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 ...

In this paper, we describe our approaches in the NTCIR12 IMine task, including Chinese Query Understanding and Chinese Vertical Incorporating. In Query Understanding subtask, we propose different strategies to mine subtopic candidates from a wide range of resources and present a twostep method to predict the vertical intent for each subtopic. In Vertical Incorporating subtask, we adopt a probab...

Our group submitted task for Temporal Intent Disambiguation (TID) Subtask (Chinese) of NTCIR-2012. We using word2vec to model query String into feature vector, and using cos function to measure the similarity between query string and training corpus SougouCA. Our results shows the approach is efficient for solving thoes Task.

In this paper, we describe the system submitted by USTC team for the Short Text Conversation (STC) task of the NTCIR-12. We proposed transition-p2c, encoder-decoderReverse and joint-Train models for the STC task and submitted 5 official runs. The transition-p2c model provides transition probability between post and comment in word’s level which complements the TF-IDF feature. The encoderdecoder...

We present our approach for tackling the iUnit ranking and iUnit summarization subtasks of MobileClick2. We first conduct intent discovery based on latent topic modeling. Our iUnit ranking method exploits the discovered intents and considers the importance of an iUnit in each Web content document. We further develop our iUnit summarization model using the outcome from the iUnit ranking subtask....

This paper reports a dialogue system developed at the University of Tokyo for participation in NTCIR-12 on the short text conversation (STC) pilot task. We participated in the Japanese STC task on Twitter and built a system that selects plausible responses for an input post (tweet) from a given pool of tweets. Our system first selects a (small) set of tweets as response candidates from the pool...

The GIR team participated in the NTCIR 12 Temporal Information Access (Temporalia) Task. This report describes our approach to solving the Temporal Intent Disambiguation (TID) problem and discusses the official results. We explore the rich temporal information in the labeled and unlabeled search queries. A semi-supervised linear classifiers is then built up to predict the temporal classes for e...

This paper describes a prototype multilingual Q&A system that we have designed to participate in the Q&A Track of TREC-12. The system answer concrete responses, then we participate in the Q&A main task for factoid questions. The main areas of our system are: (1) Inductive Logic Programming to learn the question type, (2) Clustering of Named Entities to improve Information Retrieval and (3) Sema...

The SLSTC team participated in the NTCIR-12 Short Text Conversation (STC)[1] task. This report describes our approach to solving the STC problem and discusses the ocial results.