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Between History and Natural Language Processing: Study, Enrichment and Online Publication of French Parliamentary Debates of the Early Third Republic (1881-1899)
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In: ParlaCLARIN III at LREC2022 - Workshop on Creating, Enriching and Using Parliamentary Corpora ; https://hal.archives-ouvertes.fr/hal-03623351 ; ParlaCLARIN III at LREC2022 - Workshop on Creating, Enriching and Using Parliamentary Corpora, Jun 2022, Marseille, France ; https://www.clarin.eu/ParlaCLARIN-III (2022)
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| 2 |
Chinese-Uyghur Bilingual Lexicon Extraction Based on Weak Supervision
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In: Information; Volume 13; Issue 4; Pages: 175 (2022)
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Investigating the Efficient Use of Word Embedding with Neural-Topic Models for Interpretable Topics from Short Texts
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In: Sensors; Volume 22; Issue 3; Pages: 852 (2022)
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| 4 |
Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 9; Pages: 5705 (2022)
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| 5 |
An Enhanced Neural Word Embedding Model for Transfer Learning
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In: Applied Sciences; Volume 12; Issue 6; Pages: 2848 (2022)
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| 6 |
Deep Sentiment Analysis Using CNN-LSTM Architecture of English and Roman Urdu Text Shared in Social Media
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2694 (2022)
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| 7 |
Predicting Academic Performance: Analysis of Students’ Mental Health Condition from Social Media Interactions
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In: Behavioral Sciences; Volume 12; Issue 4; Pages: 87 (2022)
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| 8 |
Vec2Dynamics: A Temporal Word Embedding Approach to Exploring the Dynamics of Scientific Keywords—Machine Learning as a Case Study
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In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 21 (2022)
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| 9 |
Methods, Models and Tools for Improving the Quality of Textual Annotations
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In: Modelling; Volume 3; Issue 2; Pages: 224-242 (2022)
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| 10 |
Deep Learning with Word Embedding Improves Kazakh Named-Entity Recognition
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In: Information; Volume 13; Issue 4; Pages: 180 (2022)
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| 11 |
Text mining at multiple granularity: leveraging subwords, words, phrases, and sentences
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| 13 |
Investigating the impact of preprocessing on document embedding: an empirical comparison
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In: ISSN: 1759-1163 ; EISSN: 1759-1171 ; International Journal of Data Mining, Modelling and Management ; https://hal.inrae.fr/hal-03574696 ; International Journal of Data Mining, Modelling and Management, Inderscience, 2021, 13 (4), pp.351-363 (2021)
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| 14 |
Creating multi-scripts sentiment analysis lexicons for Algerian, Moroccan and Tunisian dialects
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In: 7th International Conference on Data Mining (DTMN 2021) Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) ; https://hal.archives-ouvertes.fr/hal-03308111 ; 7th International Conference on Data Mining (DTMN 2021) Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT), Sep 2021, Copenhagen, Denmark (2021)
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| 15 |
Additive Linking in L2 French Discourse by German Learners: Syntactic Embedding and Intonation Patterns
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In: EISSN: 2226-471X ; Languages ; https://halshs.archives-ouvertes.fr/halshs-03252581 ; Languages, MDPI, 2021, 6 (1), pp.20. ⟨10.3390/languages6010020⟩ (2021)
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| 16 |
Additive Linking in L2 French Discourse by German Learners: Syntactic Embedding and Intonation Patterns
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In: EISSN: 2226-471X ; Languages ; https://hal.archives-ouvertes.fr/hal-03312978 ; Languages, MDPI, 2021, 6 (1), pp.20. ⟨10.3390/languages6010020⟩ (2021)
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| 17 |
How to make believe: Inquisitivity, veridicality, and evidentiality in belief reports
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| 19 |
Bilingual English-German word embedding models for scientific text ...
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Abstract:
This data set contains three word embedding models, constructed from the same training corpus of English and German parallel scientific texts (abstracts and research project descriptions). All text was pre-processed by language-specific stemming with the Porter stemming algorithm, removing numbers, and lower-casing. The first model is a 1000-dimensional Latent Semantic Analysis model, constructed from concatenating the English and German texts. The input data was a m×n (297,852×923,864) document-term matrix of tf-idf weights. This was processed with truncated SVD. There are two files, the word vectors in file lsa_1000_Vmat.csv (the V* term by latent factors matrix of right singular values) and the dimension weights in lsa_1000_d_weights.csv (the 1000 values of the diagonal of the \(\Sigma\) matrix. lsa_1000_Vmat.csv has two fields, the term and its vector representation in LSA space, separated by a "|" character. The structure looks like this: ... : Funding was provided by the German Federal Ministry of Education and Research [grant numbers 01PQ16004 and 01PQ17001 ...
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Keyword:
Latent Semantic Analysis; Random Indexing; word embedding
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URL: https://dx.doi.org/10.5281/zenodo.4467633
https://zenodo.org/record/4467633
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Bilingual English-German word embedding models for scientific text ...
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