RUCIR at NTCIR-13 WWW Task
نویسندگان
چکیده
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 ranking model and rank the documents by the ranking scores. Finally, we could get the ranking list.
منابع مشابه
RUCIR at NTCIR-12 IMINE-2 Task
In this paper, we present our participation in the Query Understanding subtask and the Vertical Incorporating subtask of the NTCIR-12 IMine-2 task, for both English and Chinese topics. In the Query Understanding subtask, we combine the extracted candidates from search engine suggestions and Wikipeida, and classify their verticals after clustering and ranking them. In the Vertical Incorporating ...
متن کاملSLWWW at the NTCIR-13 WWW Task
SLWWW participated in the Chinese Subtask of the NTCIR13 WWW Task. We applied the query expansion methods based on word embeddings proposed by Kuzi, Shtok, and Kurland. However, according to our comparison with the baseline run, our runs were not successful. As the baseline run provided by the organisers was not included in the pools for constructing relevance assessments, we discuss condensed-...
متن کاملTHUIR at NTCIR-13 WWW Task
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...
متن کاملAn Evaluation of the Kernel Based Neural Ranking Model in NTCIR-13 WWW
This paper describes CMUIR’s participation in the NTCIR13 We Want Web (WWW) task. In the context of the Chinese subtask, we experimented with a neural network approach using the kernel based neural ranking model (KNRM). The model learns a word embedding that encodes IRcustomized soft match patterns from a Chinese search log. The learned model is then directly applied to re-rank the baseline run...
متن کاملOverview of NTCIR-12
This is an overview of NTCIR-13, the thirteenth sesquiannual research project for evaluating information access technologies. NTCIR-13 presents a diverse set of evaluation tasks related to information retrieval, question answering, natural language processing, etc (in total, nine tasks have been organized at NTCIR-13). This paper describes an outline of the research project, which includes its ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2017