MITIE library wrapped in Ruby with FFI
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mrc-osi-bert-quantizationby Huawei-MRC-OSI
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Clinical named entitiy recognition and other NLP services for Fred Hutch Data Science
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An implementation of Lisp/Scheme-like cons in Python
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Ready made code pipeline to process text (via different NLP magix). Deployable online (Heroku and GCloud).
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Rich Search and Discovery for Research Datasets: Building the next generation of scholarly infrastructure
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BiLSTM-IDCNN-CRF model
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Mycroft skill for integrating Todoist.
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My AI study notes
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NLP 入门指南+资料汇总
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Develop QA system to answer questions on stories from Aesop's Fables and the Blogs Corpus using word overlaps, distance measures, dependency parses, WordNet and Word2vec pretrained models.
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A very simple BiLSTM-CRF model for Chinese Named Entity Recognition 中文命名实体识别 (pytorch)
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Word2Vec on Tags of Stackoverflow.
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🖖NLP,TFIDF Calculation module and its result.
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Unsupervised-Pseudo-Multi-sense-Eliminationby ExplorerFreda
Python 3 Version:Current License: Permissive (MIT)
Code implementation of our paper in LREC 2018, Constructing High Quality Sense-specific Corpus and Word Embedding via Unsupervised Elimination of Pseudo Multi-sense
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vn-eng-translation-sentiment-google-apiby khuongav
Python 3 Version:Current License: No License (No License)
License: No License (No License)Building an application with Microservices to translate text from Vietnamese to English and analyze the sentiment of English text with Natural Language API.
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My first and best esolang.
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Conjugate Computation Variational Inference for Correlated Topic Models
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Text classifier
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BERT-Text-Features for Tokenized Transcripts from P2FA.
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A tool to help you understand the trending topics in a specific geographic location.
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A repository for English text normalization.
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Ruby Gem for refine Ruby Core classes.
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Sentiment analysis has been a popular field in natural language processing. Sentiments can be expressed explicitly or implicitly. Most current studies on sentiment analysis focus on the identification of explicit sentiments. However, implicit sentiment analysis has become one of the most difficult tasks in sentiment analysis due to the absence of explicit sentiment words. In this article, we propose a BiLSTM model with multi-polarity orthogonal attention for implicit sentiment analysis. Compared to the traditional single attention model, the difference between the words and the sentiment orientation can be identified by using multi-polarity attention. This difference can be regarded as a significant feature for implicit sen timent analysis. Moreover, an orthogonal restriction mechanism is adopted to ensure that the discrim inatory performance can be maintained during optimization. The experimental results on the SMP2019 implicit sentiment analysis dataset and two explicit sentiment analysis datasets demonstrate that our model more accurately captures the characteristic differences among sentiment polarities.
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GPT-2 style architecture for training language generators for specific tasks. [Production Ready]
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Fast parallel sentence mining from comparable corpora
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Understanding raw query of user and converting into specific eCommerce search query
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Automatic Generation of HTN Domains from VGDL Videogame Descriptions
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A pos-tagging library with Viterbi, CYK and SVO -> XSV translator made as part of my final exam for the Cognitive System course in Department of Computer Science.
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Trie tree implementation with keywords density calcuation functionality
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Set Field Group Location using Field other Groups
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common data structure with python
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Searches the matching sentence for a question within a text [work in progress]
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Small bits that aren't part of anything else.
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Python 3 Version:Current License: No License (No License)
License: No License (No License)Sentiment Analysis of YouTube Comments Using Natural Language Processing
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Intern work in Alibaba: Pretrain BERT for text encoding with knowledge augmentation task.
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Highlighting Annotator for Ranking and Exploration
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Fine-tuning XLNet for Extractive Summarization
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PYME module for PSF extraction
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Approval Sorted Margins, a Condorcet method combined with explicit approval cutoff
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Users can choose to use the BERT's sequence output or word output to complete different NLP tasks, from data loading to final result evaluation, the entire process is fully modular, and Azure pipeline can run it through. Custom layers can be built in the customer_layer.py file according to different NLP tasks. Currently, the multi-label classification and multi-class full-connection layer, lstm layer and bilstm layer have been provided, in the later stage, we can continue to improve tasks such as classification, question and answer, fill in the blanks, and translation.
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mitierby satek
MITIE library wrapped in Ruby with FFI
Ruby 3Updated: 7 y ago License: Permissive (MIT)
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Product-recommendation-systemby akash9182
Python 3Updated: 4 y ago License: Permissive (MIT)
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mrc-osi-bert-quantizationby Huawei-MRC-OSI
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HutchNER_APIby abbottLane
Clinical named entitiy recognition and other NLP services for Fred Hutch Data Science
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python-consby pythological
An implementation of Lisp/Scheme-like cons in Python
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Text2Analysisby previtus
Ready made code pipeline to process text (via different NLP magix). Deployable online (Heroku and GCloud).
Python 3Updated: 4 y ago License: No License (No License)
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rcc_bookby DerwenAI
Rich Search and Discovery for Research Datasets: Building the next generation of scholarly infrastructure
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skill-todoistby gerlachry
Mycroft skill for integrating Todoist.
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NLP-learningby Liu-Feng-deeplearning
NLP 入门指南+资料汇总
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bio-simverbby cambridgeltl
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Question-Answering-Systemby sanswons
Develop QA system to answer questions on stories from Aesop's Fables and the Blogs Corpus using word overlaps, distance measures, dependency parses, WordNet and Word2vec pretrained models.
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Chinese_NERby WallE-Chang
A very simple BiLSTM-CRF model for Chinese Named Entity Recognition 中文命名实体识别 (pytorch)
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Word2VecStackoverflowby ase-sharif
Word2Vec on Tags of Stackoverflow.
Python 3Updated: 7 y ago License: Permissive (MIT)
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FILab_TFIDF_Pythonby BecomeWeasel
🖖NLP,TFIDF Calculation module and its result.
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Unsupervised-Pseudo-Multi-sense-Eliminationby ExplorerFreda
Code implementation of our paper in LREC 2018, Constructing High Quality Sense-specific Corpus and Word Embedding via Unsupervised Elimination of Pseudo Multi-sense
Python 3Updated: 4 y ago License: Permissive (MIT)
3Updated: 4 y ago License: Permissive (MIT)Support
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vn-eng-translation-sentiment-google-apiby khuongav
Building an application with Microservices to translate text from Vietnamese to English and analyze the sentiment of English text with Natural Language API.
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CVI_CTMby Salma-El-Alaoui
Conjugate Computation Variational Inference for Correlated Topic Models
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BERT-Text-Featuresby wxjiao
BERT-Text-Features for Tokenized Transcripts from P2FA.
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GNMT2by Mingyearn
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twitter-geo-toolby BattleoftheCamps
A tool to help you understand the trending topics in a specific geographic location.
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eng_text_normby Joee1995
A repository for English text normalization.
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gorilla_patchby AlexWayfer
Ruby Gem for refine Ruby Core classes.
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bilstm_mpoaby SXU-CICI
Sentiment analysis has been a popular field in natural language processing. Sentiments can be expressed explicitly or implicitly. Most current studies on sentiment analysis focus on the identification of explicit sentiments. However, implicit sentiment analysis has become one of the most difficult tasks in sentiment analysis due to the absence of explicit sentiment words. In this article, we propose a BiLSTM model with multi-polarity orthogonal attention for implicit sentiment analysis. Compared to the traditional single attention model, the difference between the words and the sentiment orientation can be identified by using multi-polarity attention. This difference can be regarded as a significant feature for implicit sen timent analysis. Moreover, an orthogonal restriction mechanism is adopted to ensure that the discrim inatory performance can be maintained during optimization. The experimental results on the SMP2019 implicit sentiment analysis dataset and two explicit sentiment analysis datasets demonstrate that our model more accurately captures the characteristic differences among sentiment polarities.
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GPT-2-Completeby yashbonde
GPT-2 style architecture for training language generators for specific tasks. [Production Ready]
Python 3Updated: 4 y ago License: Permissive (MIT)
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LEXACCby accurat-toolkit
Fast parallel sentence mining from comparable corpora
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Query-Understandingby DataEngg
Understanding raw query of user and converting into specific eCommerce search query
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VGDL-to-HTN-Parserby IgnacioVellido
Automatic Generation of HTN Domains from VGDL Videogame Descriptions
Java 3Updated: 3 y ago License: No License (No License)
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cognitive-system-postaggerby made2591
A pos-tagging library with Viterbi, CYK and SVO -> XSV translator made as part of my final exam for the Cognitive System course in Department of Computer Science.
Python 3Updated: 4 y ago License: Strong Copyleft (GPL-3.0)
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density-trieby klesh
Trie tree implementation with keywords density calcuation functionality
JavaScript 3Updated: 5 y ago License: Permissive (MIT)
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acf-custom-field-locations-ruleby vitalijm
Set Field Group Location using Field other Groups
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Data-Structureby omidekz
common data structure with python
Python 3Updated: 4 y ago License: Weak Copyleft (LGPL-3.0)
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nlpby Wangkaixinlove
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CodeSwitch-Redditby ellarabi
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SentenceSearchby TechnicPlay
Searches the matching sentence for a question within a text [work in progress]
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textwranglerby mattmurray
Python 3Updated: 4 y ago License: Permissive (MIT)
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small-stuffby seth-shaw-unlv
Small bits that aren't part of anything else.
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YouTube-Video-Review-Analysis-Using-Natural-Language-Processingby lhamu
Sentiment Analysis of YouTube Comments Using Natural Language Processing
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knowledge_augmented_bertby CristinaMa0917
Intern work in Alibaba: Pretrain BERT for text encoding with knowledge augmentation task.
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HAREby CC-RMD-EpiBio
Highlighting Annotator for Ranking and Exploration
Python 3Updated: 4 y ago License: Proprietary (Proprietary)
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xlsumby jihun-hong
Fine-tuning XLNet for Extractive Summarization
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pyme-psf-extractionby bewersdorflab
PYME module for PSF extraction
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COMP90051-2019S2-Project1by HanxunH
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nlpyby 353solutions
Python 3Updated: 4 y ago License: Permissive (MIT)
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enwordsby enwords
Ruby 3Updated: 3 y ago License: Permissive (MIT)
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approval-sorted-marginsby dodecatheon
Approval Sorted Margins, a Condorcet method combined with explicit approval cutoff
Python 3Updated: 4 y ago License: Strong Copyleft (GPL-3.0)
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bert_for_nlp_moduleby lMisli
Users can choose to use the BERT's sequence output or word output to complete different NLP tasks, from data loading to final result evaluation, the entire process is fully modular, and Azure pipeline can run it through. Custom layers can be built in the customer_layer.py file according to different NLP tasks. Currently, the multi-label classification and multi-class full-connection layer, lstm layer and bilstm layer have been provided, in the later stage, we can continue to improve tasks such as classification, question and answer, fill in the blanks, and translation.
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