Downloaded 31 times
Uploaded byNikola Milosevic
Machine learning (ML) and natural language processing (NLP)
The document discusses artificial intelligence (AI) and machine learning, defining AI as a machine's ability to interact with the environment and emphasizing machine learning as a subfield of computer science focused on learning tasks without explicit programming. It outlines various types of machine learning, challenges of analyzing text data, and the steps involved in lexical and semantic processing. Additionally, it mentions frameworks and tools for machine learning and natural language processing, including Sci-kit Learn and Keras, while highlighting the importance of feature engineering and search techniques in extracting information from unstructured data.
More Related Content
Similar to Machine learning (ML) and natural language processing (NLP)
Machine learning (ML) and natural language processing (NLP)
- 1. Natural language processingand machine learning Nikola Milosevic
- 2. What is AI? •Intelligence presented by a machine • Flexible agent that interacts with the environment and performs actions to maximize success towards certain goal
- 3.
- 4. What is machinelearning • Subfield of computer science that explores how machines can learn to perform certain task without explicit programming
- 5.
- 6. Types of machinelearning • Supervised learning • Semi-supervised learning • Unsupervised learning • Reinforcement learning
- 7. Machine learning problems •Classification • Clustering • Regression
- 8. Testing the model •Iteratively improve the model • Test multiple algorithms – find the best one • No free lunch theory • Feedback loop for feature selection • Konfuziona matrica 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃 + 𝑇𝑁 𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁
- 9. Examples of MLframeworks and algorithms • SCI-kit learn – Python library – Implementation of the most useful algorithms – Naïve Bayes, SVM, Random forests, decision trees… • Keras – Python library implementing about everything related to neural networks
- 10. Text data • About80% of data in organizations are in text format • Harder to analyse than structured data • Huge amount of textual documents – Only in biomedicine 2200 scientific papers are published every day • Growing exponentially
- 11. Main goals oftext mining • Make communication easier (e.g. translation) • Automate some processes (e.g. communication agents/chatbots) • Do data mining on textual and unstructured data
- 12.
- 13. Challenges • Man sawa woman with the telescope. – Who has a telescope? • Multiple senses, synonyms, homonyms, irony • Grammar and context can help • Acronyms
- 14. Approaches • Rule based –Human defined rules to extract information – Needs expert humans who know how people express certain things – Is quite laborious • Machine learning based – Machine tries to learn what to extract guided by human – Needs annotated corpora (usually fairly large) • This is expensive to create and quite laborious
- 15. Levels of analysis •Lexical – Analysis of words • Syntactic – Analysis of organization of words (phrases, sentences) • Semantic – Analysis meaning • Sometimes pragmatic – Analysis pragmatics of the use of certain words, phrases. Why author used that?
- 16.
- 17. Lexical processing • Partof speech tagging • Parsing – Constituency – Dependency Stanford parser
- 18. Semantic processing • Textclassification – Sentiment analysis (positive/negative) – Classification by topics (politics/sport/business) – Authorship detection (Tolkien, Rowling, Shakespeare) • Named entity recognition • Topic modelling (unsupervised) • Search
- 19. Sequence modelling • Machinelearning technique useful for named entity recognition • Conditional random fields (CRF) or recurrent neural networks (often LSTM)
- 20. Feature engineering • Selectingimportant features that help extract information • Can be: – Words, PoS, word shapes, vocabulary features, etc. – May depend on task and methodology – Iterative process of selecting and improving the performance – Some features may confuse the algorithm
- 21. Search • Finds documentsthat are the most relevant for a given user query • Usual techniques include algorithm called TF- IDF and cosine similarity • May additionally use links towards text, positions of matched words and similar things to rank found documents • Apache Lucene, Solr (Java), there are also Python libraries
- 22. Language models • Usedas features to classification and other NLP tasks • Contain some basic characteristics of language • The most naïve (but also frequently used) is called Bag of Words • NN use more advanced models: word2vec, Glove, ULMo, BERT…
- 23. Useful tools andlibraries • Apache OpenNLP – Java • Apache Lucene – Java, C# • Stanford Core NLP – Java • NLTK – Python • GATE – GUI alat • SharpNLP • ... • Weka – for machine learning (GUI)