I'm trying to train a model that is able to classify short texts (200-600 words per text). I got a training set with their corresponding labels, and a text might have one or more labels. My first approach was to use TF-IDF with Naive Bayes but it wasn't performing very well. But...
In most cases, RNNs are the best to classify texts. The use of pretrained embeddings makes the model efficient. Also, not the least, you can give Bayes text classification a try. It has been super useful in spam classification. Tip: You can implement the above methods with each other, c...
In this work, we explore the use of techniques from natural language processing to classify file fragments. We take a supervised learning approach, based on the use of support vector machines combined with the bag-of-words model, where text documents are represented as unordered bags of words....
With Transformers, we can supply our audio analyzer with the ability to classify text, recognize named entities, answer questions, summarize text, translate, and generate text. Most notably, it also providesspeech recognitionandaudio classification capabilities.Basically, we get an API that taps into ...
Natural Language Processing (NLP) is used to classify text domains. In all applications where data is critical, such as News media, educational institutes, business organizations, research organizations, scientific, technology companies, and government organizations maintaining huge every day generated data...
A Study of the Performance of Embedding Methods for Arabic Short-Text Sentiment Analysis Using Deep Learning Approaches Sentiment analysis aims to classify a text according to sentimental polarities of people's opinions, such as positive, negative, or neutral. While most of the studies focus on el...
(NLP) tasks including Statistical Ma- chine Translation (SMT) (Och and Ney, 2003) and Cross-Language Information Retrieval (Balles- teros and Croft, 1997). However, manually cre- ating and updating such resources is an expensive process. In addition to this, new terms are con- stantly ...
Our research explores the use of natural language processing (NLP) methods to automatically classify entities for the purpose of knowledge graph population... T Jiang,S Vinogradova,N Stringham,... 被引量: 0发表: 2023年 Depression Detection From Social Media Textual Data Using Natural Language Proc...
two types of deeprecurrent networksincludingRNNand GRU were used.1Text is a sequence of words and each word is dependent on the words that come before it. So,RNNand GRU are perfect for text processing. For example, if we want to predict the next word or classify a text, we should know...
classifier = nltk.NaiveBayesClassifier.train(train_set)printnltk.classify.accuracy(classifier, test_set) classifier.show_most_informative_features(5) This returns: File"test.py", line38,in<module>forwinmovie_reviews.words() File"/usr/local/lib/python2.6/dist-packages/nltk/corpus/reader/plaintext...