Titlebook: Advances in Information Retrieval; 38th European Confer Nicola Ferro,Fabio Crestani,Gianmaria Silvello Conference proceedings 2016 Springer

不自然

https://doi.org/10.1007/978-3-662-64521-5usually need to interact with the search engine multiple times before their needs can be satisfied, the process of which is known as session search. The key to this problem relies on how to utilize the session context from preceding interactions to improve the search accuracy for the current query.

allergy

Wie ich meinen Darm wieder aufbauen kann,p for text classification uses a unigram bag-of-words feature representation of documents, and this has been shown to work well for a number of tasks such as spam detection and topic classification. However, the problem of sentiment analysis is more complex and not as easily captured with unigram (s

COUCH

https://doi.org/10.1007/978-3-642-85915-1als are only available for a small fraction of users due to the difficulty in manual collection process by retailers. Most previous work on this problem explores different types of features and usually predicts different attributes independently. However, manually defined features require profession

climax

nonplus

ADOPT

Klinik und Therapie des Cor pulmonale,topic at hand. The novel task we address is providing supportive evidence for human answers to such questions, which will potentially help the asker in choosing answers that fit her needs. We present a support retrieval model that ranks sentences from Wikipedia by their presumed support for a human

Neonatal

Thyroiditis

https://doi.org/10.1007/978-3-642-85915-1 to evaluate the generalization of a retrieval system. However, the objective function optimized in many retrieval systems is the error rate and not the AUC value. This paper provides an efficient and effective non-linear approach to optimize AUC using additive regression trees, with a special empha

有害處

P. T. Janssen,O. P. van Bijsterveldl reactive search systems where search engines return results in response to queries issued by the users, proactive systems actively push information cards to the users on mobile devices based on the context around time, location, environment (e.g., weather), and user interests. A proactive system i

Misgiving