MariRazno

BERT_classifier

Multiclass text classifier based on BERT architecture.

BERT_monoware

BERT service implementation

BERT_NER_DIA

This repository contains script that extracts ICD codes and diagnoses from medical reports

ChatBot

Collocations_check

We say "make a mistake", but "do a favour"; we say "big surprise", but "great anger"; we say "highly unlikely", but "seriously wrong". Words collocate in interesting and unpredictable ways. Moreover, word collocations can tell us more about the meaning of the word. Your task is to research how verbs from the same synset collocate with adverbs. For example, we usually "love somebody dearly", "honor somebody highly", and "admire somebody greatly". The task: collect more synonyms for this synset: "say", "tell", "speak", "claim", "communicate" write a function that finds a verb from the synset in the sentence and collects all adverbs that this verb governs; consider only adverbs that end with "-ly" write a program that collects all verbs and their adverbs in the blog corpus the output of the program should be ten most frequent adverbs that collocate with the verb

Data_Scraping

Scraping text data for analysis from the web-sites

Gender_Classification

NaiveBayesClassifier

Generate-a-Song

2-gramm based song generator

MariRazno/BERT_classifier

Multiclass text classifier based on BERT architecture.

MariRazno/BERT_monoware

BERT service implementation

MariRazno/BERT_NER_DIA

This repository contains script that extracts ICD codes and diagnoses from medical reports

MariRazno/ChatBot

MariRazno/Collocations_check

We say "make a mistake", but "do a favour"; we say "big surprise", but "great anger"; we say "highly unlikely", but "seriously wrong". Words collocate in interesting and unpredictable ways. Moreover, word collocations can tell us more about the meaning of the word. Your task is to research how verbs from the same synset collocate with adverbs. For example, we usually "love somebody dearly", "honor somebody highly", and "admire somebody greatly". The task: collect more synonyms for this synset: "say", "tell", "speak", "claim", "communicate" write a function that finds a verb from the synset in the sentence and collects all adverbs that this verb governs; consider only adverbs that end with "-ly" write a program that collects all verbs and their adverbs in the blog corpus the output of the program should be ten most frequent adverbs that collocate with the verb

MariRazno/Data_Scraping

Scraping text data for analysis from the web-sites

MariRazno/Gender_Classification

NaiveBayesClassifier

MariRazno/Generate-a-Song

2-gramm based song generator

MariRazno/Head_lines_Correction

The Associated Press Stylebook is a style guide widely used among American journalists. It enforces the following rules for capitalization of news headlines: Capitalize words with 4 or more letters. Capitalize the first and the last word in the headline. Capitalize nouns, pronouns, adjectives, verbs, adverbs, numerals, and subordinating conjunctions. Lowercase all other parts of speech: articles, coordinating conjunctions, prepositions, particles, interjections.

MariRazno/Law-terms-extraction-using-SpaCy-rules

Highlighting terms (nouns and predicates) and thematic modeling using SpaCy (for Russian). Calculating TF-IDF for the relevant terms extraction (sk-learn)

MariRazno/ML-Star-rate-prediction

Predicting the star rate to the users` comments according using Supervised ML algorithm.

MariRazno/Pymorphy_textAnalyzer

Analization of ukrainian and russian texts using Pymorphy

MariRazno/Python-Gematria

Read about Gematria, a method for assigning numbers to words and for mapping between words having the same number (http://en.wikipedia.org/wiki/Gematria). There are different views on how to count Gematria. Your script will incorporate two different scores. Write a function count_gematria(word, option) that sums the numerical values of the letters of a word using letter_values_1 if option is 1 and letter_values_2 if option is 2:

MariRazno/Python-Zen

Write a function real_zen(input_file) that reads zen.txt as input_file and prints "The Zen of Python" in the following format: the title, "by" + the author and then the Zen itself line by line, starting with the line number. You should ignore the comments: The Zen of Python by Tim Peters 1. Beautiful is better than ugly. 2. Explicit is better than implicit. ... 19. Namespaces are one honking great idea -- let's do more of those! Your function should print 2 lines with the title and the author and then 19 more lines with the wisdom about Python, starting with the numbers from 1 to 19. Read all necessary information from the file.

MariRazno/SpaCy-rules-for-Key-Words-extraction

SpaCy Part-of-Speech tagging model that can identify "Profiles", "Categories", "Goals", "Measures", "Actions" from text data. The grammar-based rules using POS tagging and dependency parsing upon for better accuracy.

MariRazno/stopwords-ru

Russian stopwords collection

MariRazno/Symantic_similarity

symantic_similarity, Reznik similarity

MariRazno/Text_Exploring

1)Text segmentation, 2)Tokenization, 3)Building concordance, 4)Steming, 5)Lematization

MariRazno/text_service

MariRazno/The_Most_Popular_N-Gramms

Finding the most popular n-gramms based on corpus words or corpus sentences

MariRazno/VSM

MariRazno/Word_Frequences

Calculating word frequences using NLTK methods

MariRazno/WordDistribution

Word distribution in NLTK gutenberg

MariRazno/WordNet-word-description

Giving all the information about word from WordNet