous news articles and title of a proposed article body of the proposed article top-p fake news Table 1: Summary of the characteristics of TGMs that can act as threat models …


Page 1. UPTEC F 20043 Examensarbete 30 hp September 2020 Automatic language identification of short texts Anna Avenberg Page 2. Teknisknaturvetenskaplig fakultet UTH-enheten Besöksadress: Ångströmlaboratoriet …


Automatic Metaphor Interpretation Using Word Embeddings

K Bar, N Dershowitz, L Dankin - arXiv preprint arXiv:2010.02665, 2020

… relatively large corpus. Specifically, we use DepCC,1 a dependency-parsed “web-scale corpus” based on CommonCrawl.2 There are 365 million documents in the corpus, comprising about 252B tokens. Among other preprocessing …


T Van de Cruys - Proceedings of the 58th Annual Meeting of the …, 2020

Page 1. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2471–2480 July 5 - 10, 2020. c 2020 Association for Computational Linguistics 2471 Automatic Poetry Generation from Prosaic Text …


Page 1. Faculty of Electrical Engineering, Mathematics & Computer Science Automatic Short Answer Grading using Text-to-Text Transfer Transformer Model Stefan Haller M.Sc. Thesis in Business Information …


Automatic Speech Recognition for ILSE-Interviews: Longitudinal Conversational Speech Recordings covering Aging and Cognitive Decline

… In addition, we selected text data similar to ILSE from large amounts of Common Crawl Data1 based on Term Frequency–Inverse Document Frequency (tf-idf) for the training of Recurrent Neural Network (RNN) based …


S Kim, WW Chang, N Lipka, F Dernoncourt, CY Park - US Patent App. 16/368,334, 2020

US20200311207A1 - Automatic text segmentation based on relevant context - Google Patents. Automatic text segmentation based on relevant context. Download PDF Info. Publication number US20200311207A1. US20200311207A1 …


Z Liu, X Xia, M Yan, S Li - Automated Software Engineering, to appear, 2020

Page 1. Automating Just-In-Time Comment Updating Zhongxin Liu∗† Zhejiang University China liu_zx@zju.edu.cn Xin Xia‡ Monash University Australia xin.xia@monash.edu Meng Yan Chongqing University China mengy@cqu.edu.cn …


Automating Text Naturalness Evaluation of NLG Systems

E Çano, O Bojar - arXiv preprint arXiv:2006.13268, 2020

… The model is trained on RealNews, a large news collection they derived from Page 4. 4 E. Çano and O. Bojar Common Crawl1 dumps … They are typically assessed by 1 https://commoncrawl.org/ 2 http://www.statmt.org …


BanFakeNews: A Dataset for Detecting Fake News in Bangla

MZ Hossain, MA Rahman, MS Islam, S Kar - arXiv preprint arXiv:2004.08789, 2020

… words in it. We experiment with the Bangla 300 dimensional word vectors pre-trained7 with Fasttext (Grave et al., 2018) on Wikipedia8 and Common Crawl9, where we have a coverage of 55.21%. Additionally, we experiment …


Bangla Text Classification using Transformers

T Alam, A Khan, F Alam - arXiv preprint arXiv:2011.04446, 2020

… NSP task is removed and only MLM loss is used for pretraining. XLM-RoBERTa [18] is the multilingual variant of RoBERTa trained with a multilingual MLM. It is trained on one hundred languages, with more than two terabytes of filtered Common Crawl data …


Bankruptcy Map: A System for Searching and Analyzing US Bankruptcy Cases at Scale

E Choi, G Brassil, K Keller, J Ouyang, K Wang

… layers [14]. The network was pre-trained on the large OntoNotes dataset, with GloVe vectors used for feature creation trained on Common Crawl data [3, 16]. The model recognized named entities and their types. We collected …


BARThez: a Skilled Pretrained French Sequence-to-Sequence Model

MK Eddine, AJP Tixier, M Vazirgiannis - arXiv preprint arXiv:2010.12321, 2020

… Other than that, BARTHez corpus is similar to FlauBERT's. It primarily consists in the French part of CommonCrawl, NewsCrawl, Wikipedia and other smaller corpora that are listed in Table 1. To clean the corpus from noisy examples …


Benchmarking Neural and Statistical Machine Translation on Low-Resource African Languages

… The columns CommonCrawl and Wikipedia indicate the amount of monolingual data on the web, which can be viewed as an indicator of the upper limit of how much web-crawled data we may be able to obtain. CommonCrawl …


BERT-based Ensembles for Modeling Disclosure and Support in Conversational Social Media Text

… Gokaslan, A., Cohen, V.: Openwebtext corpus. http://Skylion007.github.io/ OpenWebTextCorpus (2019) 5. Nagel, S.: Cc-news (2016), http://web.archive.org/save/ http://commoncrawl. org/2016/10/newsdataset-available/ 6. Rajendran …


BERT-Based Simplification of Japanese Sentence-Ending Predicates in Descriptive Text

T Kato, R Miyata, S Sato - Proceedings of the 13th International Conference on …, 2020

… For any parts having more than one word, the average of the embedding is used. To obtain the embedding vectors, we used existing Japanese pre-trained word vectors that were trained on Common Crawl and Wikipedia using fastText.7 …


BERTweet: A pre-trained language model for English Tweets

DQ Nguyen, T Vu, AT Nguyen - arXiv preprint arXiv:2005.10200, 2020

… The pre-trained RoBERTa is a strong language model for English, learned from 160GB of texts covering books, Wikipedia, CommonCrawl news, CommonCrawl stories, and web text contents. XLM-R is a cross-lingual variant …


Page 1. Masaryk University Faculty of Informatics Better Web Corpora For Corpus Linguistics And NLP Doctoral Thesis Vít Suchomel Brno, Spring 2020 Page 2. Masaryk University Faculty of Informatics Better Web Corpora …


A Fan, S Bhosale, H Schwenk, Z Ma, A El-Kishky… - arXiv preprint arXiv …, 2020

Page 1. Beyond English-Centric Multilingual Machine Translation Angela Fan∗, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary …


Beyond Instructional Videos: Probing for More Diverse Visual-Textual Grounding on YouTube

J Hessel, Z Zhu, B Pang, R Soricut - arXiv preprint arXiv:2004.14338, 2020

… corresponding visual content. However, in contrast to the highly diverse corpora utilized for text-based pretraining (Wikipedia, Common Crawl, etc.), pretraining for web videos so far has been limited to instructional videos. This domain …


Page 1. NORTHWESTERN UNIVERSITY Biases as Values: Evaluating Algorithms in Context A DISSERTATION SUBMITTED TO THE GRADUATE SCHOOL IN PARTIAL FULFILLMENT OF THE REQUIREMENTS for the degree DOCTOR OF PHILOSOPHY …


Page 1. Biomedical Information Extraction Pipelines for Public Health in the Age of Deep Learning by Arjun Magge Ranganatha A Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy …


Blocking Techniques for Entity Linkage: A Semantics-Based Approach

F Azzalini, S Jin, M Renzi, L Tanca - Data Science and Engineering, 2020

… each attribute value \(t[A_{k}]\) is transformed into a real-valued vector \(\mathbf{v }(w)\). The fastText model we use is crawl-300d-2M-subword [23] where each word is represented as a 300-dimensional vector and the …


Bottom-Up Modeling of Permissions to Reuse Residual Clinical Biospecimens and Health Data

Page 1. Bottom-Up Modelling of Permissions to Reuse Residual Clinical Biospecimens and Health Data by Elizabeth Umberfield A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor …


BREXIT: Psychometric Profiling the Political Salubrious through Machine Learning

… We used the English multi-task Convoluted Neural Network trained on OntoNotes, with GloVe vectors trained on Common Crawl, which assigns word vectors, context-specific token vectors, POS tags, dependency parse …


BREXIT: Psychometric Profiling the Political Salubrious through Machine Learning: Predicting personality traits of Boris Johnson through Twitter political text

J Usher, P Dondio - Proceedings of the 10th International Conference on …, 2020


Building a user-generated content north-african arabizi treebank: Tackling hell

D Seddah, F Essaidi, A Fethi, M Futeral, B Muller… - Proceedings of the 58th …, 2020

… used data-driven language identification models to extract NArabizi samples among the whole collection of the Common-Crawl-based OSCAR … one based on search query-based web-crawling and the other from a cleaned version …


Building a Wide Reach Corpus for Secure Parser Development

T Allison, W Burke, V Constantinou, E Goh…

… [17] CGR Lavanya Pamulaparty and MS Rao, “A novel approach for avoiding overload in the web crawling.” Odisha, India: High Performance Computing and Applications (ICHPCA), 2014. [18] “Common Crawl,” https://commoncrawl.org …


Building LARO: Language Agnostic Sentence Embeddings from finetuned RoBERTa⋆

… XLM-RoBERTa is the successor of RoBERTa and a large multi-lingual language model. It is a Transformer-based model, technically a Transformer en- coder, and was trained on 2.5TB of filtered CommonCrawl data in 100 …


Building Web Corpora for Minority Languages

H Jauhiainen, T Jauhiainen, K Lindén - Proceedings of the 12th Web as Corpus …, 2020

… Common Crawl Foundation3 regularly crawls the Internet and offers the texts it finds for free download. Smith et al … Kanerva et al. (2014) used the morphological analyser OMorFi4 to find Finnish sentences in the Common Crawl corpus …


Caliskan Et Al-authors-full

… We use the largest of the four corpora provided—the “Common Crawl” corpus obtained from a large-scale crawl of the web, containing 840 billion tokens (roughly, words). Tokens in this corpus are casesensitive, resulting in 2.2 million different ones …


CALM: Continuous Adaptive Learning for Language Modeling

K Arumae, P Bhatia - arXiv preprint arXiv:2004.03794, 2020

… We processed publicly available biomedical and non-biomedical corpora for pre-training our models. For non-biomedical data, we use BookCorpus and English Wikipedia data, CommonCrawl Stories (Trinh and Le, 2018), and OpenWebText (Gokaslan and Cohen) …


Can Embeddings Adequately Represent Medical Terminology? New Large-Scale Medical Term Similarity Datasets Have the Answer!

C Schulz, D Juric - arXiv preprint arXiv:2003.11082, 2020

… 3) Non-medical: As a comparison, we also include - the GloVe word embedding (Pennington, Socher, and Manning 2014); - 2 Fasttext embeddings trained on Wikipedia and Common Crawl (plus its model (M)) (Mikolov et al. 2018) …


Can Emojis Convey Human Emotions? A Study to Understand the Association between Emojis and Emotions

AAM Shoeb, G de Melo - Proceedings of the 2020 Conference on Empirical …, 2020

… Here, sim(v1,v2) denotes the cosine similarity be- tween two vectors. We first consider the widely used 300dimensional GloVe (Pennington et al., 2014) models pretrained on CommonCrawl 840B and Twitter, as these contain emojis …


M Squarcina, M Tempesta, L Veronese, S Calzavara… - arXiv preprint arXiv …, 2020

Page 1. Can I Take Your Subdomain? Exploring Related-Domain Attacks in the Modern Web Marco Squarcina1, Mauro Tempesta1, Lorenzo Veronese1, Stefano Calzavara2, Matteo Maffei1 1TU Wien, 2Università Ca' Foscari Venezia Abstract …


Can Knowledge Rich Sentences Help Language Models to Solve Common Sense Reasoning Problems?

… 20 Page 33. Figure 3. RoBERTa network architecture 2. CommonCrawl News was used, which contained 63 million news articles between September 2016 and February 2019 3. OPENWEBTEXT (Gokaslan and Cohen 2019) which is a text corpus containing …


Capturing Word Order in Averaging Based Sentence Embeddings

JH Lee, JC Collados, LE Anke, S Schockaert

… two million words. These word vectors were trained on Common Crawl with 600 billion tokens [19]. The sentences that we used for training, validation and testing were obtained from an English Wikipedia dump. All sentences …


Page 1. Caspar: Extracting and Synthesizing User Stories of Problems from App Reviews Hui Guo Secure Computing Institute North Carolina State University Raleigh, North Carolina hguo5@ncsu.edu Munindar P. Singh Secure …


Page 1. CatchPhish: A URL and Anti-Phishing Research Platform Stephen Waddell MInf Project (Part 2) Report Master of Informatics School of Informatics University of Edinburgh 2020 Page 2. Page 3. 3 Abstract In this work, I …


CauseNet: Towards a Causality Graph Extracted from the Web

S Heindorf, Y Scholten, H Wachsmuth, ACN Ngomo…

… Web To extract causal relations from the web at scale, we analyze the ClueWeb12 web crawl, which comprises about 733,019,372 English web pages crawled between February and May 2012.3 We chose this crawl over …


CC-News-En: A Large English News Corpus

J Mackenzie, R Benham, M Petri, JR Trippas…

… Temporal Growth. The Common Crawl foundation are constantly adding new documents to CC-News … 10DMOZ is now superseded by Curlie: https://www.curlie. org 11https://github.com/commoncrawl/news-crawl/issues/8 Page 4 …


CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs

A El-Kishky, V Chaudhary, F Guzmán, P Koehn - Proc. of EMNLP, 2020

… we mined over 392 million aligned documents (100M with English and 292M without English) across 68 Common Crawl snapshots. We assess the efficacy of this rule-based alignment in the next section. We select a small subset …


Chart-based Zero-shot Constituency Parsing on Multiple Languages

… (2019)), the XLM model trained with masked language modeling on 100 languages (XLM, Conneau and Lample (2019)), and the XLM-R and XLM-R-large models that are trained with the filtered CommonCrawl data (Wenzek et al. 2019) by Conneau et al. (2019) …


ChemTables: A Dataset for Semantic Classification of Tables in Chemical Patents

Z Zhai, C Druckenbrodt, C Thorne, SA Akhondi… - 2020

… This model and the baselines it compares to are evaluated on a web table dataset, built by extracting tables from top 500 web pages which contain the highest numbers of tables in a subset of the April 2016 Common Crawl corpus [20] …


Circles are like Ellipses, or Ellipses are like Circles? Measuring the Degree of Asymmetry of Static and Contextual Embeddings and the Implications to Representation …

W Zhang, M Campbell, Y Yu, S Kumaravel - arXiv preprint arXiv:2012.01631, 2020

Page 1. Circles are like Ellipses, or Ellipses are like Circles? Measuring the Degree of Asymmetry of Static and Contextual Word Embeddings and the Implications to Representation Learning Wei Zhang 1, Murray Campbell 1 …


CiTIUS at the TREC 2020 Health Misinformation Track

M Fernández-Pichel, DE Losada, JC Pichel…

… 2 DOCUMENTS AND TOPICS In the TREC 2020 Health Misinformation Track, a news corpus from January 2020 to April 2020 was provided. The documents were obtained from CommonCrawl News, which contains news articles from all over the world …


Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network

M Karim, BR Chakravarthi, M Arcan, JP McCrae… - arXiv preprint arXiv …, 2020

… The fourth one called fastText [17], which is trained on common crawl and Wikipedia using CBOW with position-weights, in di- mension 300, with character n-grams of length 5, a window of size 5 and 10 negatives. Eventually …


Page 1. PHD PROGRAM IN SMART COMPUTING DIPARTIMENTO DI INGEGNERIA DELL'INFORMAZIONE (DINFO) Classification of cancer pathology reports with Deep Learning methods Stefano Martina Dissertation presented …


Classification of cancer pathology reports: a large-scale comparative study

S Martina, L Ventura, P Frasconi - IEEE Journal of Biomedical and Health Informatics, 2020

… GloVe) [20]). It is a common practice to use pre-compiled libraries of word vectors trained on several billion tokens extracted from various sources such as Wikipedia, the English Gigaword 5, Common Crawl, or Twitter. These …


Classification of Cyberbullying Text in Arabic

BA Rachid, H Azza, HHB Ghezala - 2020 International Joint Conference on Neural …, 2020

… The second set of pre-trained embeddings is the one provided in [24], in which word vectors were trained on online encyclopedia Wikipedia and the Common Crawl corpus using an extension of the fastText model (Fasttext embeddings) …


Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection

E Raff, W Fleshman, R Zak, HS Anderson, B Filar… - arXiv preprint arXiv …, 2020

… This better demonstrates the gap between current deep learning and domain knowledge based approaches for classifying malware. We also use the Common Crawl to collect 676,843 benign PDF 103 104 105 106 107 108 …


CLEF eHealth Evaluation Lab 2020

… This collection consists of over 5 million medical webpages from selected domains acquired from the CommonCrawl [7]. Given the positive feedback received for this document collection, it will be used again in the 2020 CHS task …


Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation

K Huang, A Singh, S Chen, ET Moseley, C Deng… - arXiv preprint arXiv …, 2019

… Pretraining Clinical XLNet. The text representation generated from large pre-training models depends on the corpus it is pre-trained on. XLNet is pre-trained on common language corpora such as BookCorpus, Wikipedia, Common Crawl and etc …


CLUE: A Chinese Language Understanding Evaluation Benchmark

L Xu, X Zhang, L Li, H Hu, C Cao, W Liu, J Li, Y Li… - arXiv preprint arXiv …, 2020

… CLUECorpus2020 (Xu et al., 2020) It contains 100 GB Chinese raw corpus, which is retrieved from Common Crawl … CLUEOSCAR6 OSCAR is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus …


CLUECorpus2020: A Large-scale Chinese Corpus for Pre-trainingLanguage Model

L Xu, X Zhang, Q Dong - arXiv preprint arXiv:2003.01355, 2020

… Common Crawl is an organization that crawls the web and freely provides its archives and datasets to the public. Common Crawl usually crawls internet web content once a month. Common Crawl's web archives consist of petabytes of data collected since 2011 …


Clustering Approach to Topic Modeling in Users Dialogue

E Feldina, O Makhnytkina - Proceedings of SAI Intelligent Systems Conference, 2020

… FastText using FastText weights based on the pre-trained CBOW model with a word window size of five trained on Common Crawl and Wikipedia, vector size 300. Table 2. Results of the implementation of the clustering …


CO-EVOLUTION OF CULTURE AND MEANING REVEALED THROUGH LARGE-SCALE SEMANTIC ALIGNMENT

… We also replicated on word embeddings derived from the OpenSubtitles database (Li- son & Tiedemann, 2016) and a combination of Wikipedia and the Common Crawl dataset (Grave, Bojanowski, Gupta, Joulin, & Mikolov, 2018)) …


COD3S: Diverse Generation with Discrete Semantic Signatures

N Weir, J Sedoc, B Van Durme - arXiv preprint arXiv:2010.02882, 2020

… The model is trained on the co-released corpus CausalBank, which comprises causal statements harvested from English Common Crawl (Buck et al., 2014) … 2014. N-gram counts and language models from the common crawl …


Combination of Neural Machine Translation Systems at WMT20

B Marie, R Rubino, A Fujita - Proceedings of the Fifth Conference on Machine …, 2020

… As En- glish monolingual data, we used all the provided data, but sampled only 200M lines from the “Common Crawl” corpora, except the “News Discussions” and “Wiki Dumps” corpora … corpora but also sampled only …


Combinatorial feature embedding based on CNN and LSTM for biomedical named entity recognition

M Cho, C Park, J Ha, S Park - Journal of Biomedical Informatics, 2020

JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main content Skip to article …


Combining Character and Word Embeddings for Affect in Arabic Informal Social Media Microblogs

AI Alharbi, M Lee - International Conference on Applications of Natural …, 2020

… Here, the researchers derived the training data from three separate sources: Wikipedia, Twitter and Common Crawl webpages crawl data; they employed two word-level models to learn word representations for general NLP tasks …


Combining Different Parsers and Datasets for CAPITEL UD Parsing

F Sánchez-León - Proceedings of the Iberian Languages Evaluation …, 2020

… Besides, we have used fastText word embeddings trained on Common Crawl and Wikipedia corpora.5 Table 1 shows results on development set of a model built with the training material using different word embeddings.6 Increasing …


Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers

R Barman, M Ehrmann, S Clematide, SA Oliveira… - arXiv preprint arXiv …, 2020

… First, four pre-trained embeddings of the Flair library14 are used with their default implementation settings, as follows: - fastText-fr, ie the French fastText embeddings of size 300 pre-trained on Common Crawl and Wikipedia; …


Commonsense Aesthetics

Page 1. COMMONSENSE AESTHETICS Aaron Kurosu Roek A DISSERTATION PRESENTED TO THE FACULTY OF PRINCETON UNIVERSITY IN CANDIDACY FOR THE DEGREE OF DOCTOR OF …


Commonsense Learning: An Indispensable Path towards Human-centric Multimedia

B Huang, S Tang, G Shen, G Li, X Wang, W Zhu - … of the 1st International Workshop on …, 2020

… BERT uses Bookcorpus [84] and English Wikipedia with a size of about 13GB, while XLNet uses more than 100GB of corpus. T5 uses Colossal Clean Crawled Corpus captured from the Common Crawl website with a size of 750GB …


Communication-Efficient String Sorting

T Bingmann, P Sanders, M Schimek - arXiv preprint arXiv:2001.08516, 2020

Page 1. arXiv:2001.08516v1 [cs.DC] 23 Jan 2020 Communication-Efficient String Sorting Timo Bingmann, Peter Sanders, Matthias Schimek Karlsruhe Institute of Technology, Karlsruhe, Germany {bingmann,sanders}@kit.edu, matthias schimek@gmx.de …


Comparative Analysis of Deep Learning Models for Myanmar Text Classification

MS Phyu, KT Nwet - Asian Conference on Intelligent Information and …, 2020

… Grave et al. [3] published pre-trained word vectors for two hundred forty-six languages trained on common crawl and Wikipedia. They proposed bag-of-character n-grams based on skip-gram that could capture sub-word information to enrich word vectors …


Comparative Analysis of Machine Learning Algorithms for Computer-Assisted Reporting Based on Fully Automated Cross-Lingual RadLex® Mappings

ME Maros, CG Cho, AG Junge, B Kämpgen, V Saase… - 2020

… However, pre-trained word vector models for 157 languages, which were pre-trained on Common Crawl and Wikipedia by the fastText package authors are available for direct download (https://fasttext.cc/docs/en/crawl-vectors.html) [58] …


COMPARATIVE ANALYSIS OF SUBDOMAIN ENUMERATION TOOLS AND STATIC CODE ANALYSIS

… Certificates= censys,certspotter, Google CT APIs: AlienVault, BinaryEdge, BufferOver, CIRCL, CommonCrawl, DNSDB, GitHub, HackerTarget, NetworksDB, PassiveTotal, Pastebin.. Web Archives: ArchiveIt, ArchiveToday, Arquivo, Wayback and others …


Comparative Analysis of Word Embeddings for Capturing Word Similarities

M Toshevska, F Stojanovska, J Kalajdjieski - arXiv preprint arXiv:2005.03812, 2020

… architectures [23]. In our experiments, we have used pre-trained models both trained with subword information on Wikipedia 2017 (16B tokens) and trained with subword information on Common Crawl (600B tokens)4. 2 https …


Comparing Different Methods for Named Entity Recognition in Portuguese Neurology Text

F Lopes, C Teixeira, HG Oliveira - Journal of Medical Systems, 2020

… In order to check which was preferable, two different WE models were used: A pre-trained general Portuguese FastText model, Footnote 3 based on billions of tokens from Wikipedia and Common Crawl [41], with a 5-character window (general language) …


Comparing High Dimensional Word Embeddings Trained on Medical Text to Bag-of-Words for Predicting Medical Codes

V Yogarajan, H Gouk, T Smith, M Mayo, B Pfahringer - Asian Conference on …, 2020

… Our embeddings are trained to the exact same specifications as the Wikipedia and common crawl fastText models in [10] … For 300-dimensional embeddings, W300 are word embeddings that are trained by fastText on Wikipedia and other common crawl text …


Comparing Neural Network Parsers for a Less-resourced and Morphologically-rich Language: Amharic Dependency Parser

BE Seyoum, Y Miyao, BY Mekonnen - Proceedings of the first workshop on …, 2020

… For this purpose, we used the trained model for Amharic using fasttext7. The data for training the model is from Wikipedia and Common Crawl8. The models were trained using continuous bag of words (CBOW) …


Comparing pre-trained language models for Spanish hate speech detection

FM Plaza-del-Arco, MD Molina-González… - Expert Systems with …, 2020


Comparing Probabilistic, Distributional and Transformer-Based Models on Logical Metonymy Interpretation

G Rambelli, E Chersoni, A Lenci, P Blache, CR Huang - … of the 1st Conference of the …, 2020

… in random order. XLNet's training corpora were the same as BERT plus Giga5, ClueWeb 2012-B and Common Crawl, for a total of 32.89B subword piece. Also in this case, we used the large pre-trained model. GPT-2 (Radford …


Comparing supervised learning algorithms for Spatial Nominal Entity recognition

A Medad, M Gaio, L Moncla, S Mustière, YL Nir - AGILE: GIScience Series, 2020

… We have made the hypothesis that as FastText is a model of pretrained vectors (300 dimensions) on Wikipedia and Common Crawl, it provides a generic representation of words … CC BY 4.0 License. Page 12. Common Crawl and Wikipedia using the CBOW method …


Comparison between machine learning and human learning from examples generated with machine teaching

… For instance, progress in areas like NLP (Natural Language Processing) has led to the development of outstanding deep neural networks such as GPT-3, a task-agnostic model trained using huge data repositories like …


Comparison of Named Entity Recognition Tools Applied to News Articles

S Vychegzhanin, E Kotelnikov - 2019 Ivannikov Ispras Open Conference (ISPRAS), 2019

… spaCy Python MIT Bloom embeddings and a residual convolutional neural network en_core_web_sm OntoNotes en_core_web_md OntoNotes, Common Crawl en_core_web_lg OntoNotes, Common Crawl xx_ent_wiki_sm WikiNER ru2 …


Comprehensive Stereotype Content Dictionaries Using a Semi‐Automated Method

G Nicolas, X Bai, ST Fiske - European Journal of Social Psychology

… word embeddings used here are Word2Vec's model pretrained on Google News (Mikolov, Chen, Corrado, & Dean, 2013) and Glove' model pretrained on the Common Crawl (Pennington, Socher, & Manning, 2014; presented in Supplement) …


Page 1. Computational Approaches for Identifying Sensational Soft News Thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering by Research by Vijayasaradhi Indurthi 201450803 …


Page 1. 1 UNIVERSITY COLLEGE LONDON (UCL) Computational explorations of semantic cognition PHD THESIS Armand Stefan Rotaru Supervisors: Primary: Prof. Gabriella Vigliocco Secondary: Prof. Lewis Griffin Page 2. 2 …


Page 1. Computational Mechanisms of Language Understanding and Use in the Brain and Behaviour by Ivana Kajić A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Doctor of Philosophy in Computer Science …


Connections and selections: Comparing multivariate predictions and parameter associations from latent variable models of picture naming

GM Walker, J Fridriksson, G Hickok - Cognitive Neuropsychology, 2020

Connectionist simulation models and processing tree mathematical models of picture naming have complementary advantages and disadvantages. These model types were compared in terms of their predicti...


Constraining the Transformer NMT Model with Heuristic Grid Beam Search

… the training corpus consists of 4.4 Million segments from Europarl (Koehn, 2005) and CommonCrawl (Smith et al., 2013); … Smith, JR, Saint-Amand, H., Plamada, M., Koehn, P., Callison-Burch, C., and Lopez, A. (2013). Dirt cheap …


Contemporary Polish Language Model (Version 2) Using Big Data and Sub-Word Approach

… In this paper, we present a set of 6-gram language models based on a big-data training of the contemporary Polish language, using the Common Crawl corpus (a compilation of over 3.25 billion webpages) and other resources …


Context-aware Feature Generation for Zero-shot Semantic Segmentation

Z Gu, S Zhou, L Niu, Z Zhao, L Zhang - arXiv preprint arXiv:2008.06893, 2020

… in the supplementary. Following SPNet [43], we concatenate two different types of word embeddings (d = 600, 300 for each), ie, word2vec [30] trained on Google News and fast-Text [15] trained on Common Crawl. The word …


Contextual Question Answering with Improved Embedding Models

… In the GloVe word embedding based BiDAF++ model, we utilize pretrained GloVe(Pennington et al., 2014) embeddings with 300 output dimensions (840B.300d). These embeddings have been prertrained on a common crawl of 840 billion to- kens …


Contextualized Embeddings in Named-Entity Recognition: An Empirical Study on Generalization

B Taillé, V Guigue, P Gallinari - arXiv preprint arXiv:2001.08053, 2020

… 3 Word Representations Word embeddings map each word to a single vector which results in a lexical representation. We take GloVe 840B embeddings [13] trained on Common Crawl as the pretrained word embeddings baseline …


Contextualized Emotion Recognition in Conversation as Sequence Tagging

Y Wang, J Zhang, J Ma, S Wang, J Xiao - Proceedings of the 21th Annual Meeting of …, 2020

… GloVe vectors trained on Common Crawl 840B with 300 dimensions are used as fixed word em- beddings. We use a 12-layers 4-heads Transformer encoder of which the inner-layer dimensionality is 2048 and the hidden size is 100 …


Controllable Text Generation

Page 1. CARNEGIE MELLON UNIVERSITY Controllable Text Generation Should machines re ect the way humans interact in society? esis Proposal by Shrimai Prabhumoye esis proposal submi ed in partial ful llment for the degree of Doctor of Philosophy esis committee …


Z Jiang, W Yu, D Zhou, Y Chen, J Feng, S Yan - arXiv preprint arXiv:2008.02496, 2020

Page 1. ConvBERT: Improving BERT with Span-based Dynamic Convolution Zihang Jiang1∗, Weihao Yu1∗, Daquan Zhou1, Yunpeng Chen2, Jiashi Feng1, Shuicheng Yan2 1National University of Singapore, 2Yitu Technology …


Correcting the Autocorrect: Context-Aware Typographical Error Correction via Training Data Augmentation

K Shah, G de Melo - arXiv preprint arXiv:2005.01158, 2020

… from 2We do not rely on embeddings trained on CommonCrawl, as Web data contains substantially more misspelling forms. 3Specifically, those with a character length three standard deviations above or below mean. Hence …


Crawling the German Health Web: Exploratory Study and Graph Analysis

R Zowalla, T Wetter, D Pfeifer - Journal of Medical Internet Research, 2020

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet.


Creating semantic representations

FÅ Nielsen, LK Hansen - Statistical Semantics, 2020

… Evaluations in 2017 with fastText trained for 3 days on either the very large Common Crawl data set or a combination of the English Wikipedia and news datasets set a new state-of-the-art on 88.5% for the accuracy on a …


Page 1. FACULDADE DE ENGENHARIA DA UNIVERSIDADE DO PORTO Creation of a database based on artificial intelligence in order to understand the role played by biofilms on outbreaks Ana Patrícia Pinheiro de Melo Integrated Master in Bioengineering …


Page 1. Creative Natural Language Generation: Humor and Beyond by Nabil Tarique Hossain Submitted in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Supervised by Professor Henry Kautz and Dr …


Credibility Assessment of User Generated health information of the Bengali language in microblogging sites employing NLP techniques

… word. We use the pre-trained word vectors of Fasttext 5 over Word2Vec 6 and GloVe 7 since it has the best multi lingual word vectors amongst the three, supporting 157 languages, trained on Common Crawl and Wikipedia. We …


Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning--a Case Study on COVID-19

AS Imran, SM Doudpota, Z Kastrati, R Bhatra - arXiv preprint arXiv:2008.10031, 2020

… corpora, a 2010 Wikipedia dump with 1 billion tokens; a 2014 Wikipedia dump with 1.6 billion tokens; Gigaword 5 which has 4.3 billion tokens; the combination Gigaword5 + Wikipedia2014, which has 6 billion tokens; and …


Cross-lingual Inductive Transfer to Detect Offensive Language

K Pant, T Dadu - arXiv preprint arXiv:2007.03771, 2020

… We further finetune XLM-R, pretrained on 2.5 TB Common Crawl corpus spanning 100 languages … XLM-R is a transformer-based cross-lingual model pretrained using a multilingual masked language model objective on 2.5 …


Cross-Lingual Information Retrieval in the Medical Domain

Page 1. DOCTORAL THESIS Shadi Saleh Cross-lingual Information Retrieval in the Medical Domain Institute of Formal and Applied Linguistics Supervisor of the doctoral thesis: doc. RNDr. Pavel Pecina, PhD. Study programme …


Cross-Lingual Relation Extraction with Transformers

J Ni, T Moon, P Awasthy, R Florian - arXiv preprint arXiv:2010.08652, 2020

… Conneau et al., 2020). mBERT was pre-trained with Wikipedia text of 104 languages with the largest sizes, and XLM-R were pre-trained with Wikipedia text and CommonCrawl Corpus of 100 languages. Both models use no …


Cross-lingual Retrieval for Iterative Self-Supervised Training

C Tran, Y Tang, X Li, J Gu - arXiv preprint arXiv:2006.09526, 2020

… 5 Experiment Evaluation We pretrained an mBART model with Common Crawl dataset constrained to the 25 languages as in [19] for which we have evaluation data … We subsample the resulting common crawl data to 100 million sentences in each language …


Cross-lingual Transfer Learning for Semantic Role Labeling in Russian

… The model is also based on Transformer architecture (Vaswani et al., 2017). We applied the XLM-R Masked Language Model, which is pretrained on 2.5 TB of CommonCrawl data, in 100 languages, with 8 heads, 6 layers, 1024 hidden units per layer …


Cross-Lingual Word Embeddings for Turkic Languages

E Kuriyozov, Y Doval, C Gómez-Rodríguez - arXiv preprint arXiv:2005.08340, 2020

… Cross-lingual embeddings used for both experiments were trained under the following conditions: • Monolingual word embeddings were obtained from available pre-trained word vectors (Grave et al., 2018) trained on …


Cross-Modal Transfer Learning for Multilingual Speech-to-Text Translation

C Tran, C Wang, Y Tang, Y Tang, J Pino, X Li - arXiv preprint arXiv:2010.12829, 2020

… This model is pretrained using two types of noise in g — random span masking and order permutation — as described in [3]. We re-use the finetuned mBART50 models from [13] which are pretrained on …


CS-NLP team at SemEval-2020 Task 4: Evaluation of State-of-the-artNLP Deep Learning Architectures on Commonsense Reasoning Task

S Saeedi, A Panahi, S Saeedi, AC Fong - arXiv preprint arXiv:2006.01205, 2020

… The architecture of RoBERTalarge is comprised of of 24-layer, 1024-hidden dimension, 16-self attention heads, 355M parameters and pretrained on book corpus plus English Wikipedia, English CommonCrawl News, and WebText corpus …


Culprit Analytics from Detective Novels

Page 1. Culprit Analytics from Detective Novels Thesis submitted in partial fulfillment of the requirements for the degree of Masters of Science in Computer Science and Engineering by Research by Aditya Motwani …


Cultural Cartography with Word Embeddings

DS Stoltz, MA Taylor - arXiv preprint arXiv:2007.04508, 2020

… fastText embeddings are trained on Wikipedia data dumps and the 25 billion web pages of the Common Crawl), and thus are not trained on the researcher's own corpus. Corpus-trained embeddings, by contrast, are word vectors trained exclusively on the …


Cultural Differences in Bias? Origin and Gender Bias in Pre-Trained German and French Word Embeddings

… The validation experiments in English were executed on the same pre-trained word embeddings as in the original experiments (Caliskan et al., 2017): • GloVe pre-trained word embeddings using the ”Common …


Cultural influences on word meanings revealed through large-scale semantic alignment

B Thompson, SG Roberts, G Lupyan - Nature Human Behaviour, 2020

If the structure of language vocabularies mirrors the structure of natural divisions that are universally perceived, then the meanings of words in different languages should closely align. By contrast, if shared word meanings are …


Current Limitations of Language Models: What You Need is Retrieval

A Komatsuzaki - arXiv preprint arXiv:2009.06857, 2020

… Most naturally available samples as well as the reasonable output of most tasks have rather limited length, though others (eg books) do not. For example, the average sample length of WebText is only about 1000 tokens …


Curriculum Pre-training for End-to-End Speech Translation

C Wang, Y Wu, S Liu, M Zhou, Z Yang - arXiv preprint arXiv:2004.10093, 2020

… (2017) 6LibriSpeech En-Fr, IWSLT En-De and Fisher-CallHome Es-En 7https://wit3.fbk.eu/mt.php?release= 2017-01-trnted 8Europarl v7, Common Crawl, News Comentary v13 and Rapid corpus of EU press releases. using …


CX DB8: A queryable extractive summarizer and semantic search engine

A Roush - arXiv preprint arXiv:2012.03942, 2020

… unsupervised models. Since unsupervised models are usually trained on massive corpuses, like Wikipedia or Common Crawl (Penninglon et al., 2014), they do not overfit as much to any particular topic or domain. Furthermore …


Dávid Márk Nemeskey Natural Language Processing Methods for Language Modeling

CV Erzsébet, Z Horváth, A Benczúr, A Kornai

… 83 4.2.2 Common Crawl . . . . . 84 … Chapter 4 details our work of compiling Webcorpus 2.0, a new Hungarian gigaword corpus, from the Common Crawl and the Hungarian Wikipedia. Its main purpose being a …


DAN+: Danish Nested Named Entities and Lexical Normalization

… Twitter data. Bert variants For Danish BERT we use the model trained by Botxo (https://github.com/ botxo/nordic_bert), which is pre-trained on Wikipedia, Common Crawl, Danish debate forums and Danish open subtitles. For …


Page 1. Danish Clinical Event Extraction Developing a clinical event extraction system for electronic health records using deep learning and active learning FREDERIK WONSILD MATHIAS GIOVANNI MØLLER Master's thesis …


Data augmentation techniques for the Video Question Answering task

A Falcon, O Lanz, G Serra - arXiv preprint arXiv:2008.09849, 2020

… To compute the word embeddings for the question and the answers, we consider GloVe [23], pretrained on the Common Crawl dataset3, which outputs a vector of size E = 300 for 3 The Common Crawl dataset is available …


Data selection for unsupervised translation of German–Upper Sorbian

L Edman, A Toral, G van Noord - Proceedings of the Fifth Conference on Machine …, 2020

… gz. For German, we use monolingual data from News Crawl and Common Crawl … 19.57 Table 3: BLEU scores of models trained using 5 million sentences from News Crawl and various amounts of sentences from Common Crawl …


Page 1. Data-driven Crosslinguistic Modeling of Constituent Ordering Preferences By Zoey (Ying) Liu Dissertation Submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Linguistics in the Office of Graduate Studies of the …


Data-driven models and computational tools for neurolinguistics: a language technology perspective

E Artemova, A Bakarov, A Artemov, E Burnaev… - arXiv preprint arXiv …, 2020

… The corpus size can be estimated by the number of tokens8: so, the size of English Wikipedia is 1M tokens, the size of Google news corpus is 1B tokens, and the size of CommonCrawl corpus is 600B tokens. Structured expert-based …


Dataless Short Text Classification Based on Biterm Topic Model and Word Embeddings

Y Yang, H Wang, J Zhu, Y Wu, K Jiang, W Guo, W Shi

… We set the number of iterations to 50 as our models achieve competitive performance since then. For word embeddings, we employ the widely used GloVe Common Crawl as mentioned before. It contains 840B to- kens, 2.2M vocab and 300d vectors …


Dataset for Automatic Summarization of Russian News

I Gusev - arXiv preprint arXiv:2006.11063, 2020

… 3.3 Abstractive methods All of the tested models are based on a sequence-to-sequence framework. Pointergenerator and CopyNet were trained only on our train dataset, and mBART was pretrained on texts of 25 languages extracted from the Common Crawl …


Dataset for Automatic Summarization of Russian

… sequence-to-sequence framework. Pointer-generator and CopyNet were trained only on our training dataset, and mBART was pretrained on texts of 25 languages extracted from the Common Crawl. We performed no additional …


Datasets and Performance Metrics for Greek Named Entity Recognition

N Bartziokas, T Mavropoulos, C Kotropoulos - 11th Hellenic Conference on Artificial …, 2020

… performance. Thus, most works omit such supplementary features. Established word embeddings, usually pre-trained on large corpora, such as Common Crawl's or Wikipedia's collections, are fine-tuned to a great extent. Google …


DeBERTa: Decoding-enhanced BERT with Disentangled Attention

P He, X Liu, J Gao, W Chen - arXiv preprint arXiv:2006.03654, 2020

… the setting of BERT [4], except that we use the BPE vocabulary as [2, 5]. For training data, we use Wikipedia (English Wikipedia dump3; 12GB), BookCorpus [26] (6GB), OPENWEBTEXT (public Reddit content [27]; 38GB) …


DECAB-LSTM: Deep Contextualized Attentional Bidirectional LSTM for cancer hallmark classification

L Jiang, X Sun, F Mercaldo, A Santone - Knowledge-Based Systems, 2020

JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Skip to main content Skip to article …


Decoding individual identity from brain activity elicited in imagining common experiences

AJ Anderson, K McDermott, B Rooks, KL Heffner… - Nature Communications, 2020

Everyone experiences common events differently. This leads to personal memories that presumably provide neural signatures of individual identity when events are reimagined. We present initial evidence that these signatures …


Deep Exogenous and Endogenous Influence Combination for Social Chatter Intensity Prediction

S Dutta, S Masud, S Chakrabarti, T Chakraborty - arXiv preprint arXiv:2006.07812, 2020

… news on discussions. We report on extensive experiments using a two-month-long discussion corpus of Reddit, and a contemporaneous corpus of online news articles from the Common Crawl. ChatterNet shows considerable …


Deep Intelligent Contextual Embedding for Twitter Sentiment Analysis

K Musial-Gabrys, U Naseem - International Conference on Document Analysis and …, 2019

… and GloVe. In our model we have used pre-trained GloVe embedding of 300 dimensions which are trained on 840 billion token from common crawl because it gives better results as compared to Word2Vec in our case. GloVe …


Deep Learning Based Multi-Label Text Classification of UNGA Resolutions

F Sovrano, M Palmirani, F Vitali - arXiv preprint arXiv:2004.03455, 2020

… The pre-trained models we are going to use are: a GloVe model from Spacy [19] and pre-trained on data from Common Crawl [20], and the Universal Sentence Encoder (USE) model for document embedding coming from …


Deep Learning for Twitter Sentiment Analysis: The Effect of Pre-trained Word Embedding

A Krouska, C Troussas, M Virvou - Machine Learning Paradigms, 2020

… The model contains 300-dimensional vectors for 3 million words and phrases. Crawl GloVe was trained on a Common Crawl dataset of 42 billion tokens (words), providing a vocabulary of 2 million words with an embedding vector …


Deep learning model for end-to-end approximation of COSMIC functional size based on use-case names

M Ochodek, S Kopczyńska, M Staron - Information and Software Technology, 2020

… we investigate different pre-trained word embeddings to learn that using the embeddings trained on Wikipedia+Gigaworld (300d), Common Crawl 840B/42B (300d), and Stack Overflow (200d) give the best prediction accuracy. This paper is structured as follows …


Deep N-ary Error Correcting Output Codes

H Zhang, JT Zhou, T Wang, IW Tsang, RSM Goh - arXiv preprint arXiv:2009.10465, 2020

… For the Bi-LSTMs model of TREC and SST text datasets, we use the 300-dimensional publicly available pre-trained word embeddings as the word-level feature representation, which is trained by fastText4 package …


Deep Neural Attention-Based Model for the Evaluation of Italian Sentences Complexity

D Schicchi, G Pilato, GL Bosco - 2020 IEEE 14th International Conference on …, 2020

… based algorithms. To the best of our knowledge, the most prominent sentence-based corpus for the Italian language is the PACCSS-IT corpus [19]. It has 1www.wikipedia. org 2www.commoncrawl.org 254 Page 3. been created …


Deep Neural Networks Ensemble with Word Vector Representation Models to Resolve Coreference Resolution in Russian

A Sboev, R Rybka, A Gryaznov - Advanced Technologies in Robotics and Intelligent …, 2020

… Among the context-insensitive vectorization models, the following were compared: Word2vec model, 3 trained on corpus of articles from the Russian part of Wikipedia (further referred to as RuWiki) and data from CommonCrawl 4 ; …


Deep Neural Networks for Sentiment Analysis in Tweets with Emoticons

Page 1. DEEP NEURAL NETWORKS FOR SENTIMENT ANALYSIS IN TWEETS WITH EMOTICONS by Mutharasu Narayanaperumal A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Systems …


Page 1. Alma Mater Studiorum · Universit`a di Bologna SCUOLA DI SCIENZE Corso di Laurea Magistrale in Informatica Deep Question Answering: A New Teacher For DistilBERT Relatore: Chiar.mo Prof. Fabio Tamburini Correlatore: Chiar.mo Prof. Philipp Cimiano …


DeepPhish: Automated Phishing Detection Using Recurrent Neural Network

M Arivukarasi, A Antonidoss - Advances in Smart System Technologies

… At that point, we prepared an irregular timberland classifier with 100 choice trees. 6 Conclusions. To assess the methodologies, we utilized a database that included one million real URLs from the common crawl database and …


DeepSinger: Singing Voice Synthesis with Data Mined From the Web

Y Ren, X Tan, T Qin, J Luan, Z Zhao, TY Liu - arXiv preprint arXiv:2007.04590, 2020

… A variety of tasks collect training data from the Web, such as the large-scale web-crawled text dataset ClueWeb [3] and Common Crawl3 for language modeling [40], LETOR [31] for search ranking [4], and WebVision [22] for image classification …


Definition of Phishing Sites Based on the Team Model of Fuzzy Neural Networks

II Ismagilov, AA Murtazin, DV Kataseva, AS Katasev… - Helix, 2020

… To obtain a set of data on legitimate sites, two sources were used: Alexa Internet and Common Crawl … Common Crawl is a non-profit organization; it crawls monthly the Internet and makes its archives and datasets available …


DeL-haTE: A Deep Learning Tunable Ensemble for Hate Speech Detection

J Melton, A Bagavathi, S Krishnan - arXiv preprint arXiv:2011.01861, 2020

… We compare the following five word embedding methods: Word2Vec vectors trained on Google News corpus [17], GloVe vectors trained on CommonCrawl (GLoVe-CC) and Twitter (GLoVe-Twitter) corpora [18], and FastText vectors …


Delay Mitigation for Backchannel Prediction in Spoken Dialog System

AI Adiba, T Homma, D Bertero, T Sumiyoshi… - Conversational Dialogue Systems …

… individual word. The word embedding is then used as input for our model architecture. We found that our dataset in the fastText model trained with the Common Crawl dataset 2 had the smallest number of unknown words. Thus, the …


DeLFT and entity-fishing: Tools for CLEF HIPE 2020 Shared Task

T Kristanti, L Romary - CLEF 2020-Conference and Labs of the Evaluation …, 2020

… Word Embeddings We use various static word embeddings: Global Vectors for Word Representation (GloVe) [14], English fastText Common Crawl [1,11], and French Wikipedia fastText.5 We also use ELMo [16] contextualized …


Depthwise Separable Convolutional Neural Network for Confidential Information Analysis

Y Lu, J Jiang, M Yu, C Liu, C Liu, W Huang, Z Lv - International Conference on …, 2020

… Word2Vec. The Word2VecModified-Wikipedia are trained on Wikipedia through modified Word2vec. The GloVe-Crawl840B are trained on Common Crawl through GloVe. The GloVe-Wikipedia are trained on Wikipedia through GloVe …


Design2Struct: Generating website structures from design images using neural networks

… The second contribution is the release of a large CommonCrawl2 based dataset, filtered and transformed to be used in the field of GUI to structure conversion. The dataset is 1https://github.com/mvelzel/Design2Struct 2https://commoncrawl.org …


Detecting Alzheimer's Disease by Exploiting Linguistic Information from Nepali Transcript

S Thapa, S Adhikari, U Naseem, P Singh, G Bharathy… - International Conference on …, 2020

… For pre-trained Nepali Word2Vec model, the model created by Lamsal [15] is used in the study. Similarly, for the pre-trained fastText embeddings, the pre-trained word vectors trained on Common Crawl and Wikipedia using fastText were used [14] …


Detecting and Visualizing Hate Speech in Social Media: A Cyber Watchdog for Surveillance

S Modha, P Majumder, T Mandl, C Mandalia - Expert Systems with Applications, 2020

… The word vectors were trained on 600 billion tokens of the Common Crawl corpus (Simonite, 2013). The Common Crawl is a nonprofit organization that crawls the web and freely provides its archives and datasets to the public …


Detecting Deceptive Language in Crime Interrogation

YY Kao, PH Chen, CC Tzeng, ZY Chen, B Shmueli… - International Conference on …, 2020

… fastText is a lightweight library for text representation. Its pre-trained model, trained on Common Crawl and Wikipedia corpus, has the ability to capture hidden information about a language such as word analogies or semantic …


Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases

W Guo, A Caliskan - arXiv preprint arXiv:2006.03955, 2020

… intersectional group members. Caliskan et al. [1] have shown that social biases are embedded in linguistic regularities learned by GloVe. These embeddings are trained on the word co-occurrence statistics of the Common Crawl corpus …


Detecting Entailment in Code-Mixed Hindi-English Conversations

S Chakravarthy, A Umapathy, AW Black - Proceedings of the Sixth Workshop on …, 2020

… XLM-RoBERTa (XLM-R) (Conneau et al., 2020) is trained on the CommonCrawl corpus, which in- cludes Romanized Hindi text, making this model the closest one to being pre-trained on Hinglish. 3 Task Definition Khanuja et al …


Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank

E Briakou, M Carpuat - arXiv preprint arXiv:2010.03662, 2020

… (2018) work on subtitles and Common Crawl corpora where sentence alignment errors abound, and Pham et al … di- vergence English-French parallel sentences drawn from OpenSubtitles and CommonCrawl corpora by prior work (Vyas et al., 2018) …


Detecting Hallucinated Content in Conditional Neural Sequence Generation

C Zhou, J Gu, M Diab, P Guzman, L Zettlemoyer… - arXiv preprint arXiv …, 2020

Page 1. DETECTING HALLUCINATED CONTENT IN CONDITIONAL NEURAL SEQUENCE GENERATION Chunting Zhou1∗, Jiatao Gu2, Mona Diab2, Paco Guzman2, Luke Zettlemoyer2, Marjan Ghazvininejad2 Language …


Detecting Incivility and Impoliteness in Online Discussions.

AK Stoll, M Ziegele, O Quiring - Computational Communication Research, 2020

Page 1. VOL. 2, NO. 1, 2020 109 Detecting Impoliteness and Incivility in Online Discussions Classification Approaches for German User Comments Anke Stoll, Marc Ziegele, Oliver Quiring CCR 2 (1): 109–134 DOI: 10.5117/CCR2020.1.005.KATH …


Detecting misogyny in Spanish Tweets. An approach based on linguistics features and word embeddings

JA García-Díaz, M Cánovas-García… - Future Generation …, 2020

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DETECTING OUT-OF-DISTRIBUTION TRANSLATIONS WITH VARIATIONAL TRANSFORMERS

… The following datasets were used in our experiments: (1) WMT EN ↔ DE: The training set for translation tasks between English (EN) and German (DE) composed of news-commentary-v13 with 284k sentences pairs …


D Emelin, I Titov, R Sennrich - arXiv preprint arXiv:2011.01846, 2020

Page 1. Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks Denis Emelin1, Ivan Titov1, 2, and Rico Sennrich3, 1 1University of Edinburgh, Scotland 2University of Amsterdam …


Detecting, Classifying, and Mapping Retail Storefronts Using Street-level Imagery

S Sharifi Noorian, S Qiu, A Psyllidis, A Bozzon… - Proceedings of the 2020 …, 2020

SS Noorian, S Qiu, A Psyllidis, A Bozzon, GJ Houben

… a bag of character n-grams. As our evaluation will focus on the Manhattan Borough of New York City, the pre-trained (on Common Crawl and Wikipedia 3) word vectors for English are used. According to the desired language …


Detection of Emerging Words in Portuguese Tweets

A Pinto, H Moniz, F Batista - 9th Symposium on Languages, Applications and …, 2020

… We have used two pre-trained word vectors for Portuguese4, the first one trained on the Common Crawl5, and the second trained on the … www.nilc.icmc. usp.br/nilc/projects/unitex-pb/web/dicionarios.html 4 https://fasttext.cc/docs …


Detection of Harassment on Twitter with Deep Learning Techniques

I Espinoza, F Weiss - Machine Learning and Knowledge Discovery in …, 2020

… trained embedding model. We use the implementation off Spacy library for Python4 with the pre-trained model called 'en vectors web lg', which has 300 dimensions and it's trained over common crawl texts. With Spacy we have …


Determining Event Outcomes: The Case of# fail

S Murugan, D Chinnappa, E Blanco - Proceedings of the 2020 Conference on …, 2020

… has variable length). Additionally, the word embeddings (GloVe embeddings pre-trained with CommonCrawl) allow us to leverage a distributional representation of tags, including those not seen during training. The second …


Developing a Twitter bot that can join a discussion using state-of-the-art architectures

YM Çetinkaya, İH Toroslu, H Davulcu - Social Network Analysis and Mining, 2020

… requests. Radford et al. (2019) construct an auto-regressive feed-forward model instead of seq2seq-RNN as a language model using Common Crawl as a dataset and generate sentences with predicting next word. Generative …


Developing an online hate classifier for multiple social media platforms

J Salminen, M Hopf, SA Chowdhury, S Jung… - Human-centric Computing …, 2020

The proliferation of social media enables people to express their opinions widely online. However, at the same time, this has resulted in the emergence of conflict and hate, making online...


Development and evaluation of a Polish Automatic Speech Recognition system using the TLK toolkit

… 12 2.8 Trellis representing the decoding step . . . . . 15 3.1 Examples of sentences extracted from the Common Crawl corpus . . . . . 21 4.1 Actions performed to create the final acoustic models . . . . . 24 4.2 Phonetic transcription of a Polish word …


Development of a Search Engine to Answer Comparative Queries

… extraction and tuning the retrieval model. Page 6. References 1. Bevendorff, J., Stein, B., Hagen, M., Potthast, M.: Elastic chatnoir: Search engine for the clueweb and the common crawl. In: ECIR (2018) 2. Bondarenko, A., Fröbe …


Development of Word Embeddings for Uzbek Language

B Mansurov, A Mansurov - arXiv preprint arXiv:2009.14384, 2020

… variant of Uzbek. As far as we're aware, only fastText [5] word embeddings exist for the Latin variant. However, fastText was trained on the relatively low quality Uzbek Wikipedia and noisy Common Crawl corpus. In this paper …


Page 1. Dialog Response Generation Using Adversarially Learned Latent Bag-of-Words by Kashif Khan A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Master of Mathematics in Computer Science …


Dictionary for Computer-Assisted Text Analysis of Cyber Security (TACS)

… algorithm [372] implemented via the Python package textacy [7]. We then grouped the terms by semantic similarity, with the help of a vector space model available via the Python library spacy ('en_core_web_lg'), with 685k …


Dictionary-based Data Augmentation for Cross-Domain Neural Machine Translation

W Peng, C Huang, T Li, Y Chen, Q Liu - arXiv preprint arXiv:2004.02577, 2020

… The OOD data used for pre-training for the baseline model are extracted from WMT 144 including Eu- roparl V7, New-commentary V9 and Common Crawl corpora … Train Dataset (OOD) Europarl,News-commentary, Common …


A van Loon, A Goldberg, S Srivastava - SocArXiv. July, 2020

Page 1. Differences Beyond Identity: Perceived Construal Distance and Interparty Animosity in the United States ∗ Austin van Loon Stanford University Amir Goldberg Stanford University Sameer B. Srivastava …


Dilated Convolution Networks for Classification of ICD-9 based Clinical Summaries

M Morisio, N Kanwal, I Tutor, DG Rizzo - 2020

… This architecture uses multiple dilation layers with a label-specific dot-based attention mechanism. We have extracted the embeddings from Common Crawl Glove (Global Vector). The architecture of the model is designed to calculate attention to words and their context …


J Launay, I Poli, F Boniface, F Krzakala - arXiv preprint arXiv:2006.12878, 2020

Page 1. Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures Julien Launay1,2 Iacopo Poli1 François Boniface1 Florent Krzakala1,2 1LightOn 2École Normale Supérieure {julien, iacopo, francois, florent}@lighton.ai Abstract …


Discovering key topics from short, real-world medical inquiries via natural language processing and unsupervised learning

A Ziletti, C Berns, O Treichel, T Weber, J Liang… - arXiv preprint arXiv …, 2020

… Table IIA1 presents a qualitative comparison of a standard embedding (en core web lg, trained on the Common Crawl) and a specialized biomedical embedding (scispaCy en core sci lg, trained also on PubMed). Specifically …


Discovering Relational Intelligence in Online Social Networks

L Tan, T Pham, HK Ho, TS Kok - International Conference on Database and Expert …, 2020

… 100 mil tweets. 283. \(^\text {a}\)https://archive.ics.uci.edu/ml/datasets /bag+of+words. \(^\text {b}\)http://commoncrawl.org/2014/07/april-2014-crawldata-available/. \(^text {c}\)https://developer.twitter.com/en/docs.html. \(^\text …


Discovering web services in social web service repositories using deep variational autoencoders

I Lizarralde, C Mateos, A Zunino, TA Majchrzak… - Information Processing & …, 2020

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Disentangling semantic composition and semantic association in the left temporal lobe Abbreviated title: Semantic composition versus association

… derived using the well-known GloVe word embeddings model (Pennington et al., 2014; freely available at https://nlp.stanford.edu/projects/glove/) trained on Common Crawl (https://commoncrawl.org/), which contains petabytes …