Questions for Natural Language Processing

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www.YoYoBrain.com - Accelerators for Memory and Learning Questions for Natural Language Processing Category: Default - (33 questions) NLTK: function to recognize named entities nltk.ne_chunk() like person, place, etc NLTK: break words into parts of speech nltk.pos_tag() NLTK: latent semantic analysis theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text NLTK: class to perform word stemming PorterStemmer NLTK: how to tokenize strings nltk.tokenize.word_tokenize() NLTK: class for lemmatizing WordNextLemmatizer NLTK: how to tokenize sentences nltk.tokenize.sent_tokenizer() NLTK - tokenize a string or document based regexp_tokenize() on a regular expression NLTK - special class for tweet tokenization TweetTokenizer NLTK - class that has stop words nltk.corpus.stopwords NLP - library gensim popular open-source NLP library Gensim: how to create a gensim dictionary from gensim.corpora.dictionary import Dictionary dictionary = Dictionary(tokenized_docs) Gensim: how to create a corpus with corpus = [dictionary.doc2bow(doc) for doc in dictionary and tokenized_docs tokenized_docs] Gensim: dictionary attribute returns ids from dictionary.token2id the text Gensim: how to get the id for "computer" dictionary.token2id.get("computer") from dictionary Gensim: how to fit a Tf-idf using corpus from gensim.models.tfidfmodel import TfidfModel tfidf = TfidfModel(corpus) NLP - SpaCy library NLP library with focus on creating NLP pipelines to generate models and corpora SpaCy: how to load pre-trained English import spacy model nlp - spacy.load('en') SpaCy: how to load text into spacy doc = nlp(text) model nlp  SpaCy: how to see the entities from loaded doc.ents document doc=nlp(text) NLP: library to do multilingual named entity polyglot recognition Polyglot: how to do named entity recognition from polyglot.text import Text ptext = Text(text) NLP: Flesch reading ease rank of ease of English text for reading - higher score means easier to read Depends on:- average sentence length- number of syllables in a word NLP: Gunning fog index score of readability of English - lower is easier to read - average sentence length- percentage of complex words (3 or more syllables) NLP: library for calculating different reading textatistic difficulty scores NLP: score text reading level using textatistic from textatistic import library Textatisticreadability_scores = Textatistic(text).scores SpaCy: how to generate a list of tokens from tokens = [token.text for token in doc] tokenized doc SpaCy: generate a list of lemmas from lemmas = [token.lemma_ for the token in tokenized doc doc] SpaCy: list of stopwords spacy.en.stop_words.STOP_WORDS SpaCy: how to generate a list of tokenized pos = [ (token.text, token.pos_) for token in and POS tags from tokenized doc doc] SpaCy: load a list of text and named entities ne = [(ent.text, ent.label_) for ent in tuples from tokenized doc documents] SpaCy: how to generate a list of word embedding = [token.vector for token in doc] embeddings from tokenized doc SpaCy: how to calculate word embedding token1.similarity(token2) similarity between 2 tokens token1 and doc1.similarity(doc2) token2 or 2 docs doc1 and doc2.

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15 Things You Should Know About Spacy
15 THINGS YOU SHOULD KNOW ABOUT SPACY ALEXANDER CS HENDORF @ EUROPYTHON 2020 0 -preface- Natural Language Processing Natural Language Processing (NLP): A Unstructured Data Avalanche - Est. 20% of all data - Structured Data - Data in databases, format-xyz Different Kinds of Data - Est. 80% of all data, requires extensive pre-processing and different methods for analysis - Unstructured Data - Verbal, text, sign- communication - Pictures, movies, x-rays - Pink noise, singularities Some Applications of NLP - Dialogue systems (Chatbots) - Machine Translation - Sentiment Analysis - Speech-to-text and vice versa - Spelling / grammar checking - Text completion Many Smart Things in Text Data! Rule-Based Exploratory / Statistical NLP Use Cases � Alexander C. S. Hendorf [email protected] -Partner & Principal Consultant Data Science & AI @hendorf Consulting and building AI & Data Science for enterprises. -Python Software Foundation Fellow, Python Softwareverband chair, Emeritus EuroPython organizer, Currently Program Chair of EuroSciPy, PyConDE & PyData Berlin, PyData community organizer -Speaker Europe & USA MongoDB World New York / San José, PyCons, CEBIT Developer World, BI Forum, IT-Tage FFM, PyData London, Berlin, PyParis,… here! NLP Alchemy Toolset 1 - What is spaCy? - Stable Open-source library for NLP: - Supports over 55+ languages - Comes with many pretrained language models - Designed for production usage - Advantages: - Fast and efficient - Relatively intuitive - Simple deep learning implementation - Out-of-the-box support for: - Named entity recognition - Part-of-speech (POS) tagging - Labelled dependency parsing - … 2 – Building Blocks of spaCy 2 – Building Blocks I. - Tokenization Segmenting text into words, punctuations marks - POS (Part-of-speech tagging) cat -> noun, scratched -> verb - Lemmatization cats -> cat, scratched -> scratch - Sentence Boundary Detection Hello, Mrs. Poppins! - NER (Named Entity Recognition) Marry Poppins -> person, Apple -> company - Serialization Saving NLP documents 2 – Building Blocks II.
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Arxiv:2002.06235V1
Semantic Relatedness and Taxonomic Word Embeddings Magdalena Kacmajor John D. Kelleher Innovation Exchange ADAPT Research Centre IBM Ireland Technological University Dublin [email protected] [email protected] Filip Klubickaˇ Alfredo Maldonado ADAPT Research Centre Underwriters Laboratories Technological University Dublin [email protected] [email protected] Abstract This paper1 connects a series of papers dealing with taxonomic word embeddings. It begins by noting that there are different types of semantic relatedness and that different lexical representa- tions encode different forms of relatedness. A particularly important distinction within semantic relatedness is that of thematic versus taxonomic relatedness. Next, we present a number of ex- periments that analyse taxonomic embeddings that have been trained on a synthetic corpus that has been generated via a random walk over a taxonomy. These experiments demonstrate how the properties of the synthetic corpus, such as the percentage of rare words, are affected by the shape of the knowledge graph the corpus is generated from. Finally, we explore the interactions between the relative sizes of natural and synthetic corpora on the performance of embeddings when taxonomic and thematic embeddings are combined. 1 Introduction Deep learning has revolutionised natural language processing over the last decade. A key enabler of deep learning for natural language processing has been the development of word embeddings. One reason for this is that deep learning intrinsically involves the use of neural network models and these models only work with numeric inputs. Consequently, applying deep learning to natural language processing ﬁrst requires developing a numeric representation for words. Word embeddings provide a way of creating numeric representations that have proven to have a number of advantages over traditional numeric rep- resentations of language.
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Combining Word Embeddings with Semantic Resources
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Probabilistic Topic Modelling with Semantic Graph
Probabilistic Topic Modelling with Semantic Graph B Long Chen( ), Joemon M. Jose, Haitao Yu, Fajie Yuan, and Huaizhi Zhang School of Computing Science, University of Glasgow, Sir Alwyns Building, Glasgow, UK [email protected] Abstract. In this paper we propose a novel framework, topic model with semantic graph (TMSG), which couples topic model with the rich knowledge from DBpedia. To begin with, we extract the disambiguated entities from the document collection using a document entity linking system, i.e., DBpedia Spotlight, from which two types of entity graphs are created from DBpedia to capture local and global contextual knowl- edge, respectively. Given the semantic graph representation of the docu- ments, we propagate the inherent topic-document distribution with the disambiguated entities of the semantic graphs. Experiments conducted on two real-world datasets show that TMSG can signiﬁcantly outperform the state-of-the-art techniques, namely, author-topic Model (ATM) and topic model with biased propagation (TMBP). Keywords: Topic model · Semantic graph · DBpedia 1 Introduction Topic models, such as Probabilistic Latent Semantic Analysis (PLSA) [7]and Latent Dirichlet Analysis (LDA) [2], have been remarkably successful in ana- lyzing textual content. Speciﬁcally, each document in a document collection is represented as random mixtures over latent topics, where each topic is character- ized by a distribution over words. Such a paradigm is widely applied in various areas of text mining. In view of the fact that the information used by these mod- els are limited to document collection itself, some recent progress have been made on incorporating external resources, such as time [8], geographic location [12], and authorship [15], into topic models.
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Employee Matching Using Machine Learning Methods
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Matrix Decompositions and Latent Semantic Indexing
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Word-Like Character N-Gram Embedding
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Learned in Speech Recognition: Contextual Acoustic Word Embeddings
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Knowledge-Powered Deep Learning for Word Embedding
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