نتایج جستجو برای: 13 at 4
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This paper describes our approaches and results in NTCIR13 WWW task. In English subtask, we adopt several advanced deep models, like DSSM and DRMM. In Chinese subtask, we additionally make a few changes in models to ensure them work well in the Chinese context and train the Duet model with the weak-supervised relevance labels generated by various click models. Meanwhile, we extract 3 types of f...
In recent years, popular search engines are utilizing the power of Knowledge Graph(KG) to provide specific answers to queries and questions in a direct way. It is expected that search engine result pages (SERPs) will provide facts about the quires satisfying semantic meaning, which encouraging researchers to constructing more powerful Knowledge Graph. One of the major challenges is disambiguati...
In this paper, we present our approach in the We Want Web(WWW)[1] task of NTCIR-13, for both English and Chinese languages. We implement a ranking model for traditional re-ranking problems based on learning to rank. We first process the raw data and extract text features, match features, embedding features and semantic features for each query-document pair. Then we use LamdaMART[2] to train the...
We participated in the NTCIR-13 OpenLiveQ Task in view of improving the baseline method, which is a linear combination of 77 features with different weights. We added three settings of extended BM25F as additional features, replaced the target function of weight optimization and recalculated feature weights using 5-fold cross validation. Our run achieved the best Q-measure score among 10 runs i...
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