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Université de Montréal at the NTCIR-11 IMine Task
"... In this paper, we describe our participation to the NTCIR-11 IMine task, for both subtopic mining and document rank-ing sub-tasks. We experimented a new approach for aspect embedding which learns query aspects by selecting (good) expansion terms from a set of resources. In our partici-pation, we use ..."
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In this paper, we describe our participation to the NTCIR-11 IMine task, for both subtopic mining and document rank-ing sub-tasks. We experimented a new approach for aspect embedding which learns query aspects by selecting (good) expansion terms from a set of resources. In our partici-pation, we used five representative resources: ConceptNet, Wikipedia, query logs, feedback documents and query sug-gestions from Bing, Google and Yahoo!. Our method is trained in a supervised manner according to the principle that related terms should correspond to the same aspects. We tested our approach when using a single resource, and when using different resources. Experimental results show that our best document ranking run is ranked No. 2 of all 15 runs in terms of coarse-grain and fine-grain results.
The KLE’s Subtopic Mining System for the NTCIR-11 IMine Task
"... This paper describes our subtopic mining system for the NTCIR-11 IMine task. We propose a method that mines second-level subtopics using simple patterns and a hierar-chical structure of subtopic candidates based on sets of rel-evant documents, and combine the provided resources con-sidering their ch ..."
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This paper describes our subtopic mining system for the NTCIR-11 IMine task. We propose a method that mines second-level subtopics using simple patterns and a hierar-chical structure of subtopic candidates based on sets of rel-evant documents, and combine the provided resources con-sidering their characteristics. Our system generates rst-level subtopics using keywords in second-level subtopics, and groups the results by word correlation.
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"... In this paper, we describe our participation in the English Subtopic Mining and Document Ranking subtasks of the NTCIR-11 IMINE Task. In the Subtopic Mining subtask, we mine subtopics from query suggestions, query dimensions, and Freebase entities of a given query, rank them based on their importanc ..."
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In this paper, we describe our participation in the English Subtopic Mining and Document Ranking subtasks of the NTCIR-11 IMINE Task. In the Subtopic Mining subtask, we mine subtopics from query suggestions, query dimensions, and Freebase entities of a given query, rank them based on their importance for the given query, and nally construct a two-level hierarchy of subtopics. In the Document Ranking subtask, we diversify top search results by estimating the coverage of the mined subtopics. The best performance of our system achieves an Hscore of 0.1762, a Fscore of 0.3043, a Sscore of 0.3689, and an H-measure of 0.0634 for subtopic mining task. For document ranking run, the best perfor-mance of our system achieves a
InteractiveMediaMINE at the NTCIR-11 IMine Search Task Shohei MINE
"... The InteractiveMediaMINE team participated in the Task Mine subtask of the NTCIR-11 IMine Search Task. Our framework consists of three steps. First, we extend the query entered by the user in order to optimize the search engine. Second, we extract candidates of tasks from Ya-hoo! Chiebukuro with th ..."
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The InteractiveMediaMINE team participated in the Task Mine subtask of the NTCIR-11 IMine Search Task. Our framework consists of three steps. First, we extend the query entered by the user in order to optimize the search engine. Second, we extract candidates of tasks from Ya-hoo! Chiebukuro with the extended search query. Here, we use the top 10 pages of the search results. Finally, we calcu-late the score of the extracted tasks by the words frequency of each sentence; our system outputs tasks in the descend-ing order of the score. This paper describes our approach to solving the Task Mine problem and discusses its results.
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"... Our method for TaskMine consists of three steps. Firstly, we search for seed Web pages by using the query string with a word houhou, which means method. We collect more pages in consideration of the anchor texts in the seed pages. Then, we nd pairs of chunks satisfying predened patterns by dependenc ..."
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Our method for TaskMine consists of three steps. Firstly, we search for seed Web pages by using the query string with a word houhou, which means method. We collect more pages in consideration of the anchor texts in the seed pages. Then, we nd pairs of chunks satisfying predened patterns by dependency parsing on the sentences. We extract a tar-get and a postpositional particle from a depending chunk, and extract an operation and a negation (if it exists) from a depended chunk. We regard a quadruplet of them as a subtask. Finally, we rank the extracted subtasks by their frequency based score.
Team Name
"... In this paper, we present our participation in the Subtopic Mining subtask of the NTCIR-11 IMine task, for the En-glish language. Our participation presents a novel strategy for intent mining given a list of candidates for a specific query topic. This strategy is based on a topic exploration through ..."
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In this paper, we present our participation in the Subtopic Mining subtask of the NTCIR-11 IMine task, for the En-glish language. Our participation presents a novel strategy for intent mining given a list of candidates for a specific query topic. This strategy is based on a topic exploration through the use of continuous vector space models for each of the candidates based on classical vectorial operations. Our best run outperforms the other participants ’ submissions in terms of F-score and achieves a high position in the general ranking.
Team Name
"... In NTCIR-10, we participated in the subtask of Subtopic Mining. We classify test topics into two types: role-explicit topic and role-implicit topic. According to the topic type, we devise different approaches to perform subtopic mining. Specifically, for role-explicit topics, we propose an approach ..."
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In NTCIR-10, we participated in the subtask of Subtopic Mining. We classify test topics into two types: role-explicit topic and role-implicit topic. According to the topic type, we devise different approaches to perform subtopic mining. Specifically, for role-explicit topics, we propose an approach of modifier graph based subtopic mining. The key idea is that: The modifier graph corresponding to a role-explicit topic is decomposable into clusters with strong intra-cluster interaction and relatively weak inter-cluster interaction. Each modifier cluster intuitively reveals a possible subtopic. For role-implicit topics that generally express single information needs, we directly generate the ranked list through semantic similarities leveraging on lexical ontologies. The evaluation results show that our best Chinese subtopic mining run gets the first position among all the runs in terms of #D nDCG . However, our English subtopic mining runs show a poor performance, which is planned to be further improved in our future work.
