Natural Language Processing Libraries - Page 395

An AI application web developed by python using Django Web Framework

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A collection of Python scripts for creating topic models for Serendip. Uses Mallet and Gensim

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Tool for Text Mining

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Dead simple conditional build tool

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System for suggesting new concepts for vocabularies

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Mã nguồn cho lộ trình hoc python backend - vimentor.com

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Neural morphological tagging with Modern Hebrew (Keras)

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Bitirme Tezi-Derin öğrenme tabanlı ses-text çevirimi gerçekleştirilerek metin işleme ile textler 5 kategoride sınıflandırılıp yeni girilen ya da kaydedilen textin kategorisini bulmayı sağlayan uygulamadır.

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A novel approach to Passage Ranking Problem using BERT model.

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A simple token generation application to demonstrate three ways of token generation

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An intelligent tool to help find best job for you

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A toolkit for comparative evaluation of outputs of two or more NLP systems

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ETIP research repo about nested named entity recognition

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Proposing model for embedding sentences into vectors that capture both semantic and syntactical information in a computationally effective manner.

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A curated list of papers and experiments in the field of Natural Language Processing (NLP)

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Text to Image Generation

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Server for the Year 2 university group project

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Flask based application to extract entities from text using Spacy.

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Python package containing functions that may be useful for word research.

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Random Semantically-Unpredictable Sentence Generator with NLTK

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BERT model, tested for CNTK and Tensorflow

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WASP: Web Audio Sound Programming

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Chinese nlp tasks finetuning with google bert

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BI-DIRECTIONAL ATTENTION FLOW FOR MACHINE COMPREHENSION

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Automatic Part-of-Speech Tagging (labelling words as noun, verb, adjective, etc.) using Hidden Markov Model, trained on Brown Corpus

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Applies Canonical Correlation Analysis to the task of visual dialogue.

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English to hindi NMT using attention written in Python using Keras

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Fictional language toolbox in Java. Generate random languages, mix them together, generate text in them, reversibly translate English text to a fictional language, etc.

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A small esoteric programming language developed for CMSI 488 - Language Translation and Implementation at Loyola Marymount University.

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Part-of-speech-specific implementation of Melamud et al. 2015 lexical substitution model

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Induction 2019 task

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DATA This assignment is about part-of-speech tagging on Twitter data. The data is located in ./data directory with a train and dev split. The test data is also included, but with false POS tags on purpose. You will develop and tune your models only using train and dev sets, and will generate predictions for the test data once you are done developing. The accuracy will be computed by TA with the goldstandard labels. This data set contains tweets annotated with their universal parts-of-speech tags, with 379 tweets for training and 112 for dev, and 12 possible part-of-speech labels. The test corpus will contain 295 tweets. The format of the data files is pretty straight forward. It contains a line for each token (with its label separated by a whitespace), and with sentences separated with empty line. See the below example an example, and examine the text files yourself (always a good idea). @paulwalk X It PRON 's VERB the DET view NOUN from ADP where ADV I PRON 'm VERB living VERB for ADP two NUM weeks NOUN . . Empire NOUN State NOUN Building NOUN = X ESB NOUN . . Pretty ADV bad ADJ storm NOUN here ADV last ADJ evening NOUN Files data.py: The primary entry point that reads the data, and trains and evaluates the tagger implementation. usage: python data.py [-h] [-m MODEL] [--test] optional arguments: -h, --help show this help message and exit -m MODEL, --model MODEL 'LR'/'lr' for logistic regression tagger 'CRF'/'crf' for conditional random field tagger --test Make predictions for test dataset tagger.py: Code for two sequence taggers, logistic regression and CRF. Both of these taggers rely on 'feats.py' and 'feat_gen.py' to compute the features for each token. The CRF tagger also relies on 'viterbi.py' to decode (which is currently incorrect), and on 'struct_perceptron.py' for the training algorithm (which also needs Viterbi to be working). feats.py & 'feat_gen.py: Code to compute, index, and maintain the token features. The primary purpose of 'feats.py' is to map the boolean features computed in 'feats_gen.py' to integers, and do the reverse mapping (if you want to know the name of a feature from its index). 'feats_gen.py' is used to compute the features of a token in a sentence, which you will be extending. The method there returns the computed features for a token as a list of string (so does not have to worry about indices, etc.). 'struct_perceptron.py': A direct port (with negligible changes) of the structured perceptron trainer from the 'pystruct' project. Only used for the CRF tagger. The description of the various hyperparameters of the trainer are available here, but you should change them from the constructor in 'tagger.py'. 'viterbi.py' (and 'viterbi_test.py'): General purpose interface to a sequence Viterbi decoder in 'viterbi.py', which currently has an incorrect implementation. Once you have implemented the Viterbi implementation, running 'python viterbi_test.py' should result in succesful execution without any exceptions. conlleval.pl: This is the official evaluation script for the CONLL evaluation. Although it computes the same metrics as the python code does, it supports a bunch of features, such as: (a) Latex formatted tables, by using -l, (b) BIO annotation by default, turned off using -r. In particular, when evaluating the output prediction files (~.pred) for POS tagging, $ ./conlleval.pl -r -d \t < ./predictions/twitter_dev.pos.pred

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OCR with Tesseract, Deep Learning, Python, Apache NiFi, ...

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We propose a model based on the bag of mixed-gram terms to deal with sentiment classification task and extracting sentimental features. We obtain a very short-dimensional vector to represent sentiment and use the sentimental representations to complete the task of sentiment classification. Furthermore, since the sentimental representa- tions and some traditional word vectors have complementary advantages, we combine the sentimental representations with convolutional neural networks that use other word vectors and ultimately implement a more efficient classifier. Experimental results show that this combination method can use static word vectors to deal with sentimental classification tasks well, and the sentimental representations here play the role of fine-turned word vectors in previous research.

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Java and Python scripts used to prepare the training data for different NLU Services, query NLU Services and evaluate the performances

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The Java Package of Key Word Extractor.

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Language pipelines

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A new library for building PSL models in Python

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SENG 401 Winter 2020 Term project

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Using OOP to implement chess

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[Course] A pinyin inputer based on n-gram algorithm. (AI2017 Homework1)

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A sentiment analysis library for tweet objects and strings

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openAI gpt-2

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Dead — please use the new Origami Registry (https://github.com/Financial-Times/origami-registry-ui)

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NLP100knock challenge in Ruby and Python3

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Clinical Acoustics & Text Processing (CATPro) provides tools for monitoring and predicting mental health disorders using acoustic signal processing and natural language processing.

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