해군사관학교 학술정보원검색
- Home
- 기능목록
- 검색
-
Marc 보기
MARC 닫기
-
LDR
04450cam a2200445M 4500
-
001
000001335022
-
005
20210114163722
-
006
m d
-
007
cr |n|||||||||
-
008
200903s2020 xx o 0|| 0 eng d
-
019
▼a 1196251511
-
020
▼a 9781800200807
▼q (electronic bk.) -
020
▼a 1800200803
▼q (electronic bk.) -
020
▼z 1800208421
-
020
▼z 9781800208421
-
035
▼a 2581635
▼b (N$T) -
035
▼a (OCoLC)1192307401
▼z (OCoLC)1196251511 -
040
▼a YDX
▼b eng
▼c YDX
▼d EBLCP
▼d N$T
▼d 248023 -
050
▼a QA76.9.N38
-
082
▼a 006.35
▼2 23 -
245
▼a NATURAL LANGUAGE PROCESSING WORKSHOP -
▼h [electronic resource]:
▼b design and build nlp projects. -
260
▼a [S.l.]:
▼b PACKT PUBLISHING LIMITED,
▼c 2020. -
300
▼a 1 online resource.
-
505
▼a Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Natural Language Processing -- Introduction -- History of NLP -- Text Analytics and NLP -- Exercise 1.01: Basic Text Analytics -- Various Steps in NLP -- Tokenization -- Exercise 1.02: Tokenization of a Simple Sentence -- PoS Tagging -- Exercise 1.03: PoS Tagging -- Stop Word Removal -- Exercise 1.04: Stop Word Removal -- Text Normalization -- Exercise 1.05: Text Normalization -- Spelling Correction -- Exercise 1.06: Spelling Correction of a Word and a Sentence -- Stemming -- Exercise 1.07: Using Stemming
-
505
▼a Lemmatization -- Exercise 1.08: Extracting the Base Word Using Lemmatization -- Named Entity Recognition (NER) -- Exercise 1.09: Treating Named Entities -- Word Sense Disambiguation -- Exercise 1.10: Word Sense Disambiguation -- Sentence Boundary Detection -- Exercise 1.11: Sentence Boundary Detection -- Activity 1.01: Preprocessing of Raw Text -- Kick Starting an NLP Project -- Data Collection -- Data Preprocessing -- Feature Extraction -- Model Development -- Model Assessment -- Model Deployment -- Summary -- Chapter 2: Feature Extraction Methods -- Introduction -- Types of Data
-
505
▼a Categorizing Data Based on Structure -- Categorizing Data Based on Content -- Cleaning Text Data -- Tokenization -- Exercise 2.01: Text Cleaning and Tokenization -- Exercise 2.02: Extracting n-grams -- Exercise 2.03: Tokenizing Text with Keras and TextBlob -- Types of Tokenizers -- Exercise 2.04: Tokenizing Text Using Various Tokenizers -- Stemming -- RegexpStemmer -- Exercise 2.05: Converting Words in the Present Continuous Tense into Base Words with RegexpStemmer -- The Porter Stemmer -- Exercise 2.06: Using the Porter Stemmer -- Lemmatization -- Exercise 2.07: Performing Lemmatization
-
505
▼a Exercise 2.08: Singularizing and Pluralizing Words -- Language Translation -- Exercise 2.09: Language Translation -- Stop-Word Removal -- Exercise 2.10: Removing Stop Words from Text -- Activity 2.01: Extracting Top Keywords from the News Article -- Feature Extraction from Texts -- Extracting General Features from Raw Text -- Exercise 2.11: Extracting General Features from Raw Text -- Exercise 2.12: Extracting General Features from Text -- Bag of Words (BoW) -- Exercise 2.13: Creating a Bag of Words -- Zipf's Law -- Exercise 2.14: Zipf's Law -- Term Frequency-Inverse Document Frequency (TFIDF)
-
505
▼a Exercise 2.15: TFIDF Representation -- Finding Text Similarity -- Application of Feature Extraction -- Exercise 2.16: Calculating Text Similarity Using Jaccard and Cosine Similarity -- Word Sense Disambiguation Using the Lesk Algorithm -- Exercise 2.17: Implementing the Lesk Algorithm Using String Similarity and Text Vectorization -- Word Clouds -- Exercise 2.18: Generating Word Clouds -- Other Visualizations -- Exercise 2.19: Other Visualizations Dependency Parse Trees and Named Entities -- Activity 2.02: Text Visualization -- Summary -- Chapter 3: Developing a Text Classifier -- Introduction
-
590
▼a Master record variable field(s) change: 050, 082, 650 - OCLC control number change
-
650
▼a Natural language processing (Computer science)
-
655
▼a Electronic books.
-
655
▼a Electronic books.
-
776
▼c Original,
▼z 1800208421,
▼z 9781800208421
▼w (OCoLC)1162875810 -
856
▼3 EBSCOhost
▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2581635 -
938
▼a YBP Library Services
▼b YANK
▼n 301482209 -
938
▼a ProQuest Ebook Central
▼b EBLB
▼n EBL6349569 -
938
▼a EBSCOhost
▼b EBSC
▼n 2581635 -
990
▼a 강리원
-
991
▼a eBook
-
994
▼a 92
▼b N$T
-
LDR
- 바구니담기
- 내서재 담기
- 내보내기
상세정보
NATURAL LANGUAGE PROCESSING WORKSHOP - : design and build nlp projects [electronic resource]
QR코드| 자료유형 : | eBook |
|---|---|
| ISBN : | 9781800200807 |
| ISBN : | 1800200803 |
| ISBN : | |
| ISBN : | |
| 서명/저자사항 : | NATURAL LANGUAGE PROCESSING WORKSHOP - [electronic resource]: design and build nlp projects. |
| 발행사항 : | [S.l.]: PACKT PUBLISHING LIMITED, 2020. |
| 형태사항 : | 1 online resource. |
| 내용주기 : | Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Introduction to Natural Language Processing -- Introduction -- History of NLP -- Text Analytics and NLP -- Exercise 1.01: Basic Text Analytics -- Various Steps in NLP -- Tokenization -- Exercise 1.02: Tokenization of a Simple Sentence -- PoS Tagging -- Exercise 1.03: PoS Tagging -- Stop Word Removal -- Exercise 1.04: Stop Word Removal -- Text Normalization -- Exercise 1.05: Text Normalization -- Spelling Correction -- Exercise 1.06: Spelling Correction of a Word and a Sentence -- Stemming -- Exercise 1.07: Using Stemming |
| 내용주기 : | Lemmatization -- Exercise 1.08: Extracting the Base Word Using Lemmatization -- Named Entity Recognition (NER) -- Exercise 1.09: Treating Named Entities -- Word Sense Disambiguation -- Exercise 1.10: Word Sense Disambiguation -- Sentence Boundary Detection -- Exercise 1.11: Sentence Boundary Detection -- Activity 1.01: Preprocessing of Raw Text -- Kick Starting an NLP Project -- Data Collection -- Data Preprocessing -- Feature Extraction -- Model Development -- Model Assessment -- Model Deployment -- Summary -- Chapter 2: Feature Extraction Methods -- Introduction -- Types of Data |
| 내용주기 : | Categorizing Data Based on Structure -- Categorizing Data Based on Content -- Cleaning Text Data -- Tokenization -- Exercise 2.01: Text Cleaning and Tokenization -- Exercise 2.02: Extracting n-grams -- Exercise 2.03: Tokenizing Text with Keras and TextBlob -- Types of Tokenizers -- Exercise 2.04: Tokenizing Text Using Various Tokenizers -- Stemming -- RegexpStemmer -- Exercise 2.05: Converting Words in the Present Continuous Tense into Base Words with RegexpStemmer -- The Porter Stemmer -- Exercise 2.06: Using the Porter Stemmer -- Lemmatization -- Exercise 2.07: Performing Lemmatization |
| 내용주기 : | Exercise 2.08: Singularizing and Pluralizing Words -- Language Translation -- Exercise 2.09: Language Translation -- Stop-Word Removal -- Exercise 2.10: Removing Stop Words from Text -- Activity 2.01: Extracting Top Keywords from the News Article -- Feature Extraction from Texts -- Extracting General Features from Raw Text -- Exercise 2.11: Extracting General Features from Raw Text -- Exercise 2.12: Extracting General Features from Text -- Bag of Words (BoW) -- Exercise 2.13: Creating a Bag of Words -- Zipf's Law -- Exercise 2.14: Zipf's Law -- Term Frequency-Inverse Document Frequency (TFIDF) |
| 내용주기 : | Exercise 2.15: TFIDF Representation -- Finding Text Similarity -- Application of Feature Extraction -- Exercise 2.16: Calculating Text Similarity Using Jaccard and Cosine Similarity -- Word Sense Disambiguation Using the Lesk Algorithm -- Exercise 2.17: Implementing the Lesk Algorithm Using String Similarity and Text Vectorization -- Word Clouds -- Exercise 2.18: Generating Word Clouds -- Other Visualizations -- Exercise 2.19: Other Visualizations Dependency Parse Trees and Named Entities -- Activity 2.02: Text Visualization -- Summary -- Chapter 3: Developing a Text Classifier -- Introduction |
| 일반주제명 : | Natural language processing (Computer science) -- |
| 기타형태 저록 : | Original, 1800208421, 9781800208421 |
| 언어 | 영어 |
| URL : |
|---|
예약
- 1. 예약현황은 홈페이지 로그인 후 예약 페이지에 확인 가능합니다.
- 2. 도착 통보된 예약자료 대출을 원하지 않는 경우에는 예약 현황에서 취소할 수 있습니다.
- 3. 기타 문의사항은 도서관에 문의 바랍니다.
무인예약대출
- 1. 무인예약대출 현황은 홈페이지 로그인 후 무인예약대출 페이지에 확인 가능합니다.
- 2. 무인예약대출자료 대출을 원하지 않는 경우에는 무인예약대출 페이지에서 신청 또는 접수상태인 경우만 취소할 수 있습니다.
- 3. 희망대출일은 신청일로부터 최대 1주일 까지 가능합니다.
- 4. 희망대출일을 선택하지 않은 경우 대출대기 통보 후 1주일까지 기기에서 대출가능합니다.
- 5. 기타 문의사항은 도서관에 문의 바랍니다.
서평쓰기