We used a bag-of-words approach for feature-set generation and compared machine learning algorithms using support vector machines (SVMs), nave Bayes (NB), and bagged classification and regression trees (CART) fo
learning algorithms (Support Vector Machines, Sequential Minimal Optimization) to assess various variance factors that affect AES. They demonstrated the effectiveness of this approach by analyzing student answers to prompts, as stored in two standard datasets. Motivated by the lack of explainability of A...
nlpnatural-language-processingtext-miningnlp-machine-learningtext-mining-analysisnlp-deep-learningtext-mining-for-beginnerstext-mining-in-pythontext-mining-processtext-mining-with-pythontext-mining-pythontext-mining-pipelinenatural-language-processing-algorithmsnatural-language-processing-statisticsnatural-language-...
randomForestSRC R Implement supervised machine learning algorithms (Random Forest); including multilabel classification https://cran.r-project.org/web/packages/randomForestSRC/ LiblineaR R Implement supervised machine learning algorithms (Logistic regression, linear SVM); including multi-class classification...
From this study we were able to conclude that the best Machine Learning algorithms to achieve a good performance are the Ensemble of Classifiers (a method that combines the individual decisions of a set of classifiers through majority or voting). In terms of the accuracy of the results, the ...
As part of that service, Europe PMC Labs provides a platform for prototype services that use text mining in innovative ways, providing semantically guided search. This chapter describes the EvidenceFinder application, which provides passage retrieval of facts by generating questions from basic search ...
Key steps in text mining applications In the past, NLP algorithms were primarily based on statistical or rules-based models that provided direction on what to look for in data sets. In the mid-2010s, though, deep learning models that work in a less supervised way emerged as an alternative ...
Machine learning (ML) This approach involves training a model to identify the sentiment of a piece of text based on a set of labeled training data. These models can be trained using a wide range of ML algorithms, including decision trees, support vector machines (SVMs), and neural networks....
nlplanguagemachine-learningnatural-language-processingtext-miningawesomedeep-learningawesome-list UpdatedNov 13, 2023 adbar/trafilatura Sponsor Star4.2k Python & Command-line tool to gather text and metadata on the Web: Crawling, scraping, extraction, output as CSV, JSON, HTML, MD, TXT, XML ...
Table 3 shows the results of the various NER methods, i.e., dictionary-based (MetaMap), rule-based (Rules) and the three machine learning algorithms (MEMM, HMM and CRF), when applied separately to the different text types in the corpus (i.e., discharge summaries and literature articles...