65 skills found · Page 1 of 3
kevinlu1248 / PyatePYthon Automated Term Extraction
gionanide / Speech Signal Processing And ClassificationFront-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].
yangheng95 / LCF ATEPCcodes for paper A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction
Sshanu / Relation Classification Using Bidirectional LSTM TreeTensorFlow Implementation of the paper "End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures" and "Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths" for classifying relations
abhishek305 / PyBot A ChatBot For Answering Python Queries Using NLPPybot can change the way learners try to learn python programming language in a more interactive way. This chatbot will try to solve or provide answer to almost every python related issues or queries that the user is asking for. We are implementing NLP for improving the efficiency of the chatbot. We will include voice feature for more interactivity to the user. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation. NLTK has been called “a wonderful tool for teaching and working in, computational linguistics using Python,” and “an amazing library to play with natural language.The main issue with text data is that it is all in text format (strings). However, the Machine learning algorithms need some sort of numerical feature vector in order to perform the task. So before we start with any NLP project we need to pre-process it to make it ideal for working. Converting the entire text into uppercase or lowercase, so that the algorithm does not treat the same words in different cases as different Tokenization is just the term used to describe the process of converting the normal text strings into a list of tokens i.e words that we actually want. Sentence tokenizer can be used to find the list of sentences and Word tokenizer can be used to find the list of words in strings.Removing Noise i.e everything that isn’t in a standard number or letter.Removing Stop words. Sometimes, some extremely common words which would appear to be of little value in helping select documents matching a user need are excluded from the vocabulary entirely. These words are called stop words.Stemming is the process of reducing inflected (or sometimes derived) words to their stem, base or root form — generally a written word form. Example if we were to stem the following words: “Stems”, “Stemming”, “Stemmed”, “and Stemtization”, the result would be a single word “stem”. A slight variant of stemming is lemmatization. The major difference between these is, that, stemming can often create non-existent words, whereas lemmas are actual words. So, your root stem, meaning the word you end up with, is not something you can just look up in a dictionary, but you can look up a lemma. Examples of Lemmatization are that “run” is a base form for words like “running” or “ran” or that the word “better” and “good” are in the same lemma so they are considered the same.
igor-shevchenko / RutermextractTerm extraction for Russian language
ziqizhang / JateJATE - Just Automatic Term Extraction (in Python)
ArrowLuo / DOERThe implementation of ACL 2019 paper DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction
lixin4ever / HASTAspect Term Extraction with History Attention and Selective Transformation (IJCAI 2018)
joshuaswarren / Openclaw EngramLocal-first memory plugin for OpenClaw AI agents. LLM-powered extraction, plain markdown storage, hybrid search via QMD. Gives agents persistent long-term memory across conversations.
nicolezattarin / BERT Aspect Based Sentiment AnalysisClass for Aspect-term extraction and Aspect-based sentiment analysis with BERT and Adapters
zhijing-jin / Pytorch RelationExtraction AttentionBiLSTMPytorch Implementation of Attention-Based BiLSTM for Relation Extraction ("Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification" ACL 2016 http://www.aclweb.org/anthology/P16-2034)
termsuite / Termsuite CoreA Java UIMA-based toolbox for multilingual and efficient terminology extraction an multilingual term alignment
HKUST-KnowComp / RINANTENeural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision
insight-centre / SaffronSaffron 4 - Text Analysis and Insight Tool
alexrabarts / Term ExtractionA Ruby library that provides access to term extraction APIs such as Yahoo! Term Extraction API and Zemanta.
zamaex96 / Hybrid CNN LSTM With Spatial AttentionThis documents the training and evaluation of a Hybrid CNN-LSTM Attention model for time series classification in a dataset. The model combines convolutional neural networks (CNNs) for feature extraction, long short-term memory (LSTM) networks for sequential modeling, and attention mechanisms to focus on important parts of the sequence.
rattle / Term Extractterm extraction gem
1tangerine1day / Aspect Term Extraction And AnalysisAspect Term Extraction and Aspect-based Sentiment Analysis on SemEval-2014 task4
AylaRT / ACTERACTER is a manually annotated dataset for term extraction, covering 3 languages (English, French, and Dutch), and 4 domains (corruption, dressage, heart failure, and wind energy).
