Learning to Rank for Information Retrieval and Natural Language Processing
โ Scribed by Hang Li
- Publisher
- Morgan & Claypool Publishers
- Year
- 2011
- Tongue
- English
- Leaves
- 115
- Series
- Synthesis Lectures on Human Language Technologies
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Subjects
ะะฝัะพัะผะฐัะธะบะฐ ะธ ะฒััะธัะปะธัะตะปัะฝะฐั ัะตั ะฝะธะบะฐ;ะัะบััััะฒะตะฝะฝัะน ะธะฝัะตะปะปะตะบั;ะะฝัะตะปะปะตะบััะฐะปัะฝัะน ะฐะฝะฐะปะธะท ะดะฐะฝะฝัั ;
๐ SIMILAR VOLUMES
Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant prog
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Due to the fast growth of the Web and the difficulties in finding desired information, efficient and effective information retrieval systems have become more important than ever, and the search engine has become an essential tool for many people. The ranker, a central component in every search engin
Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications, and different potential end-users. However, recent research has shown that