1 |
Between History and Natural Language Processing: Study, Enrichment and Online Publication of French Parliamentary Debates of the Early Third Republic (1881-1899)
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
2 |
Chinese-Uyghur Bilingual Lexicon Extraction Based on Weak Supervision
|
|
|
|
In: Information; Volume 13; Issue 4; Pages: 175 (2022)
|
|
BASE
|
|
Show details
|
|
3 |
Investigating the Efficient Use of Word Embedding with Neural-Topic Models for Interpretable Topics from Short Texts
|
|
|
|
In: Sensors; Volume 22; Issue 3; Pages: 852 (2022)
|
|
BASE
|
|
Show details
|
|
4 |
Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques
|
|
|
|
In: International Journal of Environmental Research and Public Health; Volume 19; Issue 9; Pages: 5705 (2022)
|
|
BASE
|
|
Show details
|
|
5 |
An Enhanced Neural Word Embedding Model for Transfer Learning
|
|
|
|
In: Applied Sciences; Volume 12; Issue 6; Pages: 2848 (2022)
|
|
BASE
|
|
Show details
|
|
6 |
Deep Sentiment Analysis Using CNN-LSTM Architecture of English and Roman Urdu Text Shared in Social Media
|
|
|
|
In: Applied Sciences; Volume 12; Issue 5; Pages: 2694 (2022)
|
|
BASE
|
|
Show details
|
|
7 |
Predicting Academic Performance: Analysis of Students’ Mental Health Condition from Social Media Interactions
|
|
|
|
In: Behavioral Sciences; Volume 12; Issue 4; Pages: 87 (2022)
|
|
BASE
|
|
Show details
|
|
8 |
Vec2Dynamics: A Temporal Word Embedding Approach to Exploring the Dynamics of Scientific Keywords—Machine Learning as a Case Study
|
|
|
|
In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 21 (2022)
|
|
BASE
|
|
Show details
|
|
9 |
Methods, Models and Tools for Improving the Quality of Textual Annotations
|
|
|
|
In: Modelling; Volume 3; Issue 2; Pages: 224-242 (2022)
|
|
BASE
|
|
Show details
|
|
10 |
Creating multi-scripts sentiment analysis lexicons for Algerian, Moroccan and Tunisian dialects
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
11 |
Bilingual English-German word embedding models for scientific text ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
Bilingual English-German word embedding models for scientific text ...
|
|
|
|
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 ...
|
|
Keyword:
Latent Semantic Analysis; Random Indexing; word embedding
|
|
URL: https://dx.doi.org/10.5281/zenodo.4467632 https://zenodo.org/record/4467632
|
|
BASE
|
|
Hide details
|
|
13 |
以《Cofacts 真的假的》資料庫為基礎建立中文科學假訊息之探勘模型 ; Text Mining Model for Detecting Chinese Fake Scientific Messages based on Cofacts Open Data
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Automatic Part-of-Speech Tagging for Security Vulnerability Descriptions ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Automatic Part-of-Speech Tagging for Security Vulnerability Descriptions ...
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Text ranking based on semantic meaning of sentences ; Textrankning baserad på semantisk betydelse hos meningar
|
|
|
|
BASE
|
|
Show details
|
|
19 |
Efficient Estimate of Low-Frequency Words’ Embeddings Based on the Dictionary: A Case Study on Chinese
|
|
|
|
In: Applied Sciences ; Volume 11 ; Issue 22 (2021)
|
|
BASE
|
|
Show details
|
|
20 |
Acoustic Word Embeddings for End-to-End Speech Synthesis
|
|
|
|
In: Applied Sciences ; Volume 11 ; Issue 19 (2021)
|
|
BASE
|
|
Show details
|
|
|
|