Kernel Approach for Classification Using Conditional Random Field: Information Extraction - Lokesh Pawar,Rohit Bajaj
-25% koodilla BOOKS
Toimitus 15-21 arkipäivässä
30 päivän palautusoikeus
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 ... Täydellinen kuvaus
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Kuvaus
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.
Lisätietoja
| Kirjoittaja | Lokesh Pawar, Rohit Bajaj |
|---|---|
| Julkaisija | LAP LAMBERT Academic Publishing |
| Julkaisuvuosi | 2022 |
| Kannen tyyppi | Pehmeäkantinen |
| EAN | 9786204954592 |