“I think it might help if we multiply, and not add”: Detecting Indirectness in Conversation

P Goel, Y Matsuyama, M Madaio, J Cassell

… We tried various available pre-trained models like Twitter word2vec [14] trained on 400 million Twitter tweets, GloVe representations [34] trained on Wikipedia articles (called GloVe wiki) and web content crawled via …


Page 1. University of Denver Digital Commons @ DU Electronic Theses and Dissertations Graduate Studies 8-1-2018 A Bi-Encoder LSTM Model for Learning Unstructured Dialogs Diwanshu Shekhar University of Denver Follow …


A Bidirectional LSTM-CRF Network with Subword Representations, Character Convolutions and Morphosyntactic Features for Named Entity Recognition in Polish

M Piotrowski, W Janowski, P Pezik - Proceedings ofthePolEval2018Workshop

… Chiu and Nichols 2015, Ma and Hovy 2016). Secondly, the input token sequences are matched against 300-dimensional FastText subword embeddings vectors derived from a Common Crawl dump of Polish texts (Grave et al …


A Case Study of Closed-Domain Response Suggestion with Limited Training Data

… and return the known answer to this context. To compute the word vector centroids, we use German fastText [16] word vectors trained on CommonCrawl and Wikipedia6 [17]. For the experiment on English utterances, we use …


A Cognitive Assistant for Risk Identification and Modeling

D Subramanian, D Bhattachrajya, RR Torrado…

… politics/government or alternative ways to describe the same risk. Specifically, we use Glove [17] trained on a Common Crawl corpus that has 1.9 million vocabulary words embedded in a 300-dimensional vector space. Table II …


A Comparative Analysis of Content-based Geolocation in Blogs and Tweets

K Pappas, M Azab, R Mihalcea - arXiv preprint arXiv:1811.07497, 2018

… Page 13. We also experiment with a word embedding representation, where we use word vectors obtained with GloVe [47] trained on a Common Crawl and a Twitter dataset respectively, which are added up and averaged to create a word …


A Comparative Study of Embedding Models in Predicting the Compositionality of Multiword Expressions

N Nandakumar, B Salehi, T Baldwin - Australasian Language Technology Association …

… The two character-level embedding models we experiment with are fastText (Bojanowski et al., 2017) and ELMo (Peters et al., 2018), as detailed below. fastText We used the 300-dimensional model pre-trained on Common Crawl and Wikipedia us- ing CBOW …


A Comparison of Machine Translation Paradigms for Use in Black-Box Fuzzy-Match Repair

… We report scores with a beam size of 12. 4.3 Phrase-Based SMT (Moses) We use Moses (Koehn et al., 2007) to train our phrasebased statistical MT (SMT) system using the same parallel text as the NMT model, with the addition of Common Crawl,10 for phrase extraction …


A Comprehensive Course on Big Data for Undergraduate Students

JA Shamsi, SZ ul Hassan, N Bawany, N Shoaib

… ' TABLE V PROJECTS S. No Project Title Big Data Analytics 1 Performance Analysis on EspnCricinfo data 2 YouTube Data Analysis Using Hadoop 3 Analysis of emails in Amazon Common Crawl Dataset using MapReduce …


A course on big data analytics

J Eckroth - Journal of Parallel and Distributed Computing, 2018

This report details a course on big data analytics designed for undergraduate junior and senior computer science students. The course is heavily focused on proj.


A database of German definitory contexts from selected web sources.

A Barbaresi, L Lemnitzer, A Geyken - LREC, 2018

… specific datasets, eg instructional texts compiled for teaching purposes (Borg, 2009); large encyclopedic re- sources such as Wikipedia (Kovár et al., 2016); or generalpurpose web corpora (Navigli et al., 2010); unfiltered …


A Dataset and Reranking Method for Multimodal MT of User-Generated Image Captions

… pairs: French-English We trained our French-English translation system on data made available for the WMT 2015 translation shared task.7 We used the Europarl, News Commentary and Common Crawl data for training. Russian …


A Dataset for Web-scale Knowledge Base Population

… Obtain the list of WARC paths from the Common Crawl website – Download the WARC files hosted … 8 http://commoncrawl.org 9 https://github.com/optimaize/language-detector Page 10. 4.2 DBpedia DBpedia [1] is a mature knowledge base built from the infoboxes of Wikipedia …


A Deep Learning Approach for Extracting Attributes of ABAC Policies

M Alohaly, H Takabi, E Blanco - Proceedings of the 23nd ACM on Symposium on …, 2018

… IBM 2004. Course Registration Requirements. (2004). 3. 2017. Common Crawl. (September 2017). http://commoncrawl.org/. 4. Ryma Abassi, Michael Rusinowitch, Florent Jacquemard, and Sihem Guemara El Fatmi. 2010. XML …


A Deep Learning Architecture for De-identification of Patient Notes: Implementation and Evaluation

K Khin, P Burckhardt, R Padman - arXiv preprint arXiv:1810.01570, 2018

… embeddings. The traditional word embeddings use the latest GloVe 3 [13] pre-trained word vectors that were trained on the Common Crawl with about 840 billion tokens. For every token input, ti,j,k, the GloVe 6 Page 7. system …


S Ruder, R Cotterell, Y Kementchedjhieva, A Søgaard - arXiv preprint arXiv …, 2018

Page 1. A Discriminative Latent-Variable Model for Bilingual Lexicon Induction Sebastian Ruder @,H∗ Ryan Cotterell S,P∗ Yova Kementchedjhieva Z Anders Søgaard Z @ Insight Research Centre, National University of Ireland …


A Document Descriptor using Covariance of Word Vectors

… We tested the available different dimensionality 100, 200 and 300. We also used the 300 dimensions GloVe model that used commoncrawl with 42 Billion tokens We call the last one Lrg. This model provides word vectors of 300 dimensions for each word …


A Domain is only as Good as its Buddies: Detecting Stealthy Malicious Domains via Graph Inference

IM Khalil, B Guan, M Nabeel, T Yu - 2018

… Thales scans DNS using a set of seed domain list compiled from multiple sources, including public blacklists (eg [19, 40, 47]), the Alexa list [13], the Common Crawl dataset [3], the domain feed from an undisclosed security


A flexible space-time tradeoff on hybrid index with bicriteria optimization

X Song, Y Yang, Y Jiang - Tsinghua Science and Technology, 2019

… sorting. Page 3. 108 Tsinghua Science and Technology, February 2019, 24(1): 106–122 (4) We execute a detailed experimental evaluation on two realistic datasets, GOV2 and Common Crawl, with AOL query log. Analysis shows …


A Framework to Discover Significant Product Aspects from E-commerce Product Reviews

… ranking. 3.4.3 Synonym-based Clustering. We use 300-dimensional word embeddings for one million vocabulary entries trained on the Common Crawl corpus using the GloVe algorithm [18] to compute word similarities. Synonym …


A Genetic Algorithm for Combining Visual and Textual Embeddings Evaluated on Attribute Recognition

… Following Collell and Moens (2016), we employ 300dimensional GloVe vectors (Pennington et al., 2014) trained on the largest available corpus (840B tokens and a 2.2M words vocabulary from Common Crawl corpus) …


A Geo-Tagging Framework for Address Extraction from Web Pages

J Efremova, I Endres, I Vidas, O Melnik - Industrial Conference on Data Mining, 2018

… Common Crawl is a public corpus, mostly stored on Amazon Web Services 3 . A subset of the CommonCrawl dataset has schema information in the microdata format with manual annotation of the main content such as …


A Graph-to-Sequence Model for AMR-to-Text Generation

L Song, Y Zhang, Z Wang, D Gildea - arXiv preprint arXiv:1805.02473, 2018

… 5.2 Settings We extract a vocabulary from the training set, which is shared by both the encoder and the de- coder. The word embeddings are initialized from Glove pretrained word embeddings (Pennington et al., 2014) on …


A Helping Hand: Transfer Learning for Deep Sentiment Analysis

X Dong, G Melo - Proceedings of the 56th Annual Meeting of the …, 2018

… en es de ru α 0.0004 0.0008 0.003 0.003 cs it ja α 0.003 0.003 0.003 Common Crawl data1, while for other languages, we rely on the Facebook fastText Wikipedia em- beddings (Bojanowski et al., 2016) as input representations. All of these are 300-dimensional …


A Hybrid Deep Learning System for Machine Comprehension

… and therefore can suffer from the out-of-vocabulary words. Thus, I used glove840B300d Common Crawl as the word embedding database during my training. e Apply dropout and regularization: dropout and regularization help …


A Machine Learning Approach to Correlate Emotional Intelligence and Happiness Based on Twitter Data

… The corpus in this case was the common crawl dataset [Mikolov, 2017] and the word vectors were obtained using fast text algorithm of Facebook AI research (FAIR) which gave us around 2 million word vectors. The data set …


A Multi-layer LSTM-based Approach for Robot Command Interaction Modeling

M Mensio, E Bastianelli, I Tiddi, G Rizzo - arXiv preprint arXiv:1811.05242, 2018

… for word representation,” in Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 2014, pp. 1532–1543. [23] (2012) Common crawl. [Online]. Available: http://commoncrawl.org/


A Multi-task Ensemble Framework for Emotion, Sentiment and Intensity Prediction

MS Akhtar, D Ghosal, A Ekbal, P Bhattacharyya - arXiv preprint arXiv:1808.01216, 2018

… Figure 1: Proposed Multi-task framework. 3.1 Deep Learning Models We employ the architecture of Figure 1a to train and tune all the deep learning models using pretrained GloVe (common crawl 840 billion) word embeddings (Pennington et al., 2014) …


A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction

S Chollampatt, HT Ng - arXiv preprint arXiv:1801.08831, 2018

… Grundkiewicz 2016) employs a word-level SMT approach with task-specific features and a web-scale LM trained on the Common Crawl corpus … all subsidiary models that make use of additional English corpora such as the word-class LM and the web-scale Common Crawl LM …


A Multimodal Assessment Framework for Integrating Student Writing and Drawing in Elementary Science Learning

PAM Smith, S Leeman-Munk, A Shelton, BW Mott… - IEEE Transactions on …, 2018

Page 1. 1939-1382 (c) 2018 IEEE. Personal use is permitted, but republication/ redistribution requires IEEE permission. See http://www.ieee.org/ publications_standards/publications/rights/index.html for more information. This …


A novel approach for phishing emails real time classifica-tion using k-means algorithm

V Mhaske-Dhamdhere, S Vanjale - International Journal of Engineering & …, 2017

… to get to the two techniques utilizes RF (Random Forest) and LSTM (a long/here and now memory mastermind on datasets phish tank and Common Crawl, which gives result as precision rate of 93.5% and 98.7% .RF and LSTM utilizes 14 highlights of lexical and quantifiable …


A novel approach for phishing emails real time classification using k-means algorithm

V Mhaske-Dhamdhere, S Vanjale - International Journal of Engineering & …, 2018

… to get to the two techniques utilizes RF (Random Forest) and LSTM (a long/here and now memory mastermind on datasets phish tank and Common Crawl, which gives result as precision rate of 93.5% and 98.7% .RF and …


M Gritta, MT Pilehvar, N Collier - arXiv preprint arXiv:1810.12368, 2018

… It 11 https://cloud.google.com/natural-language/ 12 https://spacy.io/usage/linguistic-features 13 https://github.com/jiesutd/NCRFpp 14 Common Crawl 42B - https://nlp.stanford.edu/ projects/glove/ Page 17. A Pragmatic Guide to Geoparsing Evaluation 17 …


A QUANTITATIVE ANALYSIS OF THE USE OF MICRODATA FOR SEMANTIC ANNOTATIONS ON EDUCATIONAL RESOURCES

R NAVARRETE, S LUJÁN-MORA - Journal of Web Engineering, 2018

… This quantitative analysis was conducted on datasets extracted from the Common Crawl Corpus [17], as it is the largest corpus of web crawl … The CCF periodically releases the web corpus results from each crawling process for public use. This is the Common Crawl Corpus …


A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings

M Artetxe, G Labaka, E Agirre - arXiv preprint arXiv:1805.06297, 2018

… EnglishSpanish. More concretely, the dataset consists of 300-dimensional CBOW embeddings trained on WacKy crawling corpora (English, Italian, German), Common Crawl (Finnish) and WMT News Crawl (Spanish). The …


A Simple Machine Learning Method for Commonsense Reasoning? A Short Commentary on Trinh & Le (2018)

WS Saba - arXiv preprint arXiv:1810.00521, 2018

… the suitcase”. T&L then compute, against the backdrop of training on a large corpus (trained on the language model LM-1-Billion, CommonCrawl, and SQuAD), the probabilities of s1 and s2 appearing in a large corpus. The …


A Simple Method for Commonsense Reasoning

TH Trinh, QV Le - arXiv preprint arXiv:1806.02847, 2018

… We train an array of large RNN language models that operate at word or character level on LM-1-Billion, CommonCrawl, SQuAD, Gutenberg Books, and a customized corpus for this task and show that diversity of training …


A Social Robot System for Modeling Children's Word Pronunciation: Socially Interactive Agents Track

S Spaulding, H Chen, S Ali, M Kulinski, C Breazeal - Proceedings of the 17th …, 2018

… 3) The semantic distance between two words is the cosine distance between their word-embedding vector representation. We used word-embedding vectors from the pre-trained Common Crawl GloVe vectors [27], included with the SpaCy Python package …


AK Mishra, BK Umrao, DK Yadav - The Journal of Supercomputing, 2018

Page 1. The Journal of Supercomputing https://doi.org/10.1007/s11227-018-2509-0 A survey on optimal utilization of preemptible VM instances in cloud computing Ashish Kumar Mishra1 · Brajesh Kumar Umrao1 · Dharmendra K. Yadav1 …


A Transfer Learning Based Hierarchical Attention Neural Network for Sentiment Classification

Z Qu, Y Wang, X Wang, S Zheng - International Conference on Data Mining and Big …, 2018

… The large one consists of 209772 sentences pairs. While training the LSTM encoder, we obtain the fixed English word vectors using the CommonCrawl-840B GloVe model. The size of hidden units is 300 in the LSTM. We use SGD for training …


A Vector Worth a Thousand Counts-A Temporal Semantic Similarity Approach to Patent Impact Prediction

DS Hain, R Jurowetzki, T Buchmann, P Wolf - 2018

… task convolutional neural network model trained on OntoNotes, with GloVe vectors (685k unique vectors with 300 dimensions) trained on Common Crawl. Given a patent abstract, spaCy predicts the meaning of each term in the document …


Absolute Orientation for Word Embedding Alignment

S Dev, S Hassan, JM Phillips - arXiv preprint arXiv:1806.01330, 2018

… For instance, some groups have built word vector embeddings for enormous datasets (eg, GloVe embedding using 840 billion tokens from Common Crawl, or the word2vec embedding using 100 billion tokens of Google News), which …


Abstractive Summarization of Reddit Posts with Multi-level Memory Networks

B Kim, H Kim, G Kim - arXiv preprint arXiv:1811.00783, 2018

… 4.1 Text Embedding Online posts include lots of morphologically similar words, which should be closely embedded. Thus, we use the fastText (Bojanowski et al., 2016) trained on the Common Crawl corpus, to initialize the word embedding matrix Wemb …


Abstractive Summarization Using Attentive Neural Techniques

J Krantz, J Kalita - arXiv preprint arXiv:1810.08838, 2018

… Page 5. similarity sub-score uses GloVe embeddings2 pretrained on Common Crawl while the dissimilarity sub-score uses Word2Vec3 trained on the Google News dataset. Using different word embeddings provides …


Accurate semantic textual similarity for cleaning noisy parallel corpora using semantic machine translation evaluation metric: The NRC supervised submissions to the …

C Lo, M Simard, D Stewart, S Larkin, C Goutte, P Littell - Proceedings of the Third …, 2018

… 2011, 2012; Bojar et al., 2013) as the basis of our development set, as we believe that all the test sets in the previous years are clean and highly parallel, as opposed to the “clean” training data where glitches may occur (especially …


H Hassan, A Aue, C Chen, V Chowdhary, J Clark… - arXiv preprint arXiv …, 2018

Page 1. Achieving Human Parity on Automatic Chinese to English News Translation Hany Hassan∗, Anthony Aue, Chang Chen, Vishal Chowdhary, Jonathan Clark, Christian Federmann, Xuedong Huang, Marcin Junczys-Dowmunt …


Adapted TextRank for Term Extraction: A Generic Method of Improving Automatic Term Extraction Algorithms

… cosine similarity function between two vectors. For the word embeddings, we use the GloVe embeddings pre-trained on the general-purpose Common Crawl data2, but others could be investigated. For multi-word expressions, we …


Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification

R He, WS Lee, HT Ng, D Dahlmeier - arXiv preprint arXiv:1809.00530, 2018

… 4.4 Training Details and Hyper-parameters We initialize word embeddings using the 300dimension GloVe vectors supplied by Pennington et al., (2014), which were trained on 840 billion tokens from the Common Crawl. For …


Addressing Age-Related Bias in Sentiment Analysis

M Díaz, I Johnson, A Lazar, AM Piper, D Gergle - 2018

… 400K words, uncased WG-6B-100D WG-6B-200D WG-6B-300D CC-42B-300D Common Crawl of the Internet 1.9M works, uncased CC-840B-300D 2.2M words, cased TW-27B-25D 2 billion Twitter tweets 1.2M words, uncased TW-27B-50D TW-27B-100D TW-27B-200D …


Advances in Pre-Training Distributed Word Representations

T Mikolov, E Grave, P Bojanowski, C Puhrsch, A Joulin - arXiv preprint arXiv …, 2017

… problems. Training such models on massive data sources, like Common Crawl, can be cumbersome and many NLP practitioners prefer to use publicly available pre-trained word vectors over training the models by themselves …


Adversarial Example Generation with Syntactically Controlled Paraphrase Networks

M Iyyer, J Wieting, K Gimpel, L Zettlemoyer - arXiv preprint arXiv:1804.06059, 2018

… The pretrained Czech-English model used for translation came from the Nematus NMT system (Sennrich et al., 2017). The training data of this system includes four sources: Common Crawl, CzEng 1.6, Europarl, and News Commentary …


Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization

EM Ponti, I Vulić, G Glavaš, N Mrkšić, A Korhonen - arXiv preprint arXiv:1809.04163, 2018

… Polyglot Wikipedia (Al-Rfou et al., 2013) using Skip-Gram with Negative Sampling (SGNS) (Mikolov et al., 2013) by Levy and Goldberg (2014) with bag-of- words contexts (window size is 2). 2) GLOVE-CC are GloVe vectors …


Aggression Identification Using Deep Learning and Data Augmentation

… More specifically, we use the common crawl embeddings5 for the English tasks and the Hindi Wikipedia embeddings6 for the Hindi tasks (Grave et al., 2018). In comparison to other embedding methods, fastText embeddings …


Aggressive Language Identification Using Word Embeddings and Sentiment Features

C Orasan - Proceedings of the First Workshop on Trolling …, 2018

… representation of words. For the experiments presented in this paper, we used the pretrained word vectors obtained from the Common Crawl corpus containing 840 billion tokens and 2.2 million vocabulary entries. Each word …


Ain't Nobody Got Time For Coding: Structure-Aware Program Synthesis From Natural Language

J Bednarek, K Piaskowski, K Krawiec - arXiv preprint arXiv:1810.09717, 2018

… Given that, we rely on a pretrained GloVe embedding, more specifically the Common Crawl, which has been trained on generic NL on 42B tokens by Pennington et al. (2014), has vocabulary size of 1.9M tokens, and embeds the words in a 300dimensional space …


Alibaba's Neural Machine Translation Systems for WMT18

Y Deng, S Cheng, J Lu, K Song, J Wang, S Wu, L Yao… - Proceedings of the Third …, 2018

… For English ↔ Russian, we use the following resources from the WMT parallel data: News Commentary v13, CommonCrawl, ParaCrawl corpus … For EN → TR, about 6 million sentences are selected from the newscrawl2016, 2017 …


ALOD2Vec Matcher⋆

… like DBpedia [3] – but in- stead on the whole Web: The data set consists of hypernymy relations extracted from the Common Crawl1, a … 1 see http://commoncrawl.org/ 2 see http://webisa.webdatacommons.org/concept …


S Chatzis, A Charalampous, K Tolias, SA Vassou - arXiv preprint arXiv:1805.09039, 2018

Page 1. Amortized Context Vector Inference for Sequence-to-Sequence Networks Sotirios Chatzis Cyprus University of Technology sotirios. chatzis@cut.ac.cy Aristotelis Charalampous Cyprus University of …


AmritaNLP at SemEval-2018 Task 10: Capturing discriminative attributes using convolution neural network over global vector representation.

V Vinayan, KP Soman - Proceedings of The 12th International Workshop on …, 2018

… ding (Pennington et al., 2014) of various dimensions are considered, which is learned over public data, available under the PDDL.2 (100, 300 di- mension word representation, embedded over 6B, 840B sizes common crawl corpus are considered) …


An Adaption of BIOASQ Question Answering dataset for Machine Reading systems by Manual Annotations of Answer Spans.

S Kamath, B Grau, Y Ma - Proceedings of the 6th BioASQ Workshop A challenge …, 2018

… Several embedding spaces were tested as input vectors (Kamath et al., 2017) and the best performing ones which were the Glove embeddings trained on common crawl data with 840B tokens, were chosen as input to the system …


An Analysis of Hierarchical Text Classification Using Word Embeddings

RA Stein, PA Jaques, JF Valiati - Information Sciences, 2018


An Encoder-decoder Approach to Predicting Causal Relations in Stories

… First, we encoded the words in each in- put segment as the sum of their GloVe embeddings3 (Pennington et al., 2014), which represent words according to a global log-bilinear regression model trained on word co-occurrence counts in the Common Crawl corpus …


An Experimental Analysis of Multi-Perspective Convolutional Neural Networks

… nity to use. Popular pre-trained word vectors include word2vec trained on Google News, GloVe [31] trained on Common Crawl/Wikipedia/Twitter, and fastText [19] trained on Common Crawl/Wikipedia. Whereas word2vec captures …


Page 1. An Unsupervised Approach to Relatedness Analysis of Legal Language by Ying Wang A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Master of Applied …


Analysis of DNS in cybersecurity

Page 1. Masarykova univerzita Fakulta informatiky Analysis of DNS in cybersecurity Master's thesis Patrik Hudák Brno, 2017 Page 2. Page 3. Declaration Hereby I declare, that this paper is my original authorial work, which I have worked out by my own …


Analysis of parallel iterative graph applications on shared memory systems

Page 1. ANALYSIS OF PARALLEL ITERATIVE GRAPH APPLICATIONS ON SHARED MEMORY SYSTEMS a thesis submitted to the graduate school of engineering and science of bilkent university in partial fulfillment of the requirements for the degree of master of science in …


Analysis of Short Text Classification strategies using Out of-domain Vocabularies

Page 1. IN DEGREE PROJECT INFORMATION AND COMMUNICATION TECHNOLOGY, SECOND CYCLE, 30 CREDITS , STOCKHOLM SWEDEN 2018 Analysis of Short Text Classification strategies using Out- of-domain Vocabularies DIEGO ROA …


Analysis of the Web Graph Aggregated by Host and Pay-Level Domain

A Funel - arXiv preprint arXiv:1802.05435, 2018

… The web graph datasets, publicly available, have been released by the Common Crawl Foundation 1 and are based on a web crawl performed during the period May-June-July 2017 … 1http://commoncrawl.org … [7] from a crawl, provided by the Common Crawl Foundation, gath …


Analyzing conversations to automatically identify action items

R Raanani, R Levy, MY Breakstone, D Facher - US Patent App. 15/854,642, 2018

… At the same time, natural language processing (NLP) approaches to both topic modeling and world-knowledge modeling, have become much more efficient due to the availability of large, freely accessible natural language corpora (eg, CommonCrawl), as well as freely …


ANALYZING CONVERSATIONS TO AUTOMATICALLY IDENTIFY CUSTOMER PAIN POINTS

R Raanani, R Levy, MY Breakstone, D Facher - US Patent App. 15/902,808, 2018


Analyzing conversations to automatically identify deals at risk

R Raanani, R Levy, D Facher, MY Breakstone - US Patent App. 15/835,807, 2018

… language processing (NLP) approaches to both topic modeling and i Insidesales.com “Market size 2013” study world-knowledge modeling, have become much more efficient due to the availability of large, freely accessible …


ANALYZING CONVERSATIONS TO AUTOMATICALLY IDENTIFY PRODUCT FEATURE REQUESTS

R Raanani, R Levy, MY Beakstone, D Facher - US Patent App. 15/902,751, 2018


Analyzing conversations to automatically identify product features that resonate with customers

R Raanani, R Levy, MY Breakstone, D Facher - US Patent App. 15/937,494, 2018


M Ott, M Auli, D Granger, MA Ranzato - arXiv preprint arXiv:1803.00047, 2018

Page 1. Analyzing Uncertainty in Neural Machine Translation Myle Ott Michael Auli David Granger Marc'Aurelio Ranzato Facebook AI Research Abstract Machine translation is a popular test bed for research in neural sequence …


Answering Factoid Questions with Recurrent Neural Networks

… one. The embedding matrix is initialized with 300-dimensional GloVe embeddings pre-trained on the 840B Common Crawl corpus [9]. We use case-sensitive embeddings, which results in ~140k usable GloVe embeddings …


Approaching Nested Named Entity Recognition with Parallel LSTM-CRFs

Ł Borchmann, A Gretkowski, F Gralinski - Proceedings ofthePolEval2018Workshop, 2018

… 2014) were trained on a very large, freely available7 Common Crawl-based Web corpus of Polish (Buck et al. 2014) … N-gram Counts and Language Models from the Common Crawl.[in:] Proceedings of the Language Resources …


Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task

M Junczys-Dowmunt, R Grundkiewicz, S Guha… - arXiv preprint arXiv …, 2018

… Current state-of-the-art GEC systems based on SMT, however, all include large-scale in- domain language models either following the steps outlined in Junczys-Dowmunt and Grundkiewicz (2016) or directly re-using their …


Approaching the largest 'API': extracting information from the Internet with Python

… For every patron their dataset. For every dataset its patron. Bibliography. Common Crawl: Get Started[Internet]. [accessed 2017 Dec. 1] Available from http://commoncrawl.org/the-data/getstarted/. Imperva Incapsula's Bot Traffic Report [Internet]. [accessed 2017 Dec …


Arabic Sentences Classification via Deep Learning

… The research [21] presents AraVec Arabic word2vec models for the Arabic language using three different dataset resources: Wikipedia, Twitter and Common Crawl webpages crawl data, the models are built in the same strategies in Mikilov word2vec, skip-gram and CBOW …


Are Automatic Metrics Robust and Reliable in Specific Machine Translation Tasks?

… 5 Experimental setup Our experimental framework related a domain adaptation task, in the English to Spanish language direction. In our setup, we trained a PB-SMT and a NMT system on the same data, from a general corpus extracted from websites (Common Crawl) …


Are we experiencing the Golden Age of Automatic Post-Editing?

M Junczys-Dowmunt - Proceedings of the AMTA 2018 Workshop on …, 2018

… EN-DE bilingual data from the WMT-16 shared tasks on IT and news translation. ▶ German monolingual Common Crawl (CC) corpus. Proceedings for AMTA 2018 Workshop: Translation Quality Estimation …


ArgumenText: Searching for Arguments in Heterogeneous Sources

C Stab, J Daxenberger, C Stahlhut, T Miller, B Schiller… - Proceedings of the 2018 …, 2018

… 3.1 Data As our objective is to search for arguments in any text domain, we build upon the English part of CommonCrawl,1 the largest Web corpus available to date. Before further processing, we followed 1http://commoncrawl.org/ Habernal et al …


Arretium or Arezzo? A Neural Approach to the Identification of Place Names in Historical Texts

… summarised below: • dropout: 0.25, 0.25 • classifier: CRF • LSTM-Size: 100 • optimizer: NADAM • word embeddings: GloVe Common Crawl 840B • character … namely: (i) GloVe embeddings, trained on a corpus of 840 billion …


Associative Multichannel Autoencoder for Multimodal Word Representation

S Wang, J Zhang, C Zong - Proceedings of the 2018 Conference on Empirical …, 2018

… representations. 4 Experimental Setup 4.1 Datasets Textual vectors. We use 300-dimensional GloVe vectors1 which are trained on the Common Crawl corpus consisting of 840B tokens and a vocabulary of 2.2M words2. Visual vectors. Our …


AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification

Y Kim, H Lee, K Jung - arXiv preprint arXiv:1804.00831, 2018

… dataset. Among those well-known word embeddings such as Word2Vec( Mikolov et al., 2013), GloVe(Pennington et al., 2014), fastText(Piotr et al., 2016), we adopt 300-dimension GloVe vectors for English ,which is trained …


Automated Detection of Adverse Drug Reactions in the Biomedical Literature Using Convolutional Neural Networks and Biomedical Word Embeddings

DS Miranda - arXiv preprint arXiv:1804.09148, 2018

… 5.1 Embeddings GloVe 840B As in Huynh's work (Huynh et al., 2016), we use pre-trained word embeddings. Huynh focused mainly on the general purpose GloVe Common Crawl 840B, 300 dimensional word embeddings (Pennington et al., 2014). Pyysalo's Embeddings …


Page 1. Automated discovery of privacy violations on the web Steven Tyler Englehardt A Dissertation Presented to the Faculty of Princeton University in Candidacy for the Degree of Doctor of Philosophy Recommended for …


Automatic generation of playlists from conversations

R Raanani, R Levy, MY Breadstone - US Patent App. 15/793,691, 2018


Automatic Neural Question Generation using Community-based Question Answering Systems

Page 1. AUTOMATIC NEURAL QUESTION GENERATION USING COMMUNITYBASED QUESTION ANSWERING SYSTEMS TINA BAGHAEE Bachelor of Science, Shahid Beheshti University, 2011 A Thesis Submitted to the …


AUTOMATIC PATTERN RECOGNITION IN CONVERSATIONS

R Raanani, R Levy, D Facher, MY Breakstone - US Patent App. 15/817,490, 2018

… language processing (NLP) approaches to both topic modeling and world-knowledge modeling, have become much more efficient due to the i Insidesales.com “Market size 2013” study availability of large, freely …


Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach

TT Vu, G Haffari - Proceedings of the 2018 Conference on Empirical …, 2018

… Interestingly, training MT+AG and MT+AG+LM models on 23K data lead to better TER/BLEU than those trained on 500K+12K. This implies the importance of in-domain training data, as the synthetic corpus is created …


Automatic Question Tagging with Deep Neural Networks

B Sun, Y Zhu, Y Xiao, R Xiao, YG Wei - IEEE Transactions on Learning Technologies, 2018

… Word2vec [41] trained on a large corpus. Some pre-trained word vectors are available, such as GloVe Common Crawl vectors1 and word2vec vectors2, which is trained on Google News. The word vectors can be divided into …


Automatically Categorizing Software Technologies

M Nassif, C Treude, M Robillard - IEEE Transactions on Software Engineering, 2018

S Khan, WH Butt - 2022 2nd International Conference on Digital Futures …, 2022

… All these approaches work by mining large text corpora. Among the latest such techniques is the WebIsA Database [32] from the Web Data Commons project, which extracts hypernyms from CommonCrawl,1 a corpus of over …


Based Speech Recognition with Gated ConvNets

V Liptchinsky, G Synnaeve, R Collobert - arXiv preprint arXiv:1712.09444, 2017

… Extra Resources Panayotov et al. (2015) HMM+DNN+pNorm phone fMLLR phone lexicon Amodei et al. (2016) 2D-CNN+RNN letter none 11.9Kh train set, Common Crawl LM Peddinti et al. (2015b) HMM+CNN phone iVectors phone lexicon Povey et al …


Belittling the Source: Trustworthiness Indicators to Obfuscate Fake News on the Web

D Esteves, AJ Reddy, P Chawla, J Lehmann - arXiv preprint arXiv:1809.00494, 2018

… Social Tags: returns the frequency of social tags in wb: R ⋃ i=1 ϕ(i, wb) 11. OpenSources: returns the open-source classification (x) for a given website: x = { 1, if w ∈ O 0, if w ∈ O 12. PageRankCC: PageRank information …


Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation

X Niu, W Xu, M Carpuat - arXiv preprint arXiv:1811.01116, 2018

… data for Swahili↔English (SW↔EN), Tagalog↔English (TL↔EN) and Somali↔English (SO↔EN) contains a mixture of domains such as news and weblogs and is collected from the IARPA MATERIAL program2, the Global …


Bi-Directional Neural Machine Translation with Synthetic Parallel Data

X Niu, M Denkowski, M Carpuat - arXiv preprint arXiv:1805.11213, 2018

… Page 3. Type Dataset # Sentences High-resource: German↔English Training Common Crawl + Europarl v7 + News Comm. v12 … 2.2). We use the training data as in-domain and either Common Crawl or ICWSM as out-of-domain …


Big Data Integration for Product Specifications

L Barbosa, V Crescenzi, XL Dong, P Merialdo, F Piai… - Data Engineering, 2018

… Only 20% of our sources contained fewer pages than the same sources in Common Crawl, and a very small fraction of the pages in these sources were product pages: on a sample set of 12 websites where Common Crawl …


Big Open Research Data

… Outline ▪ Requirements of a data scientist ▪ Open access datasets ▪ DBpedia ▪ Common Crawl ▪ The Web Data Commons project ▪ Why should we share data? ▪ How should we share data? 26/10/2017 … 5 Page 6. Common Crawl …


Bigrams and BiLSTMs Two neural networks for sequential metaphor detection

Y Bizzoni, M Ghanimifard - NAACL HLT 2018, 2018

… GloVe embeddings on British National Corpus (Consortium et al., 2007) from which the VUAMC corpus was sampled, and compared it with both pre-trained Word2Vec em- beddings on Google News corpus and standard …


Bilingual Character Representation for Efficiently Addressing Out-of-Vocabulary Words in Code-Switching Named Entity Recognition

GI Winata, CS Wu, A Madotto, P Fung - arXiv preprint arXiv:1805.12061, 2018

… We use 300-dimensional English (Mikolov et al., 2018) and Spanish (Grave et al., 2018) FastText pre-trained word vectors which comprise two million words vocabulary each and they are trained using Common Crawl and Wikipedia …


Biomedical Domain-Oriented Word Embeddings via Small Background Texts for Biomedical Text Mining Tasks

L Li, J Wan, D Huang - National CCF Conference on Natural Language …, 2017

… For example, Pennington et al. used Wikipedia, Giga word 5 and Common Crawl to learn word embeddings, each of which contained billions of tokens [15]. However, there is not always a monotonic increase in performance as the amount of background texts increase …


BlogSet-BR: A Brazilian Portuguese Blog Corpus

H Santos, V Woloszyn, R Vieira - … of the Eleventh International Conference on …, 2018

… For instance, the Common Crawl project maintains an open repository of web crawl data that can be accessed and analyzed by any research group2. This corpus has been used to build language models (Roziewski …


BomJi at SemEval-2018 Task 10: Combining Vector-, Pattern-and Graph-based Information to Identify Discriminative Attributes

… 2The pre-trained vectors are available, respectively, at https://code.google.com/archive/ p/ word2vec/ (Google News, 300 dimensions) and at https://nlp.stanford.edu/projects/ glove/ (Common Crawl, 840B tokens, 300 dimensions) …


Bootstrapping Multilingual Intent Models via Machine Translation for Dialog Automation

N Ruiz, S Bangalore, J Chen - arXiv preprint arXiv:1805.04453, 2018

… The NMT models were trained with parallel English-Spanish data from Europarl v7, CommonCrawl, and WMT News Commentary v8 from the WMT 2013 evaluation campaign (Bojar et al., 2013), as well as the TED talks from IWSLT 2014 (Cettolo et al., 2014) …


Bringing Order to Neural Word Embeddings with Embeddings Augmented by Random Permutations (EARP)

T Cohen, D Widdows - Proceedings of the 22nd Conference on Computational …, 2018

Page 1. Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018), pages 465–475 Brussels, Belgium, October 31 - November 1, 2018. c 2018 Association for Computational Linguistics 465 …


Bringing replication and reproduction together with generalisability in NLP: Three reproduction studies for Target Dependent Sentiment Analysis

A Moore, P Rayson - arXiv preprint arXiv:1806.05219, 2018

… We found the best word vectors from SSWE and the common crawl 42B 300 dimension Glove vectors by five fold stratified cross validation for the NP methods and the highest accuracy on the validation set for the LSTM methods …


C) uestion Answering System with Deep Learning

… The dataset contains more than 100k question-answer pairs on more than 500 articles, and it is split into 90k/10k train/dev question-context tuples with hidden test set. 3.2 Word Embeddings We use pre-trained GLoVe embeddings from the 840B Common Crawl corpus …


CAESAR: Context Awareness Enabled Summary-Attentive Reader

LH Chen, K Tripathi - arXiv preprint arXiv:1803.01335, 2018

… Aftering experimenting with hyperparameters and various preprocessing settings, we settle on the following experiment details which gave the optimal result. We apply pretrained GloVe word embeddings trained on common


Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

T Mihaylov, P Clark, T Khot, A Sabharwal

… 11For all experiments we use d = 300 GloVe (Pennington et al., 2014) embeddings pre-trained on 840B tokens from Common Crawl (https://nlp.stanford.edu/projects/glove/). Page 7. Question Science Fact Common Knowledge (Type) Reasoning Challenge …


Can Common Crawl reliably track persistent identifier (PID) use over time?

HS Thompson, J Tong - arXiv preprint arXiv:1802.01424, 2018

Abstract: We report here on the results of two studies using two and four monthly web crawls respectively from the Common Crawl (CC) initiative between 2014 and 2017, whose initial goal was to provide empirical evidence for the changing patterns of use of so-called


Can Network Embedding of Distributional Thesaurus be Combined with Word Vectors for Better Representation?

A Jana, P Goyal - arXiv preprint arXiv:1802.06196, 2018

… representation. For that purpose, we directly use very wellknown GloVe 1.2 embeddings (Pennington et al., 2014) trained on 840 billion words of the common crawl dataset having vector dimension of 300. As an instance …


Card-660: A Reliable Evaluation Framework for Rare Word Representation Models

MT Pilehvar, D Kartsaklis, V Prokhorov, N Collier - Proceedings of the 2018 …, 2018


Card-660: Cambridge Rare Word Dataset-a Reliable Benchmark for Infrequent Word Representation Models

MT Pilehvar, D Kartsaklis, V Prokhorov, N Collier - arXiv preprint arXiv:1808.09308, 2018


Categorization of Comparative Sentences for Argument Mining

M Franzek, A Panchenko, C Biemann - arXiv preprint arXiv:1809.06152, 2018

… The re- maining objects were combined to pairs. For each object type as given by the Wikipedia List page or the seed word, all possible combinations were created. We drew sentences from the publicly available CommonCrawl3 index of Panchenko et al …


C Lockard, XL Dong, A Einolghozati, P Shiralkar - arXiv preprint arXiv:1804.04635, 2018

Page 1. CERES: Distantly Supervised Relation Extraction from the Semi-Structured Web Colin Lockard ∗ University of Washington lockardc@cs.washington.edu Xin Luna Dong Amazon lunadong@amazon.com Arash Einolghozati ∗ Facebook arashe@fb.com …


Character-based Neural Networks for Sentence Pair Modeling

W Lan, W Xu - arXiv preprint arXiv:1805.08297, 2018

… word vectors (Pennington et al., 2014), trained on 27 billion words from Twitter (vocabulary size of 1.2 milion words) for social media datasets, and 300dimensional GloVe vectors, trained on 840 billion words (vocabulary …


Character-Level Feature Extraction with Densely Connected Networks

C Lee, YB Kim, D Lee, HS Lim - arXiv preprint arXiv:1806.09089, 2018

… Our model uses the GloVe (Pennington et al., 2014) 300-dimensional vectors trained on the Common Crawl corpus with 42B tokens as word level features, as this resulted in the best performance in preliminary experiments …


Characterising dataset search—An analysis of search logs and data requests

E Kacprzak, L Koesten, LD Ibáñez, T Blount… - Journal of Web Semantics, 2018

… In 2015 the Web Data Commons project extracted 233 million data tables from the Common Crawl [3]. The ability to generate business value from data analytics offers competitive advantage in virtually every industry worldwide …


CIKM AnalytiCup 2017 Lazada Product Title Quality Challenge An Ensemble of Deep and Shallow Learning to predict the Quality of Product Titles

K Singh, V Sunder - arXiv preprint arXiv:1804.01000, 2018

… calculate semantic similarity based features. For each pair of words in a title, we calculate the cosine distance between vectors of words generated from Common crawl google glove [Pennington et al. 2014]. Further, we normalize …


ClaiRE at SemEval-2018 Task 7-Extended Version

L Hettinger, A Dallmann, A Zehe, T Niebler, A Hotho


ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings

L Hettinger, A Dallmann, A Zehe, T Niebler, A Hotho - Proceedings of The 12th …, 2018

… As a baseline, we employ a publicly available set of 300-dimensional word embeddings trained with GloVe (Pennington et al., 2014) on the Common Crawl data4 (CC) … 4http://commoncrawl.org/ 5https://arxiv.org 6https://arxiv.org/help/bulk_data …


Classifying Occupations According to Their Skill Requirements in Job Advertisements

… There are publicly available pre-trained word embeddings models. We use a GloVe model, which contains a vocabulary of 2.2 million words and was trained using word to word co-occurrences in a Common Crawl corpus (Pennington, Socher, and Manning, 2014) …


Cloud repository as a malicious service: challenge, identification and implication

X Liao, S Alrwais, K Yuan, L Xing, XF Wang, S Hao… - Cybersecurity, 2018

… Running the scanner over all the data collected by the Common Crawl (Crawl 2015), which indexed five billion web pages, for those associated with all major cloud storage providers (including Amazon S3, Cloudfront, Google …


Clustering-Oriented Representation Learning with Attractive-Repulsive Loss

K Kenyon-Dean, A Cianflone, L Page-Caccia… - arXiv preprint arXiv …, 2018

… Each sample contains the news article's title concatenated with its description. As input to our models, we use the 300-dimensional 840B Common Crawl pretrained Glove word embeddings (Pennington, Socher, and Manning 2014) provided by Stanford6 …


Code-Switched Named Entity Recognition with Embedding Attention

C Wang, K Cho, D Kiela - Proceedings of the Third Workshop on Computational …, 2018

… We use high-quality FastText embeddings trained on Common Crawl (Grave et al., 2018; Mikolov et al., 2018) and employ shortcut-stacked sentence encoders (Nie and Bansal, 2017) to obtain deep token-level representations to feed into the CRF …


Collaborative Context-Aware Visual Question Answering

Page 1. COLLABORATIVE CONTEXT-AWARE VISUAL QUESTION ANSWERING by Andeep Singh Toor A Dissertation Submitted to the Graduate Faculty of George Mason University In Partial fulfillment of The Requirements …


Combination of Statistical and Neural Machine Translation for Myanmar–English

… For English, we used the monolingual corpora provided by the WMT18 shared News Translation Task. As for Myanmar, we experimented with two monolingual corpora: Myanmar Wikipedia and Myanmar CommonCrawl … The CommonCrawl cor …


Combining Convolution and Recursive Neural Networks for Sentiment Analysis

VD Van, T Thai, MQ Nghiem - Proceedings of the Eighth International Symposium on …, 2017

… movie reviews. These words might not appear or not have good vector representations in the pre-trained Glove Common Crawl. 153 Page 4. SoICT '17, December 7–8, 2017, Nha Trang City, Viet Nam V. Van et al. Additionally …


Combining Shallow and Deep Learning for Aggressive Text Detection

V Golem, M Karan, J Šnajder - Proceedings of the First Workshop on Trolling …, 2018

… Inputs to both models were GloVe (Pennington et al., 2014) 300-dimensional word embeddings trained on 840 billion tokens from the Common Crawl or 200-dimensional word embeddings trained on 20 billion tweets.5 Since …


Compact inverted index storage using general‐purpose compression libraries

M Petri, A Moffat - Software: Practice and Experience

… We also develop (and make freely available) a new IR test collection based on the News sub-collection of the Common Crawl.6 The News sub-collection provides daily crawls of news websites in many languages. We refer to this collection as CC-NEWS-URL …


Comparative Argument Mining

… First, does a sentence compare two known objects? And secondly, if it does, is the first-mentioned object better or worse than the second-mentioned, according to the sentence context? The data set was created with data from …


Comparing Distributional and Frame Semantic Properties of Words


Comparing Open Source Search Engine Functionality, Efficiency and Effectiveness with Respect to Digital Forensic Search

J Hansen, K Porter, A Shalaginov, K Franke - NISK 2018, 2018

… Malware ISCXAB Malware DAROA98/99 Network DARPA2000 Network MAWILab Network KDD Cup99 Network UNSW-NB15 Network NSA-CDX Network ADFA Network Name Catagory Kyoto data Network crawdad Network …


M Zhan, R Levy - Proceedings of the 2018 Conference of the North …, 2018

Page 1. Proceedings of NAACL-HLT 2018, pages 1997–2005 New Orleans, Louisiana, June 1 - 6, 2018. c 2018 Association for Computational Linguistics Comparing Theories of Speaker Choice Using a Model of Classifier Production in Mandarin Chinese …


Comparison of Paragram and Glove Results for Similarity Benchmarks

PM Skłodowskiej-Curie - Multimedia and Network Information Systems, 2019

… The TOEFL test set was introduced in [13]; the ESL test set was introduced in [28]. 4.1 Experimental Setup We use the unmodified vector space model trained on 840 billion words from Common Crawl data with the GloVe algorithm introduced in [21] …


Comparison of Word Embeddings and Sentence Encodings as Generalized Representations for Crisis Tweet Classification Tasks

… the other parameters. • SIF: denotes the SIF approach, which is considered to be a baseline for sentence embeddings. The original paper used GloVe embeddings pre-trained on the Common Crawl data. However, we used …


Compositional Source Word Representations for Neural Machine Translation

D Ataman, MA Di Gangi, M Federico - 2018

… For training the Czech–English and German– English NMT models, we use the available data sets from the WMT2 (Bojar et al., 2017) shared task on machine translation of news, which consist of Europarl (Koehn, 2005), Commoncrawl …


Concatenated $ p $-mean Word Embeddings as Universal Cross-Lingual Sentence Representations

A Rücklé, S Eger, M Peyrard, I Gurevych - arXiv preprint arXiv:1803.01400, 2018

… Word embeddings We use four diverse, po- tentially complementary types of word embeddings as basis for our sentence representation techniques: GloVe embeddings (GV) (Pennington et al., 2014) trained on Common Crawl;


Content Extraction and Lexical Analysis from Customer-Agent Interactions

S Nisioi, A Bucur, LP Dinu - Proceedings of the 2018 EMNLP Workshop W-NUT …, 2018

… Somewhat surprising is the fact that a big majority of words from the Common Crawl vocabulary (approx … Furthermore, both the lexicons used in questions and answers present little overlap with Common Crawl, and in accordance …


Context and Copying in Neural Machine Translation

R Knowles, P Koehn - Proceedings of the 2018 Conference on Empirical …, 2018

… are trained using the Marian toolkit (Junczys-Dowmunt et al., 2018).2 We use the WMT parallel text3 (Europarl, News Commentary, and CommonCrawl) along with … We consider both the full training data (including backtranslations …


Context and Embeddings in Language Modelling–an Exploration

… alone. GloVe typically performs better than Word2Vec skip-gram, especially when the vocabulary is large. GloVe is also available on different corpora such as Twitter, Common Crawl or Wikipedia. 2.2 Bag of Tricks - fastText …


Context is Everything: Finding Meaning Statistically in Semantic Spaces

E Zelikman - arXiv preprint arXiv:1803.08493, 2018

… For example ”dog” and ”but” are both commonly used words, and in many spaces will have a smaller M-distance than ”Dachshund” and ”dog.” One solution to this issue is presented later in this paper 5300 dimensional word …


Contextual-CNN: A Novel Architecture Capturing Unified Meaning for Sentence Classification

… For word representation, we use the publicly available 300-dimensional GloVe trained on the Common Crawl with 42B tokens [5], hence our embedding dimension d = 300. Word embedding is normalized to unit norm and is fixed in the experiments without fine-tuning …


Convexification of Neural Graph

H Xiao - arXiv preprint arXiv:1801.02901, 2018

… Specifically, we initialize the word embedding with 300-dimensional GloVe [Pennington et al., 2014] word vectors pre-trained in the 840B Common Crawl corpus [Pennington et al., 2014] and then set the hidden dimension as 100 for each LSTM …


Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions

Z Chen, Y Cui, W Ma, S Wang, G Hu - arXiv preprint arXiv:1811.08610, 2018

… among two datasets. The word embeddings are initialized by the pre-trained GloVe word vectors (Common Crawl, 6B tokens, 100-dimension) (Pennington, Socher, and Manning 2014), and keep fixed during training. The words …


COVER: a linguistic resource combining common sense and lexicographic information

E Mensa, DP Radicioni, A Lieto - Language Resources and Evaluation

… (2014)). In count based models, model vectors are learned by applying dimensionality reduction techniques to the co-occurrence counts matrix; in particular, GloVe embeddings have been acquired through a training …


Creation and optimization of resource contents

M Tober, D Neumann - US Patent App. 15/284,739, 2018

… 310. The crawler module 310 may automatically crawl a network and acquire contents from one or more resources in the network, acquire the contents from an open repository of web crawl data such as CommonCrawl.org …


Cross-lingual Decompositional Semantic Parsing Supplemental Material

S Zhang, X Ma, R Rudinger, K Duh, B Van Durme

… Hidden states are zero initialized. All other parameters are sampled from U(−0.1,0.1). Decoder: Word embeddings are initialized by open-source GloVe vectors (Pennington et al., 2014) trained on Common Crawl 840B with 300 dimensions …


Cross-Lingual Propagation for Deep Sentiment Analysis

X Dong, G de Melo - Proceedings of the 32nd AAAI Conference on Artificial …, 2018

… each mini-batch. 3.2 Embeddings The standard pre-trained word vectors used for English are the GloVe (Pennington, Socher, and Manning 2014) ones trained on 840 billion tokens of Common Crawl data2, 1https://www.irit …


Page 1. Cross-Pair Text Representations for Answer Sentence Selection Kateryna Tymoshenko DISI, University of Trento (Adeptmind scholar) 38123 Povo (TN), Italy kateryna.tymoshenko@unitn.it Alessandro Moschitti∗ Amazon …


CrumbTrail: an Efficient Methodology to Reduce Multiple Inheritance in Knowledge Graphs

S Faralli, I Finocchi, SP Ponzetto, P Velardi - Knowledge-Based Systems, 2018

… 5]. Finally, the availability of vast amounts of knowledge encoded in heterogeneous sources, including both structured – eg, Freebase [6], DBpedia [7] – semi-structured – eg, Wikipedia – as well as unstructured resources …


CS224N Final SQuAD Improvements

… U"]. Page 5. 4 Experiments 4.1 Implementation and Metrics The model is trained and evaluated on the SQuAD dataset, and uses pretrained GloVe word vectors on the Common Crawl corpus (Pennington et al., 2014). Running …


CUNI team: CLEF eHealth Consumer Health Search Task 2018

S Saleh, P Pecina - CEUR Workshop Proceedings: Working Notes of CLEF, 2018

… Document collection in the CLEF 2018 consumer health search task is created using CommonCrawl platform 1. First, the query set (described in Section 2.2) is submitted to Microsoft Bing APIs, and a list of domains is extracted from the top retrieved results …


CUNI Transformer Neural MT System for WMT18

M Popel - Proceedings of the Third Conference on Machine …, 2018


T Kaftan, M Balazinska, A Cheung, J Gehrke - arXiv preprint arXiv:1802.09180, 2018

Page 1. Cuttlefish: A Lightweight Primitive for Adaptive Query Processing Tomer Kaftan University of Washington Magdalena Balazinska University of Washington Alvin Cheung University of Washington Johannes Gehrke Microsoft …


CytonMT: an Efficient Neural Machine Translation Open-source Toolkit Implemented in C++

X Wang, M Utiyama, E Sumita - arXiv preprint arXiv:1802.07170, 2018

… Length penalty was applied through the formula in (Wu et al., 2016). Data Set # Sent. # Words Source Target CommonCrawl 2,399,123 54,572,703 58,869,785 Europarl v7 1,959,829 51,7061,34 54,327,972 News Comment. 223,153 5,689,117 5,660,789 Dev …


Dank Learning: Generating Memes Using Deep Neural Networks

V Peirson, L Abel, EM Tolunay - arXiv preprint arXiv:1806.04510, 2018

… In this context, pretrained GloVe [3] word embeddings constitute the most suitable choice of word representation for our project, based on the fact that they have been trained on very large corpora, offering an option of words …


Page 1. IMPERIAL COLLEGE LONDON DEPARTMENT OF COMPUTING Data Augmentation and Deep Learning for Hate Speech Detection Author: Konstantin Hemker Supervisor: Dr. Björn Schuller Submitted in partial fulfillment …


Data Science with Vadalog: Bridging Machine Learning and Reasoning Data Science with Vadalog: Bridging Machine Learning and Reasoning

L Bellomarini, RR Fayzrakhmanov, G Gottlob…

… including streams of structured or unstructured data from internal systems (eg, Enterprise Resource Planning, Workflow Management, and Supply Chain Management), external streams of unstructured data (eg, news and …


Data Science with Vadalog: Bridging Machine Learning and Reasoning

L Bellomarini, RR Fayzrakhmanov, G Gottlob… - arXiv preprint arXiv …, 2018

… Workflow Management, and Supply Chain Management), external streams of unstructured data (eg, news and social media feeds, and Common Crawl1), publicly … 1 http://commoncrawl.org/ 2 http://www.cyc.com/researchcyc …


Page 1. Data-Driven Language Understanding for Spoken Dialogue Systems Nikola Mrkšic Supervisor: Professor Steve Young Department of Engineering University of Cambridge This dissertation is submitted for the degree of Doctor …


Debunking Fake News One Feature at a Time

M Tosik, A Mallia, K Gangopadhyay - arXiv preprint arXiv:1808.02831, 2018

… cosine tdidf Cosine similarity between headline/body TF-IDF vectors Real doc similarity Cosine similarity between averaged headline/body Common Crawl vectors Real doc similarity intro Cosine similarity between averaged …


Deep Extrofitting: Specialization and Generalization of Expansional Retrofitting Word Vectors using Semantic Lexicons

H Jo - arXiv preprint arXiv:1808.07337, 2018

… The algorithm learns word vectors by making the dot products of word vectors equal to the logarithm of the words' probability of co-occurrence. We use glove.42B.300d trained on Common Crawl data, which has 1,917,493 unique words as 300-dimensional vectors …


Deep neural network architecture for sentiment analysis and emotion identification of Twitter messages

D Stojanovski, G Strezoski, G Madjarov, I Dimitrovski… - Multimedia Tools and …, 2018

… SSWE 50 10M 137K Tweets word2vec 300 100B 3M GoogleNews GloVe Crawl 300 840B 2.2M Common Crawl GloVe Twitter 200 20B 1.2M Tweets Corpora size is expressed in token count with the exception of SSWE where only the number of tweets is provided …


Deep neural networks and distant supervision for geographic location mention extraction

A Magge, D Weissenbacher, A Sarker, M Scotch… - Bioinformatics, 2018

AbstractMotivation. Virus phylogeographers rely on DNA sequences of viruses and the locations of the infected hosts found in public sequence databases like Gen.


Deep Neural Networks for Query Expansion using Word Embeddings

A Imani, A Vakili, A Montazer, A Shakery - arXiv preprint arXiv:1811.03514, 2018

… and newswire articles. This would make them more suitable for use in newswire collections. Using word embeddings pre-trained using common crawl data may yield better performance in web corpora. To summarize, the proposed …


Page 1. POLITECNICO DI TORINO Master of Science in Computer Engineering Master's Thesis Deep Semantic Learning for Conversational Agents Supervisor Prof. Maurizio Morisio Candidate Martino Mensio Tutor …


Deep-BGT at PARSEME Shared Task 2018: Bidirectional LSTM-CRF Model for Verbal Multiword Expression Identification

G Berk, B Erden, T Güngör - Proceedings of the Joint Workshop on Linguistic …, 2018

… We chose the DEPREL tag as a feature in order to capture dependencies at sentence level. We use pre-trained word embeddings released by fastText (Grave et al., 2018), which were trained on Common Crawl and Wikipedia …


DeepPhish: Simulating Malicious AI

AC Bahnsen, I Torroledo, LD Camacho, S Villegas

… Forest (RF) algorithm. Using a database comprised of one million legitimate URLs from the Common Crawl database, and one million phishing URLs from Phishtank, both models showed great statistical results. On one hand …


DEPARTMENT OF INFORMATICS M. Sc. IN COMPUTER SCIENCE

II Koutsikakis, P Malakasiotis, J Pavlopoulos

Page 1. DEPARTMENT OF INFORMATICS M.Sc. IN COMPUTER SCIENCE M.Sc. Thesis “Toxicity Detection in User Generated Content” Ioannis-Ion Koutsikakis ΕΥ1606 Supervisors: Ion Androutsopoulos Prodromos Malakasiotis …


Department of Informatics MSc in Computer Science

… tasks. Pretrained word vectors are usually trained in large corpora like Wikipedia or/and Common Crawl. Dozat et al. use 100D word2vec (Mikolov et al., 2013) pretrained (on Wikipedia and Common Crawl) word embeddings1. Dozat …


Deriving Word Embeddings Using Multilingual Transfer Learning for Opinion Mining

K Stavridis, G Koloniari, E Keramopoulos - 2018 South-Eastern European Design …, 2018

… A word's context refers to its neighboring words in a text corpus. The Word2Vec model was trained on a huge corpus from Google (news), while the GloVe model has several variations based on the training input such as Wikipedia, Twitter and Common Crawl …


Detecting Misogynous Tweets

R Ahluwalia, E Shcherbinina, E Callow, A Nascimento… - Proceedings of the Third …, 2018

… For comparison, we trained the same LSTM-networks using a fastText model trained on Common Crawl and Wikipedia text that maps words to 300-dimensional vectors [8]. Document-level embedding is an extension …


Detecting Signs of Dementia Using Word Vector Representations

B Mirheidari, D Blackburn, T Walker, A Venneri… - Proc. Interspeech 2018, 2018

… We initially used the 'w2vec' model pre-trained on the Google News dataset (3 million vocabulary size, 100 billions words and 300 vector size) and the 'GloVe' model pre-trained on the Common Crawl (2.2 million vocabulary …


Detection of mergeable Wikipedia articles based on overlapping topics

… Google News 3M vocabulary 100 billion tokens Skip-gram Common Crawl 2.2M vocabulary 840 billion tokens Glove … The results are shown in Table 3 Table 3: Combination model result Combining embedding result(Common Crawl and Google News) accuracy …


Determination of content score

A Kagoshima, K Londenberg, F Xu - US Patent App. 10/325,033, 2019

A Kagoshima, K Londenberg, F Xu - US Patent App. 15/337,268, 2018

… 310. The crawler module 310 may automatically crawl a network and acquire contents from one or more resources in the network, or acquire the contents from an open repository of web crawl data such as CommonCrawl.org …


Developing Chatbots

NK Manaswi - Deep Learning with Applications Using Python, 2018

… 200. 6B. 400,000. GloVe. 10+10. GloVe. Wikipedia + Gigaword 5. 300. 6B. 400,000. GloVe. 10+10. GloVe. Common Crawl 42B. 300. 42B. 1.9M. GloVe. AdaGrad. GloVe. Common Crawl 840B. 300. 840B. 2.2M. GloVe. AdaGrad …


Di-LSTM Contrast: A Deep Neural Network for Metaphor Detection

K Swarnkar, AK Singh - NAACL HLT 2018, 2018

… variations of our model. We use 300-dimensional GloVe vectors (Pennington et al., 2014) trained on 6B Common Crawl corpus as word embeddings, setting the embeddings of out- of-vocabulary words to zero. To prevent overfit …


Differential Approach to Web-Corpus Construction

… This approach does not allow saving maximum of metatext information, since all the boilerplate is deleted, but it allows to gather a lot in a short time (example – Common Crawl). 2) fitted – all the materials from the listed thousands of URLs are crawled …


Discovering Implicit Knowledge with Unary Relations

M Glass, A Gliozzo - Proceedings of the 56th Annual Meeting of the …, 2018

… As part of the contributions for this paper, we release the benchmark to the re- search community providing the software needed to generate it from Common Crawl and DBpedia as an open source project2. As a baseline, we adapt a state of the art deep learning based …


Discovering Novel Emergency Events in Text Streams

D Deviatkin, A Shelmanov, D Larionov - 2018

… of emergency related messages by incorporating more labeled data from CrisisLex corpora [24] and by exploring: ● Various embeddings: word-level: fastText [16] (trained on our own corpus / pre-trained on English Wikipedia) …


Discovering Phonesthemes with Sparse Regularization

… 3 Data For our experiments, we use 300-dimensional GloVe (Pennington et al., 2014) English word em- beddings trained on the cased Common Crawl. Many of the terms in the set of pretrained vectors are not English words …


Discriminator at SemEval-2018 Task 10: Minimally Supervised Discrimination

A Kulmizev, M Abdou, V Ravishankar, M Nissim - Proceedings of The 12th …, 2018

… The VSM used in our final submission consisted of an av- erage of three sets of embeddings: GloVe word embeddings trained on Common Crawl (840B to- kens) (Pennington et al., 2014), the same GloVe embeddings post …


Distinguishing attributes using text corpora and relational knowledge

… Unicode CLDR emoji data • word2vec, precomputed on Google News • GloVe, precomputed on the Common Crawl • fastText, customized to learn from parallel text, trained on OpenSubtitles 2016 We used the embeddings …


Distributed Evaluation of Subgraph Queries Using Worstcase Optimal LowMemory Dataflows

K Ammar, F McSherry, S Salihoglu, M Joglekar - arXiv preprint arXiv:1802.03760, 2018

Page 1. Distributed Evaluation of Subgraph Queries Using Worst-case Optimal Low-Memory Dataflows Khaled Ammar†, Frank McSherry‡, Semih Salihoglu†, Manas Joglekar♯ †University of Waterloo, ‡ETH Zürich,♯Google …


Distributed Representations of Tuples for Entity Resolution

MESTS Joty, MON Tang - Proceedings of the VLDB Endowment, 2018

MESTS Joty, MON Tang - Proceedings of the VLDB Endowment,(11), 2018

… is not always possible or feasible. For example, the popular GloVe dictionary is trained on the Common Crawl corpus, which is almost 2 TB requiring exorbitant computing re- sources. Given an unseen word, another simplistic …


DL Team at SemEval-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings

D Kravchenko, L Pivovarova - Proceedings of The 12th International Workshop on …, 2018

… We use the following two models: 1. Common Crawl: 300-dimensional vectors trained on huge Internet corpus of 840 billion tokens and 2.2 million distinct words … GloVe Common Crawl 46.93 53.98 43.66 56.31 66.38 59.26 Google …


DMCB at SemEval-2018 Task 1: Transfer Learning of Sentiment Classification Using Group LSTM for Emotion Intensity prediction

Y Kim, H Lee - Proceedings of The 12th International Workshop on …, 2018

… We try five pre-trained word embeddings to choose the best one for the target model. Two are trained with GloVe (Pennington et al., 2014) using different data sets: one1 is trained with very large data in Common crawl, and the …


Domain Adapted Word Embeddings for Improved Sentiment Classification

PK Sarma, YI Liang, WA Sethares - arXiv preprint arXiv:1805.04576, 2018

… Word embedding Dimension GloVe 100 word2vec 300 LSA 70 CCA-DA 68 KCCA-DA 68 GloVe common crawl 300 AdaptGloVe 300 … WG ∈ R|VG|×d2 be the matrix of generic word embeddings (obtained by, eg, running …


Dropout during inference as a model for neurological degeneration in an image captioning network

B Li, R Zhang, F Rudzicz - arXiv preprint arXiv:1808.03747, 2018

… The neural network model is implemented us- ing PyTorch (Paszke et al., 2017). We use GLoVe embeddings from SpaCy (Pennington et al., 2014; Honnibal and Johnson, 2015) trained on Common Crawl. The network was trained using Adam (Kingma and Ba, 2014) …


Dynamic Meta-Embeddings for Improved Sentence Representations

D Kiela, C Wang, K Cho - Proceedings of the 2018 Conference on Empirical …, 2018

… Figure 4 shows a similar breakdown for open versus closed class. The analysis allows us to make several interesting observations: it appears that this model prefers GloVe embeddings, followed by the two FastText …


Effective Strategies for Combining Attention Mechanism with LSTM for Aspect-Level Sentiment Classification

K Shuang, X Ren, H Guo, J Loo, P Xu - Proceedings of SAI Intelligent Systems …, 2018

… 346. Neg. 807. 196. 870. 128. 1560. 173. Total. 3608. 1120. 2328. 638. 6248. 692. 4.2 Experimental Settings. In the experiments, pre-trained word vectors are trained by Glove on Common Crawl Corpus size and they are used to …


S Min, V Zhong, R Socher, C Xiong - arXiv preprint arXiv:1805.08092, 2018

Page 1. Efficient and Robust Question Answering from Minimal Context over Documents Sewon Min1∗, Victor Zhong2, Richard Socher2, Caiming Xiong2 Seoul National University1, Salesforce Research2 shmsw25@snu …


Efficient Unsupervised Word Sense Induction, Disambiguation and Embedding

… as sentiment analysis, translation, etc. 2 Proposed Method Similar to word embeddings, sense embeddings should also be learned from a large text corpus such as Wikipedia or Common Crawl data. In our method, we first calculate …


Elastic ChatNoir: Search Engine for the ClueWeb and the Common Crawl

J Bevendorff, B Stein, M Hagen, M Potthast - European Conference on Information …, 2018

Abstract Elastic ChatNoir (Search: www. chatnoir. eu Code: www. github. com/chatnoir-eu) is an Elasticsearch-based search engine offering a freely accessible search interface for the two ClueWeb corpora and the Common Crawl …


EmbNum: Semantic labeling for numerical values with deep metric learning

P Nguyen, K Nguyen, R Ichise, H Takeda - arXiv preprint arXiv:1807.01367, 2018

… In a study of Lehmberg et al. [1], 233 million table data resources have extracted from the July 2015 version of the Common Crawl3. Additionally, Mitlohner et al … Table schema is 3 http://commoncrawl.org/ arXiv:1807.01367 …


Emergency Vocabulary

DM Nemeskey, A Kornai - Information Systems Frontiers

… 2004) and CommonCrawl.1 In the normal course of events, emergencies like natural disasters, military escalation, epidemic outbreaks etc … 5 Originally, the embedding contains 2,196,017 words with the associated 300-dimension …


Emotion Detection and Classification in a Multigenre Corpus with Joint Multi-Task Deep Learning

S Tafreshi, M Diab - Proceedings of the 27th International Conference on …, 2018

… Common training set for word embedding models are wiki+news, news, tweets, and common crawl, and the methods are Word2Vec (Mikolov et al., 2013), GloVe (Pennington et al., 2014), and fasText (Bojanowski et al., 2016) …


EmotionLines: An Emotion Corpus of Multi-Party Conversations

SY Chen, CC Hsu, CC Kuo, LW Ku - arXiv preprint arXiv:1802.08379, 2018

… Given a utterance of M words, the one-hot encoding for utterance words is denoted by U = {w1, w2, w3,..., wM }. We first embed the words to the word embedding , which is publicly available 300-dimensional GloVe pre-trained on Common Crawl data (Pennington et al., 2014) …


EmotionX-JTML: Detecting emotions with Attention

J Torres - Proceedings of the Sixth International Workshop on …, 2018

… Each column of the matrix stores the em- beddings of the corresponding word, resulting in d dimensional input matrix M ∈ RM×d. The weights of the word embeddings use the 300dimensional GloVe Embeddings …


End-to-End Retrieval in Continuous Space

D Gillick, A Presta, GS Tomar - arXiv preprint arXiv:1811.08008, 2018

… embeddings using Inverse Document Frequency (IDF)4, and try 3 different settings for pre-training: standard word2vec, word2vec trained with the Paralex dataset (closer to the question domain), and Glove (Pennington …


Energy-Aware Data Throughput Optimization for Next Generation Internet

T Kosar, I Alan, MF Bulut - Information Sciences, 2018

… Three different representative datasets were used during experiments in order to capture the throughput and power consumption differences based on the dataset type: (i) the HTML dataset is a set of raw HTML files from the …


Engaging English Speaking Facebook Users in an Anti-ISIS Awareness Campaign

A Shajkovci - Journal of Strategic Security, 2018

… STRONG VERY LOW 0 133 able, act, adult, advantage, aggressive, agreement, army, assistance, attack, aware, benefit, b . . . WEAK LOW 16 41 admit, apologize, ashamed, bum, common, crawl, death, depression, desperate, die, disgrace, dis …


English-Basque Statistical and Neural Machine Translation

IJ Unanue, LG Arratibel, EZ Borzeshi, M Piccardi - Proceedings of the Eleventh …, 2018

… For English, we have used the available CommonCrawl pre-trained embeddings3. We have evaluated the use of these embeddings in two different ways: maintaining them fixed during both training and testing (f-emb) …


Entity-Oriented Search

Page 1. The Information Retrieval Series Krisztian Balog EntityOriented Search Page 2. The Information Retrieval Series Volume 39 Series Editors ChengXiang Zhai Maarten de Rijke Editorial Board Nicholas J. Belkin Charles …


ERASeD: Exposing Racism And Sexism using Deep Learning

… We tried out the following methods: • Pretrained Embeddings – GloVe (Twitter) – GloVe (Common Crawl) The biggest drawback of using pretrained embeddings is that 20% of the words we see in the dataset from Twitter do not have embeddings …


eSCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing

M Negri, M Turchi, R Chatterjee, N Bertoldi - arXiv preprint arXiv:1803.07274, 2018

… It consists of 4.3 million instances created by first filtering a subset of IT-related sentences from the German Common Crawl corpus6, and then by using two English–German and German–English PBMT systems trained on …


Evaluating MT for massive open online courses

S Castilho, J Moorkens, F Gaspari, R Sennrich, A Way… - Machine Translation, 2018

… 2012) as distributed in OPUS (Tiedemann 2012) – OPUS European Central Bank (ECB) – OPUS European Medicines Agency (EMEA) – OPUS EU Bookshop – OPUS OpenSubtitles5 – WMT News Commentary – WMT …


Evaluating the psychological plausibility of word2vec and GloVe distributional semantic models

… In this work, we use pre-trained GloVe and word2vec vectors. GloVe vectors were trained using the Common Crawl dataset containing approximately 840 billion word tokens. Word2vec vectors were trained on the Google News …


Page 1. Evaluation of High-Dimensional Word Embeddings using Cluster and Semantic Similarity Analysis by Shahin Atakishiyev A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science …


Evaluation of sentence embeddings in downstream and linguistic probing tasks

CS Perone, R Silveira, TS Paula - arXiv preprint arXiv:1806.06259, 2018

… 1024 Word2Vec (BoW, Google news) Self-supervised 300 p-mean (monolingual) – 3600 FastText (BoW, Common Crawl) Self-supervised 300 GloVe (BoW, Common Crawl) Self-supervised 300 USE (DAN) Supervised 512 USE (Transformer) Supervised 512 …


Evidence of semantic processing difficulty in naturalistic reading

C Shain, R Futrell, M van Schijndel, E Gibson…

… a novel measure — incremental semantic relatedness — for three naturalistic reading time corpora: Dundee [12], UCL [7], and Natural Stories [9]. In particular, we embedded all three corpora using GloVe vectors [20] pretrained …


Existing Web Archives

P Webster - The SAGE Handbook of Web History. London: Sage, 2018


Explicit Ensemble Attention Learning for Improving Visual Question Answering

V Lioutas, N Passalis, A Tefas - Pattern Recognition Letters, 2018

Visual Question Answering (VQA) is among the most difficult multi-modal problems as it requires a machine to be able to properly understand a question about ar.


Explicit Retrofitting of Distributional Word Vectors

G Glavaš, I Vulić - Proceedings of the 56th Annual Meeting of the …, 2018

… al., 2013) using the Skip-Gram algorithm with Negative Sampling (SGNS) (Mikolov et al., 2013b) by Levy and Goldberg (2014b), using the context windows of size 2; (2) GLOVE-CC – vectors trained with the GloVe (Pennington …


Exploiting Efficient and Effective Lazy Semi-Bayesian Strategies for Text Classification

F Viegas, L Rocha, E Resende, T Salles, W Martins… - Neurocomputing, 2018

Automatic Document Classification (ADC) has become the basis of many important applications, eg, authorship identification, opinion mining, spam filtering, co.


Exploring and Mitigating Gender Bias in GloVe Word Embeddings

… and the cosine similarity. Finally, to show that gender 100 biases exist across different types of embeddings, we use both the GloVe word embeddings 101 pretrained on Wiki and pretrained on Common Crawl. 102 Plotting all …


L Song, Z Wang, M Yu, Y Zhang, R Florian, D Gildea - arXiv preprint arXiv …, 2018

Page 1. Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks Linfeng Song1∗, Zhiguo Wang2†, Mo Yu2†, Yue Zhang3, Radu Florian2 and Daniel Gildea1 …


Exploring Neural Methods for Parsing Discourse Representation Structures

R van Noord, L Abzianidze, A Toral, J Bos - arXiv preprint arXiv:1810.12579, 2018

… We simply add the word embedding vector after the sequence of character-embeddings for each word in the input and still initialise these embeddings using the pre-trained GloVe embeddings. 6The Common …


Exploring Optimism and Pessimism in Twitter Using Deep Learning

… We used mini-batches of 40 samples. Dropout rate was set to 0.5 and the classifier's last three layers have 300, 200, and 100 neurons. We used GloVe vectors (Pennington et al., 2014) trained on Common Crawl …


Exploring speed and memory trade-offs for achieving optimum performance on SQuAD dataset

… bidirectional in this architecture was not producing any measurable gains for some time, until I also switched to Common Crawl embedding with … When I switched to CommonCrawl embeddings, I have found out that Tensorflow …


Exploring the importance of context and embeddings in neural NER models for task-oriented dialogue systems

P Jayarao, C Jain, A Srivastava - arXiv preprint arXiv:1812.02370, 2018

… We used the publicly available Glove embeddings1 (Pennington et al., 2014) trained on Wikipedia 2014 corpus with dimension sizes 50 (G50W) and 300 (G300W) and another 300 di- mensional embeddings (G300C) trained on …


ExtRA: Extracting Prominent Review Aspects from Customer Feedback

Z Luo, S Huang, FF Xu, BY Lin, H Shi, K Zhu - … of the 2018 Conference on Empirical …, 2018

Page 1. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3477–3486 Brussels, Belgium, October 31 - November 4, 2018. c 2018 Association for Computational Linguistics 3477 …


Extrofitting: Enriching Word Representation and its Vector Space with Semantic Lexicons

H Jo, SJ Choi - arXiv preprint arXiv:1804.07946, 2018

… 3 Experiment Data 3.1 Pretrained Word Vectors GloVe (Pennington et al., 2014) has lots of variations in respect to word dimension, number of to- kens, and train sources. We used glove.6B trained on Wikipedia+Gigawords …


JG Cabañas, Á Cuevas, R Cuevas - arXiv preprint arXiv:1802.05030, 2018

Page 1. arXiv:1802.05030v1 [cs.SI] 14 Feb 2018 Facebook Use of Sensitive Data for Advertising in Europe José González Caba˜nas Universidad Carlos III de Madrid jgcabana@it.uc3m.es ´Angel Cuevas Universidad Carlos III de Madrid acrumin@it.uc3m.es …


Facilitating mapping of control policies to regulatory documents

SB Tirumala, A Jagmohan, E Khabiri, TH Li… - US Patent App. 15/349,766, 2018

… dictionary 206). The global corpora 203 can comprise a general internet-based collection of texts derived from various sources (eg, GUTENBERG®, REUTERS®, COMMON CRAWL®, and/or GOOGLE NEWS®). The regulatory …


Fast Dictionary-Based Compression for Inverted Indexes

… We use the standard Gov2 collection containing 426 GiB of text; and CCNEWS, an English subset of the freely available NEWS subset of the CommonCrawl1, consisting of news articles in the period 09/01/16 to …


Filtering and Mining Parallel Data in a Joint Multilingual Space

H Schwenk - arXiv preprint arXiv:1805.09822, 2018

… only a limited amount of parallel training data is available (4.5M sentences). 2.1M are high quality human translations and 2.4M are crawled and aligned sentences (Common Crawl corpus). As in other works, we use …


Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds

S Zhang, K Duh, B Van Durme - arXiv preprint arXiv:1804.08000, 2018

… 3.2 Hyperparameters We use open-source GloVe vectors (Pennington et al., 2014) trained on Common Crawl 840B with 300 dimensions to initialize word embeddings used in all encoders. All weight parameters are sampled from U(−0.01,0.01) …


Focused Crawling Through Reinforcement Learning

M Han, PH Wuillemin, P Senellart - International Conference on Web Engineering, 2018

… efficiently crawl relevant pages. In future work, we hope to evaluate our method in larger and various datasets, such as full English Wikipedia and dataset from the site http://commoncrawl.org/, etc. Another challenging possibility …


Follow The Money: Online Piracy and Self-Regulation in the Advertising Industry

… Get HTML source code files from Commoncrawl and parse them (∼500,000 webpages) … 10 Page 14. Figure 2: Timing of data snapshots 2016w7 2016w15 2016w23 2016w31 2016w39 2016w47 2017w3 2017w11 Note: Available data snapshots from Common Crawl …


Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities: Supplementary Material

H Pham, PP Liang, T Manzini, LP Morency, B Poczós

… visual and acoustic modalities. Language: We used 300 dimensional Glove word embeddings trained on 840 billion tokens from the common crawl dataset (Pennington, Socher, and Manning, 2014). These word embeddings …


Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation

B Thompson, H Khayrallah, A Anastasopoulos… - arXiv preprint arXiv …, 2018

… 1 For De–En and Ru– En, we also use data from WMT 2017 (Bojar et al., 2017),2 which contains data from several sources: Europarl (parliamentary proceedings) (Koehn, 2005),3 News Commentary (political and economic …


Page 1. From Mad Men to Maths Men: Concentration and Buyer Power in Online Advertising ∗ Francesco Decarolis Gabriele Rovigatti PRELIMINARY AND INCOMPLETE PLEASE DO NOT CIRCULATE Abstract This paper …


FROM TEXT CLASSIFICATION TO IMAGE CLUSTERING, PROBLEMS LESS OPTIMIZED

… We have used similar models for our BiLSTM and BiGRU models. We are using two layers stacked on top of each other and use only the last output from each direction from the second layer for our inference, because we are 1 http://commoncrawl.org/ Page 18. 10 …


Full text document

… set3 (SG-GN). We also use two embeddings that have been learned with GloVe, one from the same english Wikipedia dump (GloVe-Wiki) and one from the 840B words Common Crawl data set4 (GloVe-CC). For relations with …


Fully Convolutional Speech Recognition

N Zeghidour, Q Xu, V Liptchinsky, N Usunier… - arXiv preprint arXiv …, 2018

… 5.83 12.69 (12k training hours AM, common crawl LM) Sequence-to- sequence [9] 3.54 11.52 3.82 12.76 … (speaker adaptation, 3k acoustic states) DeepSpeech 2 [7] 5 3.6 (12k training hours AM, common crawl LM) Frontend …


Garbage In, Garbage Out

… VirusTotal 10 million URLs from January 2017 ≈ 4% malware CommonCrawl & PhishTank Sophos 10 million internal URLs from January … VirusTotal 10 million URLs from January 2017 ≈ 4% malware CommonCrawl & Phishtank …


Gated Convolutional Neural Network for Sentence Matching

P Chen, W Guo, Z Chen, J Sun, L You - memory, 2018

… 3.2. Training We implement our models on Tensorflow [26] and train them on an Nvidia GeForce GTX 1080 GPU. We initialize the word em- beddings with 300-dimensional GloVe word vectors pretrained from 840B Common Crawl corpus [19] …


Generalizing and Improving Bilingual Word Embedding Mappings with a Multi-Step Framework of Linear Transformations

… The corpora used consisted of 2.8 billion words for English (ukWaC + Wikipedia + BNC), 1.6 billion words for Italian (itWaC), 0.9 billion words for German (SdeWaC), and 2.8 billion words for Finnish (Common Crawl from WMT 2016) …


Generating Wikipedia by Summarizing Long Sequences

PJ Liu, M Saleh, E Pot, B Goodrich, R Sepassi, L Kaiser… - arXiv preprint arXiv …, 2018

… To encourage further research on large-scale summarization, we will release the URLs used in our experiments (the Wikipedia URL as well as the URLs of its references) that are available as part of the CommonCrawl dataset4, which is freely available for download …


Generation of code from text description with syntactic parsing and Tree2Tree model


Global normalized reader systems and methods

J RAIMAN, J Miller - US Patent App. 15/706,486, 2018

… prediction. The hidden dimensions of all recurrent layers were 200. In embodiments, the 300 dimensional 8.4B token Common Crawl GloVe vectors were used. Words missing from the Common Crawl vocabulary were set to zero. In …


A Wang, A Singh, J Michael, F Hill, O Levy… - arXiv preprint arXiv …, 2018

Page 1. arXiv:1804.07461v1 [cs.CL] 20 Apr 2018 GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Wang1, Amanpreet Singh1, Julian Michael2, Felix Hill3, Omer Levy2, and Samuel R. Bowman1 …


GraphIt-A High-Performance DSL for Graph Analytics

Y Zhang, M Yang, R Baghdadi, S Kamil, J Shun… - arXiv preprint arXiv …, 2018

Page 1. GraphIt – A High-Performance DSL for Graph Analytics YUNMING ZHANG, MIT CSAIL MENGJIAO YANG, MIT CSAIL RIYADH BAGHDADI, MIT CSAIL SHOAIB KAMIL, Adobe Research JULIAN SHUN, MIT CSAIL SAMAN AMARASINGHE, MIT CSAIL …


Harnessing NLG to Create Finnish Poetry Automatically

… This is 3The semantic repository can be browsed and downloaded on https://mikakalevi. com/semfi/ 4http://commoncrawl.org/ due to the Finnish agreement rule which requires the case of an adjective attribute to agree with the case of the noun …


Heuristics-based Query Reordering for Federated Queries in SPARQL 1.1 and SPARQL-LD

… The question is: how 3 http://commoncrawl.org/ 4 http://webdatacommons. org/structureddata/2016-10/stats/stats.html Page 2. 2 Thanos Yannakis, Pavlos Fafalios, and Yannis Tzitzikas can we efficiently query this large, distributed …


HFL-RC System at SemEval-2018 Task 11: Hybrid Multi-Aspects Model for Commonsense Reading Comprehension

Z Chen, Y Cui, W Ma, S Wang, T Liu, G Hu - arXiv preprint arXiv:1803.05655, 2018

… Page 4. 3 Experiments 3.1 Experimental Setups We listed the main hyper-parameters of our model in Table 1. The word embeddings are initialized by the pre-trained GloVe word vectors (Common Crawl, 6B tokens, 100-dimension) (Pennington et al., 2014) …


Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts

D Jin, P Szolovits - arXiv preprint arXiv:1808.06161, 2018

… pus of Wikipedia 2014 + Gigaword 57 (denoted as “Glove-wiki”), fastText embeddings pre-trained on Wikipedia8 (denoted as “FastText-wiki”), and fastText embeddings initialized with the standard GloVe Common Crawl …


Historical Web as a Tool for Analyzing Social Change

R Schroeder, N Brügger, J Cowls - Second International Handbook of Internet …, 2018

… are themselves responsible for the curation and selection of what to archive (eg, Archive-It), and, second, publicly available collections, archived by “amateurs,” with no cultural heritage obligations (eg, The Archive Team …


Hourglass-Incremental Graph Processing on Heterogeneous Infrastructures

… On June 2016 Facebook reportes[13] an average of 1.13 billion daily active users. Another example is the largest World Wide Web hyperlink graph made publicly available by Common Crawl[14] that has over 3.5 billion web pages and 128 billion hyperlinks between them …


How to Do Things with Translations

… 17 Page 18. as the Common Crawl corpus19 often have vocabulary sizes v exceeding 2 million, thus re … 19http://commoncrawl.org/ 20For a comprehensive survey of the most popular embedding algorithms, see Goldberg (2017). 18 Page 19 …


Human versus automatic quality evaluation of NMT and PBSMT

D Shterionov, R Superbo, P Nagle, L Casanellas… - Machine Translation, 2018

… 2015) is built with TED talks, EPPS, news commentary,8 and Common Crawl data; the NMT system they compare to (Luong and Manning 2015) is a pre-trained NMT system that was further improved with data provided by the IWSLT2015 organizers …


Hybrid Self-Attention Network for Machine Translation

K Song, T Xu, F Peng, J Lu - arXiv preprint arXiv:1811.00253, 2018

… 2016) shared vocabulary. WMT14 English-German WMT14 EnglishGerman dataset (Buck, Heafield, and van Ooyen 2014) comprises about 4.5 million sentence pairs that are extracted from three corpora: Common …


Hypothesis Only Baselines in Natural Language Inference

A Poliak, J Naradowsky, A Haldar, R Rudinger… - arXiv preprint arXiv …, 2018

… Following Conneau et al. (2017), we map the resulting to- kens to 300-dimensional GloVe vectors (Pennington et al., 2014) trained on 840 billion tokens from the Common Crawl, using the GloVe OOV vector for unknown words …


Identifying Semantic Divergences in Parallel Text without Annotations

Y Vyas, X Niu, M Carpuat - arXiv preprint arXiv:1803.11112, 2018

… On the CommonCrawl test set, the examples with disagreement are more diverse, ranging from Page 7. Divergence Detection OpenSubtitles Common Crawl Approach +P +R +F -P -R -F Overall F +P +R +F -P -R …


Identifying the Most Effective Feature Category in Machine Learning-based Phishing Website Detection

CL Tan, KL Chiew, N Musa, DHA Ibrahim - International Journal of Engineering & …, 2018

… [31] “Common Crawl”, available online: http://commoncrawl.org/, last visit: 10.01.2017. [32] Selenium Project (2017), “Selenium WebDriver”, available online: http://www.seleniumhq.org/projects/webdriver/, last visit: 10.01.2017 …


Improved Text Analytics Transfer Learning

… Our GRU model was fed a sequence of fixed 300 dimensional Glove vectors (Pennington et al., 2014), representing words based on analysis of 840 billion words from a common crawl of the internet, as the input xt for all tasks …


Improving Cross-Lingual Word Embeddings by Meeting in the Middle

Y Doval, J Camacho-Collados, L Espinosa-Anke… - arXiv preprint arXiv …, 2018

… For Italian and German, we use the itWaC and sdeWaC corpora from the WaCky project (Baroni et al., 2009), containing 2 and 0.8 billion words, respectively.2 Lastly, for Finnish, we use the Common Crawl monolingual …


Improving Machine Translation of Educational Content via Crowdsourcing

M Behnke, AVM Barone, R Sennrich, V Sosoni…

… et al., 2012) as distributed in OPUS (Tiedemann, 2012); ● OPUS European Central Bank (ECB); ● OPUS European Medicines Agency (EMEA); ● OPUS EU Bookshop; ● OPUS OpenSubtitles 7; ● WMT News Commentary; ● …


Improving Neural Machine Translation with External Information

… image descriptions, we selected similar sentence pairs from the parallel SDEWAC corpus (Faaß and Eckart, 2013) and German parts of WMT parallel corpora, such as EU Bookshop (Skadinš et al., 2014), News Commentary (Tiedemann, 2012), and CommonCrawl (Smith et al …


Improving Personalized Consumer Health Search: Notebook for eHealth at CLEF 2018

H Yang, T Gonçalves - CEUR Workshop Proceedings: Working Notes of CLEF, 2018

… Page 4. 3.2 Dataset The dataset used in CLEF 2018 CHS task consisted of web pages acquired from the CommonCrawl. By submitting the task queries to the Microsoft Bing APIs repeatedly over a period of time, an initial …


Improving the learning of chemical-protein interactions from literature using transfer learning and word embeddings.

… GloVe offers both some public pre-trained embeddings (based on Wikipedia and Common Crawl), and also the software to compile your own – in previous work (6) we had success with the public embeddings, but here, we compiled our own …


Improving Word Embedding Compositionality using Lexicographic Definitions

T Scheepers, E Gavves, E Kanoulas - 2017

… We used GloVe representations that where trained on the Common Crawl which has 840B tokens and a vocabulary of 2.2M. fastText uses a training algorithm that is based on skip-gram, however it represents each word as a bag of character n-grams …


Improving Word Embedding Coverage in Less-Resourced Languages Through Multi-Linguality and Cross-Linguality: A Case Study with Aspect-Based Sentiment …

MS Akhtar, P Sawant, S Sen, A Ekbal, P Bhattacharyya - ACM Transactions on Asian …, 2018

… However, these systems require a large corpus for training and building the models (eg, the pretrained Google News Word2Vec model was trained on 100 billion words, pretrained common-crawl GloVe model was trained on 840 billion words) …


Incorporating Statistical Machine Translation Word Knowledge into Neural Machine Translation

X Wang, Z Tu, M Zhang - IEEE/ACM Transactions on Audio, Speech, and …, 2018

Page 1. 2329-9290 (c) 2018 IEEE. Personal use is permitted, but republication/ redistribution requires IEEE permission. See http://www.ieee.org/ publications_standards/publications/rights/index.html for more information. This …


L Shu, P Molino, M Namazifar, B Liu, H Xu, H Zheng…

Page 1. Incorporating the Structure of the Belief State in End-to-End Task-Oriented Dialogue Systems Lei Shu∗1, Piero Molino2, Mahdi Namazifar3, Bing Liu1, Hu Xu1, Huaixiu Zheng3, and Gokhan Tur3 1University …


Inducing Grammars with and for Neural Machine Translation

K Tran, Y Bisk - arXiv preprint arXiv:1805.10850, 2018

Y Bisk, K Tran - Proceedings of the 2nd Workshop on Neural Machine …, 2018

… Table 1 shows the statistics of the data. For En↔De, we use a concatenation of Europarl, Common Crawl, Rapid corpus of EU press releases, and News Commentary v12 … For En↔Ru, we use Common Crawl, News Commentary v12, and Yandex Corpus …


Inducing Implicit Relations from Text Using Distantly Supervised Deep Nets

M Glass, A Gliozzo, O Hassanzadeh… - International Semantic Web …, 2018

… We also evaluate on a web-scale knowledge base population benchmark that we called CC-DBP 5 . It combines the text of Common Crawl 6 with the triples from 298 frequent relations in DBpedia [1]. Mentions of DBpedia entities …


InferLite: Simple Universal Sentence Representations from Natural Language Inference Data

J Kiros, W Chan - Proceedings of the 2018 Conference on Empirical …, 2018

… (2018) describe a contextual gating Feature dataset dim method Glove Common Crawl 300 News Google News 500 CBOW Query Google Search 800 CBOW Table 1: Comparison of word representations used. method for word embedding selection …


Inferring gender of Reddit users

Page 1. People and Knowledge Networks WeST Fachbereich 4: Informatik Institute for Web Science and Technologies Inferring gender of Reddit users Masterarbeit zur Erlangung des Grades einer Master of Science (M.Sc.) im Studiengang Web Science …


Inferring Missing Categorical Information in Noisy and Sparse Web Markup

N Tempelmeier, E Demidova, S Dietze - arXiv preprint arXiv:1803.00446, 2018

… information. For instance, from 26 million nodes describing events within the Common Crawl in 2016, 59% of nodes provide less than six statements and only 257,000 nodes (0.96%) are typed with more specific event subtypes …


Integrating Multiplicative Features into Supervised Distributional Methods for Lexical Entailment

T Vu, V Shwartz - arXiv preprint arXiv:1804.08845, 2018

… We used 300-dimensional pre-trained word em- beddings, namely, GloVe (Pennington et al., 2014b) containing 1.9M word vectors trained on a corpus of web data from Common Crawl (42B to- kens),1 and Word2vec (Mikolov …


Intent Generation for Goal-Oriented Dialogue Systems based on Schema. org Annotations

U Şimşek, D Fensel - arXiv preprint arXiv:1807.01292, 2018

… These vectors are trained based on existing ConceptNet 5.5 knowledge graph3 data, word2vec embeddings [11] trained with 300B words from Google News Dataset and GloVe embeddings [15] trained with 840B words from …


Interactions and influence of world painters from the reduced Google matrix of Wikipedia networks

SE Zant, K Jaffrès-Runser, KM Frahm… - arXiv preprint arXiv …, 2018

… At present, directed networks of real systems can be very large (about 4.2 million articles for the English Wikipedia edition in 2013 [16] or 3.5 billion web pages for a publicly accessible web crawl that was gathered by the Common Crawl Foundation in 2012 [25]) …


AS Correa, PO Zander, FSC da Silva - Proceedings of the 19th Annual International …, 2018

Page 1. Investigating open data portals automatically: a methodology and some illustrations Andreiwid Sheffer Correa Federal Institute of Sao Paulo Campinas, Sao Paulo, Brazil University of Sao Paulo Sao Paulo, Brazil andreiwid@ifsp.edu.br …


Iterative reasoning with bi-directional attention flow for machine comprehension

… [2016] pre-trained on the common crawl dataset … 4.2 Implementation Details We used pre-trained case sensitive 300-dimensional pre-trained word embeddings trained on Common Crawl and Wikipedia using fastText …


Iterative Recursive Attention Model for Interpretable Sequence Classification

M Tutek, J Šnajder - arXiv preprint arXiv:1808.10503, 2018

… We clip the global norm of the gradients to 1.0 and set weight decay to 0.00003. We use 300-dimensional GloVe word embeddings trained on the Common Crawl corpus and 100-dimensional character embeddings. We follow the recommendation of Mu et al …


KnowMore-Knowledge Base Augmentation with Structured Web Markup

R Yua, U Gadirajua, B Fetahua, O Lehmbergb…

Page 1. Semantic Web 0 (0) 1 1 IOS Press KnowMore - Knowledge Base Augmentation with Structured Web Markup Editor(s): Name Surname, University, Country Solicited review(s): Name Surname, University, Country Open review(s): Name Surname, University, Country …


L'optimisation des plongements de mots pour le français: une application de la classification des phrases

J Park - Actes de la conférence Traitement Automatique de la …

… c ATALA 2018 283 Page 299. Wikipedia Europarl New crawls Common crawl Giga in-domain total 675M 64M 223M 89M 793M 664M 2.5 B TABLE 1–The size (# of token) of corpora for embedding 2.3 Lemma-aware …


LA3: A Scalable Link-and Locality-Aware Linear Algebra-Based Graph Analytics System

Y Ahmad, O Khattab, A Malik, A Musleh, M Hammoud… - Proceedings of the VLDB …, 2018

Page 1. LA3: A Scalable Linkand Locality-Aware Linear Algebra-Based Graph Analytics System Yousuf Ahmad, Omar Khattab, Arsal Malik, Ahmad Musleh, Mohammad Hammoud Carnegie Mellon University in Qatar 1myahmad …


Language Modeling at Scale

M Patwary, M Chabbi, H Jun, J Huang, G Diamos… - arXiv preprint arXiv …, 2018

… The figure shows four datasets: 1-Billion word [6] (1b), Gutenberg [7] (gb), Common crawl [8] (cc), and Amazon review [9] (ar) … The 4 lines correspond to 4 datasets: one Billion word (1b), Gutenberg (gb), Common Crawl (cc), and Amazon Review (ar). TABLE I DATASETS …


Language use shapes cultural norms: Large scale evidence from gender

… Results Figure 2 shows the effect size measures derived from the English Wikipedia corpus plotted against effect size estimates reported by CBN from two different models (trained on the Common Crawl and Google News corpora) … model Common Crawl (GloVe) …


Large scale distributed neural network training through online distillation

AT Passos, G Pereyra, G Hinton, G Dahl, R Ormandi… - 2018

R Anil, G Pereyra, A Passos, R Ormandi, GE Dahl… - arXiv preprint arXiv …, 2018

… 2http://commoncrawl.org/2017/07/june-2017-crawl-archive-now-available/ 3It is still infeasible to train on all of this data set with large neural language models, so our experiments did not … We use the ADAM optimizer Kingma …


Large-Scale Analysis of Style Injection by Relative Path Overwrite

S Arshad, SA Mirheidari, T Lauinger, B Crispo, E Kirda… - 2018

… using RPO. We extract pages using relativepath stylesheets from the Common Crawl dataset [9], automatically test if style directives can be injected using RPO, and determine whether they are interpreted by the browser. Out …


Latent Question Interpretation Through Parameter Adaptation Using Stochastic Neuron

… In effect, it led to maximally effective behaviour in the question-answering task. 6 Experiments Implementation Details For the word embeddings we use GloVe embeddings pretrained on the 840B Common Crawl corpus [Pennington et al., 2014] …


Latent Semantic Analysis Approach for Document Summarization Based on Word Embeddings

K Al-Sabahi, Z Zuping, Y Kang - arXiv preprint arXiv:1807.02748, 2018

… Training is performed on aggregated global word-word co-occurrence statistics from a corpus. In this work, we use the one trained on Common Crawl (840B tokens, 2.2M vocab, cased, 300d vectors, 2.03 GB download): glove.840B.300d.zip. 3.2. LSA algorithm …


A Sjösten, S Van Acker, P Picazo-Sanchez, A Sabelfeld - Power, 2018

Page 1. LATEX GLOVES: Protecting Browser Extensions from Probing and Revelation Attacks Alexander Sjösten∗, Steven Van Acker∗, Pablo Picazo-Sanchez and Andrei Sabelfeld Chalmers University of Technology …


Learning Emotion-enriched Word Representations

A Agrawal, A An, M Papagelis - Proceedings of the 27th International Conference on …, 2018

… GloVe 6B Wiki + Gigaword 6B tokens 400K GloVe 42B Common Crawl 42B tokens 1.9M … Paired t-tests using the results on all four datasets indicate EWE is significantly better than all the other methods with p-values < 0.02. uncased …