Related APIs in Category: Tagging
SYSTRAN.io platform is a collection of APIs for Translation, Multilingual Dictionary lookups, Natural Language Processing (Entity recognition, Morphological analysis, Part of Speech tagging, Language Identification...) and Text Extraction (from documents, audio files or images).
TextAnalysis API provides customized Text Analysis,Text Mining and Text Processing Services like Text Summarization, Language Detection, Text Classification, Sentiment Analysis, Word Tokenize, Part-of-Speech(POS) Tagging, Named Entity Recognition(NER), Stemmer, Lemmatizer, Chunker, Parser, Key Phrase Extraction(Noun Phrase Extraction), Sentence Segmentation(Sentence Boundary Detection), Grammar Checker and other Text Analysis Tasks. It stands on the giant shoulders of NLP Tools, such as NLTK, TextBlob, Pattern, MBSP and etc. You can test the services on our demo website TextAnalysisOnline and use the TextAnalysis API on Mashape. If you have any questions or want any customized text analysis services, you can contact us by email: [email protected]
This API provides text analysis for Tone, Sentiment, Summarization, Personality Analysis, and more. This API can be used for: Part of Speech Tagging Named Entity Recognition Sentence Disambiguation KeyWord Extraction Summarization and Sentence Significance Sentiment Analysis Alliteration Detection Word Sense Disambiguation Clustering Logistic Regression Scoring Prominence Tagging for Latent Semantic Indexing Tagging for Singular Value Decomposition Phonetic Decomposition Reading Difficulty Modeling Technical Difficulty Modeling Spelling Correction String Comparison and Plagiarism Detection Author Profiling Psychographic Modeling Fact and Statistic Extraction Ism Extraction Character Language Modeling It is also useful in the creation of ChatBots, SearchEngines, and KnolExtraction for Automated Documentation.
This API allows you to extract most relevant terms from a text. It is not, like many others, a basic TF-IDF analysis. It compare the text against a very large language model, it uses a probabilistic model to identify candidates, it supports multi-words terms and not only single words. It uses part of speech tagging to clean up the results". In short it is probably the most advanced term extraction out there.
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