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Natural language processing and computational linguistics : a practical guide to text analysis with Python, Gensim, spaCy, and Keras

Natural language processing and computational linguistics : a practical guide to text analysis with Python, Gensim, spaCy, and Keras (Loan 5 times)

Material type
단행본
Personal Author
Srinivasa-Desikan, Bhargav.
Title Statement
Natural language processing and computational linguistics : a practical guide to text analysis with Python, Gensim, spaCy, and Keras / Bhargav Srinivasa-Desikan.
Publication, Distribution, etc
Birmingham :   Packt Publishing,   c2018.  
Physical Medium
iv, 295 p. : ill. ; 24 cm.
Series Statement
Expert insight
ISBN
9781788838535
Subject Added Entry-Topical Term
Python (Computer program language). Natural language processing (Computer science). Computational linguistics.
000 00000nam u2200205 a 4500
001 000045983394
005 20190513100840
008 190510s2018 enka 000 0 eng d
020 ▼a 9781788838535
040 ▼a 211009 ▼c 211009 ▼d 211009
082 0 4 ▼a 006.35 ▼2 23
084 ▼a 006.35 ▼2 DDCK
090 ▼a 006.35 ▼b S774n
100 1 ▼a Srinivasa-Desikan, Bhargav.
245 1 0 ▼a Natural language processing and computational linguistics : ▼b a practical guide to text analysis with Python, Gensim, spaCy, and Keras / ▼c Bhargav Srinivasa-Desikan.
260 ▼a Birmingham : ▼b Packt Publishing, ▼c c2018.
300 ▼a iv, 295 p. : ▼b ill. ; ▼c 24 cm.
490 1 ▼a Expert insight
650 0 ▼a Python (Computer program language).
650 0 ▼a Natural language processing (Computer science).
650 0 ▼a Computational linguistics.
830 0 ▼a Expert insight.
945 ▼a KLPA

Holdings Information

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No. 1 Location Main Library/Western Books/ Call Number 006.35 S774n Accession No. 111809242 Availability Available Due Date Make a Reservation Service B M

Contents information

Author Introduction

바르가브 스리니바사 디지칸(지은이)

프랑스 릴에 있는 INRIA에서 연구원으로 근무하고 있다. MODAL(데이터 분석 및 학습 모델) 팀의 일원이며 함수 학습(metric learning), 예측 변수 집계(predictor aggregation) 및 데이터 시각화를 담당하고 있다. 파이썬 오픈 소스 커뮤니티에 정기적으로 기고하고 있으며 'Google Summer of Code in 2016'에서 젠심(Gensim)을 이용한 동적 토픽 모델을 구현하기도 했다. 유럽과 아시아에서 열리는 파이콘과 파이데이터 행사에 고정 연사로 참석하면서 파이썬을 이용한 텍스트 분석 튜토리얼을 작성하고 있다. 파이썬 머신 러닝 패키지인 pycobra의 관리자이며 「Journal of Machine Learning Research」(MIT, 2002)를 출간했다.

Information Provided By: : Aladin

Table of Contents

Cover -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: What is Text Analysis? -- What is text analysis? -- Where''s the data at? -- Garbage in, garbage out -- Why should you do text analysis? -- Summary -- References -- Chapter 2: Python Tips for Text Analysis -- Why Python? -- Text manipulation in Python -- Summary -- References -- Chapter 3: spaCy''s Language Models -- spaCy -- Installation -- Troubleshooting -- Language models -- Installing language models -- Installation – how and why? -- Basic preprocessing with language models -- Tokenizing text -- Part-of-speech (POS) – tagging -- Named entity recognition -- Rule-based matching -- Preprocessing -- Summary -- References -- Chapter 4: Gensim – Vectorizing Text and Transformations and n-grams -- Introducing Gensim -- Vectors and why we need them -- Bag-of-words -- TF-IDF -- Other representations -- Vector transformations in Gensim -- n-grams and some more preprocessing -- Summary -- References -- Chapter 5: POS-Tagging and Its Applications -- What is POS-tagging? -- POS-tagging in Python -- POS-tagging with spaCy -- Training our own POS-taggers -- POS-tagging code examples -- Summary -- References -- Chapter 6: NER-Tagging and Its Applications -- What is NER-tagging? -- NER-tagging in Python -- NER-tagging with spaCy -- Training our own NER-taggers -- NER-tagging examples and visualization -- Summary -- References -- Chapter 7: Dependency Parsing -- Dependency parsing -- Dependency parsing in Python -- Dependency parsing with spaCy -- Training our dependency parsers -- Summary -- References -- Chapter 8: Topic Models -- What are topic models? -- Topic models in Gensim -- Latent Dirichlet allocation -- Latent semantic indexing -- Hierarchical Dirichlet process -- Dynamic topic models -- Topic models in scikit-learn -- Summary -- References -- Chapter 9: Advanced Topic Modeling -- Advanced training tips -- Exploring documents -- Topic coherence and evaluating topic models -- Visualizing topic models -- Summary -- References -- Chapter 10: Clustering and Classifying Text -- Clustering text -- Starting clustering -- K-means -- Hierarchical clustering -- Classifying text -- Summary -- References -- Chapter 11: Similarity Queries and Summarization -- Similarity metrics -- Similarity queries -- Summarizing text -- Summary -- References -- Chapter 12: Word2Vec, Doc2Vec, and Gensim -- Word2Vec -- Using Word2Vec with Gensim -- Doc2Vec -- Other word embeddings -- GloVe -- FastText -- WordRank -- Varembed -- Poincare -- Summary -- References -- Chapter 13: Deep Learning for Text -- Deep learning -- Deep learning for text (and more) -- Generating text -- Summary -- References -- Chapter 14: Keras and spaCy for Deep Learning -- Keras and spaCy -- Classification with Keras -- Classification with spaCy -- Summary -- References -- Sentiment Analysis and ChatBots -- Sentiment analysis -- Reddit for mining data -- Twitter for mining data -- ChatBots -- Summary -- References -- Other Books You May Enjoy -- Index -- .

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