2017 IntroductiontoAppliedNaturalLan: Difference between revisions
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=== Description === | === Description === | ||
The automated processing of text data has become a mission critical capability in industries as varied as medicine, finance, law, advertising, and engineering. This tutorial will review the best-practices in many of these application areas from the perspective of proven applications, methods, practices, tools and resources. When completed you will understand and be able to prototype components of an end-to-end [[NLP system]] that achieves best-baseline results. | The automated processing of text data has become a [[mission critical capability]] in industries as varied as medicine, finance, law, advertising, and engineering. This tutorial will review the best-practices in many of these application areas from the perspective of proven applications, methods, practices, tools and resources. When completed you will understand and be able to prototype components of an end-to-end [[NLP system]] that achieves best-baseline results. | ||
; Overview | ; Overview | ||
Revision as of 18:49, 20 May 2020
- (Melli, 2017) ⇒ Gabor Melli. (2017). “Introduction to Applied Natural Language Processing.” In: Proceedings of In conjunction with #DDTX17.
Subject Headings: NLP Tutorial.
Notes
- Presentation Slides: https://goo.gl/t5TbjY
- Registration Page: http://eventbrite.com/e/applied-natural-language-processing-with-gabor-melli-sold-out-tickets-31017702784
Cited By
Quotes
Description
The automated processing of text data has become a mission critical capability in industries as varied as medicine, finance, law, advertising, and engineering. This tutorial will review the best-practices in many of these application areas from the perspective of proven applications, methods, practices, tools and resources. When completed you will understand and be able to prototype components of an end-to-end NLP system that achieves best-baseline results.
- Overview
- Text Preprocessing such as tokenization, lemmatisation, and end-of-sentence detection.
- Shallow Syntactic and Semantic Analysis such as semantic role labeling, and named entity recognition.
- Text Classification & Clustering such as spam detection and topic modeling.
- Information Extraction such as relation extraction in open and closed-domains.
- Word Sense Disambiguation such as linking to an ontology.
- Word Relatedness Functions such as from continuous word embeddings in 'Deep' neural networks.
- Text Summarization such as data-driven question / answering.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2017 IntroductiontoAppliedNaturalLan | Gabor Melli | Introduction to Big Applied Natural Language Processing |