Natural Language Processing for Search
Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data.
This training explores various tasks in NLP that are useful to improve the quality of your search system and how to integrate them into your search engine, using open-source software.
• Software engineering background
• Basic understanding of Search Engines and Machine Learning.
WHAT YOU WILL LEARN
• How to integrate natural language processing techniques with your search engine
• How to use pre-trained language models and fine tune them for your specific use case
• The pros/cons of vector based search
• How to do that with Apache Lucene/Solr and Elasticsearch
• Software Engineers
• Data Scientist
• Machine Learning passionates
Based on experience with leading companies including
8th September 2022
If you can't participate, you can easly ask for a private training by contacting us on the button below.
EARLY BIRD PRICE
Hurry up! Time is running out
Deadline: 18th August 2022 | 11:59 PM (GMT+1)
APACHE LUCENE/SOLR COMMITTER
APACHE SOLR PMC MEMBER
Alessandro has been involved in designing and developing search-relevant solutions from 2010.
Over the years he has worked on various projects, with various open source technologies aiming to build search solutions able to satisfy the user information needs, often integrating such solutions with machine learning and artificial intelligence technologies.
RRE Creator Andrea Gazzarini is a curious software engineer, mainly focused on the Java language and Search technologies. With more than 15 years of experience in various software engineering areas, his adventure in the search world began in 2010, when he met Apache Solr and later Elasticsearch.
- NLP tasks useful for Search
- Open Source Libraries Overview
- Text and Speech Processing (Optical Character Recognition, Speech Recognition, Text to Speech, Tokenization)
- Morphological Analysis (Stemming/Lemmatization, Part Of Speech Tagging)
- Syntactic Analysis (Sentence Breaking)
- Lexical Semantics(Named Entity Recognition and Linking, Sentiment Analysis, Word Sense Disambiguation)
- Discourse(Coreference Resolution)
- Implicit Semantic(Topic Segmentation and Recognition)
- Higher Level Application(Automatic Summarization, Grammatical Error Correction, Machine Translation, Natural Language Understanding)
- Hands on Exercises