Kernel Approach for Classification Using Conditional Random Field | Information Extraction

Lokesh Pawar (u. a.)

ISBN 10: 6204954598 / ISBN 13: 9786204954592

Published by LAP LAMBERT Academic Publishing, 2022

About this Item

Kernel Approach for Classification Using Conditional Random Field | Information Extraction | Lokesh Pawar (u. a.) | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786204954592 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 122027566

Bibliographic Details

• Title: Kernel Approach for Classification Using Conditional Random Field | Information Extraction
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2022
• Language: English
• ISBN 10: 6204954598
• ISBN 13: 9786204954592
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 119 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Extracting useful information from the pool of big data gives birth to new domain known as Information Extraction. The domain of Information Extraction has its genesis in Natural Language Processing (NLP). The fundamental drift in this field takes the birth from various competitions that are focused on the recognition and extraction of named entities such as names of people, organizations etc. As the world become more data oriented by advent of internet, new applications of processing of structured and unstructured data comes in light. Most of the interest is to extract and classify named entities like person, organization and location etc. that is a subtask of Information Extraction known as Entity Extraction and Classification.