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| name | Meta | Corpora | Text processing | Annotation | ML | visualization | Multilanguage | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| github | Splitting | Parsing | Coreference resolution | Word inflection | Pattern Matching | X-grams | Spelling correction | WordNet | stopwords | statistics | Tagger | NER | Sentiment analysis | Classification | Clustering | Topic Modelling | Vectorization (including embeddings) | Translation | Language Identification | |||
| TextBlob |  |
NLTK-tokenizers | based on `pattern` | singularize, pluralize, lemmatize | based on `pattern` | integration | Word and phrase frequencies | 1) POS based on `pattern` 2) POS based on NLTK‘s TreeBank tagger 3) NP based on Shlomi Babluki’s implementation 4) NP uses the CoNLL 2000 corpus to train a tagger |
PatternAnalyzer (based on the `pattern`) NaiveBayesAnalyzer (an NLTK classifier trained on a movie reviews corpus) |
Naive Bayes, Decision Tree | powered by the Google Translate API | powered by the Google Translate API | ||||||||||
| textacy |  |
|||||||||||||||||||||
| pattern |  |
contains API's (Google, Gmail, Bing, Twitter, Facebook, Wikipedia, Wiktionary, DBPedia, Flickr, ...), a robust HTML DOM parser and a web crawler. | yes | by POS-tags | POS (NN, VB, JJ, DT) Chunks (NP) |
Naive Bayes, Perceptron, k-NN, SVM | k-means, hierarchical | LSA | td, df, idf, tf-idf, cosine similarity, infogain | graph.js on canvas | ||||||||||||
| pymorphy2 |  |
for Russian: singularize, pluralize, lemmatize | for Russian: morphology | |||||||||||||||||||
| PyNLPl |  |
|||||||||||||||||||||
| glove |  |
glove | ||||||||||||||||||||
| MITIE |  |
tokenizer | - "bunch of different types of binary relation detector" | yes | yes | pretrained word_feature_extractor | ||||||||||||||||
| gensim |  |
tf, tf-idf, word2vec | ||||||||||||||||||||
| NLTK |  |
n-grams | ||||||||||||||||||||
| stopwords |  |
|||||||||||||||||||||
| colibri-core |  |
n-grams, skipgrams, flexgrams | ||||||||||||||||||||
| spaCy |  |
- Non-destructive tokenization - Syntax-driven sentence segmentation |
"fast and accurate syntactic dependency parser" | Rule-based matching | English and German tagging models with rule-based morphology | > 10 built-in types Stand-off format and token tags training |
||||||||||||||||
| fastText |  |
yes | skipgram, cbow | |||||||||||||||||||
| SyntaxNet |  |
tokenizer | "transition-based dependency parser" | POS | ||||||||||||||||||
| langid |  |
pre-trained for 97 languages | ||||||||||||||||||||
| CoreNLP | |
tokenizer | yes | "multi-pass sieve coreference resolution" | lemmatize | Pattern-based entity extraction | POS | - NER with "CRF sequence models" - "Open information extraction" |
||||||||||||||
| bllip-parser |  |
"8 known unified parsing models", including models for web, news, PubMed texts | ||||||||||||||||||||
| MBSP | |
Regex-based segmentation Regex-bases tokenization |
MBLEM-based lemmatization | POS (NN, JJ, VB) Chunks (NP, VP) Relations (SBJ, OBJ) |
||||||||||||||||||
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