Kernel Approach for Classification Using Conditional Random Field - Pawar, Lokesh;Bajaj, Rohit
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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 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…mehr

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Produktbeschreibung
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.
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Autorenporträt
Er. Lokesh Pawar trabalha como professor assistente na Universidade de Chandigarh e tem um grande interesse pela inovação e investigação. Registou duas patentes atualmente em fase de concessão. Publicou os seus artigos em quase todas as publicações de renome, Elsevier, Springer, Taylor and Francis e IEEE. Publicou vários artigos indexados à SCOPUS.