Course Code

NPL_LBG

Duration

21 hours (usually 3 days including breaks)

Requirements

Knowledge and awareness of NLP principals and an appreciation of AI application in business

Overview

이 교실 기반 교육 세션은 비즈니스에서 AI 및 로보틱스의 응용 프로그램과 함께 NLP 기술을 탐구합니다 대표자는 Python을 사용하여 컴퓨터 기반 예제 및 사례 연구 해결 연습을 수행합니다 .

Machine Translated

Course Outline

Detailed training outline

Introduction to NLP
- Understanding NLP
- NLP Frameworks
- Commercial applications of NLP
- Scraping data from the web
- Working with various APIs to retrieve text data
- Working and storing text corpora saving content and relevant metadata
- Advantages of using Python and NLTK crash course
Practical Understanding of a Corpus and Dataset
- Why do we need a corpus?
- Corpus Analysis
- Types of data attributes
- Different file formats for corpora
- Preparing a dataset for NLP applications
Understanding the Structure of a Sentences
- Components of NLP
- Natural language understanding
- Morphological analysis - stem, word, token, speech tags
- Syntactic analysis
- Semantic analysis
- Handling ambigiuty
Text data preprocessing
- Corpus- raw text
  - Sentence tokenization
  - Stemming for raw text
  - Lemmization of raw text
  - Stop word removal
- Corpus-raw sentences
  - Word tokenization
  - Word lemmatization
- Working with Term-Document/Document-Term matrices
- Text tokenization into n-grams and sentences
- Practical and customized preprocessing
Analyzing Text data
- Basic feature of NLP
  - Parsers and parsing
  - POS tagging and taggers
  - Name entity recognition
  - N-grams
  - Bag of words
- Statistical features of NLP
  - Concepts of Linear algebra for NLP
  - Probabilistic theory for NLP
  - TF-IDF
  - Vectorization
  - Encoders and Decoders
  - Normalization
  - Probabilistic Models
- Advanced feature engineering and NLP
  - Basics of word2vec
  - Components of word2vec model
  - Logic of the word2vec model
  - Extension of the word2vec concept
  - Application of word2vec model
- Case study: Application of bag of words: automatic text summarization using simplified and true Luhn's algorithms
Document Clustering, Classification and Topic Modeling
- Document clustering and pattern mining (hierarchical clustering, k-means, clustering, etc.)
- Comparing and classifying documents using TFIDF, Jaccard and cosine distance measures
- Document classifcication using Naïve Bayes and Maximum Entropy
Identifying Important Text Elements
- Reducing dimensionality: Principal Component Analysis, Singular Value Decomposition non-negative matrix factorization
- Topic modeling and information retrieval using Latent Semantic Analysis
Entity Extraction, Sentiment Analysis and Advanced Topic Modeling
- Positive vs. negative: degree of sentiment
- Item Response Theory
- Part of speech tagging and its application: finding people, places and organizations mentioned in text
- Advanced topic modeling: Latent Dirichlet Allocation
Case studies
- Mining unstructured user reviews
- Sentiment classification and visualization of Product Review Data
- Mining search logs for usage patterns
- Text classification
- Topic modelling