Kernel Approach for Classification Using Conditional Random Field: Information Extraction - Lokesh Pawar,Rohit Bajaj
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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 ... Pilns apraksts
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Aprašymas
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.
Vairāk informācijas
| Autors | Lokesh Pawar, Rohit Bajaj |
|---|---|
| Izdevējs | LAP LAMBERT Academic Publishing |
| Izlaides gads | 2022 |
| Vāka tips | Mīkstais vāks |
| EAN | 9786204954592 |