Showing papers by "Katsumi Tanaka published in 2018"

Mining Alternative Actions from Community Q&A Corpus

Abstract: Web searchers often use a Web search engine to find a way or means to achieve his/her goal. For example, a user intending to solve his/her sleeping problem, the query “sleeping pills” may be used. However, there may be another solution to achieve the same goal, such as “have a cup of hot milk” or “stroll before bedtime.” The problem is that the user may not be aware that these solutions exist. Thus, he/she will probably choose to take a sleeping pill without considering these solutions. In this study, we define and tackle the alternative action mining problem. In particular, we attempt to develop a method for mining alternative actions for a given query. We define alternative actions as actions which share the same goal and define the alternative action mining problem as similar in the search result diversification. To tackle the problem, we propose leveraging a community Q&A (cQA) corpus for mining alternative actions. The cQA corpus can be seen as an archival dataset comprising dialogues between questioners, who want to know the solutions to their problem, and respondents, who suggest different solutions. We propose a method to compute how well two actions can be alternative actions by using a question-answer structure in a cQA corpus. Our method builds a question-action bipartite graph and recursively computes how well two actions can be alternative actions. We conducted experiments to investigate the effectiveness of our method using two newly built test collections, each containing 50 queries. The experimental results indicated that, for Japanese test collection, our proposed method significantly outperformed two types of baselines, one used the conventional query suggestions and the other extracted alternative-actions from the Web documents, in terms of D#-nDCG@8. Also, for English test collection, our method significantly outperformed the baseline using the conventional query suggestions in terms of D#-nDCG@8.

Entity Ranking for Queries with Modifiers Based on Knowledge Bases and Web Search Results

Abstract: This paper proposes methods of finding a ranked list of entities for a given query (e.g. “Kennin-ji”, “Tenryu-ji”, or “Kinkaku-ji” for the query “ancient zen buddhist temples in kyoto”) by leveraging different types of modifiers in the query through identifying corresponding properties (e.g. established date and location for the modifiers “ancient” and “kyoto”, respectively). While most major search engines provide the entity search functionality that returns a list of entities based on users’ queries, entities are neither presented for a wide variety of search queries, nor in the order that users expect. To enhance the effectiveness of entity search, we propose two entity ranking methods. Our first proposed method is a Webbased entity ranking that directly finds relevant entities from Web search results returned in response to the query as a whole, and propagates the estimated relevance to the other entities. The second proposed method is a property-based entity ranking that ranks entities based on properties corresponding to modifiers in the query. To this end, we propose a novel property identification method that identifies a set of relevant properties based on a Support Vector Machine (SVM) using our seven criteria that are effective for different types of modifiers. The experimental results showed that our proposed property identification method could predict more relevant properties than using each of the criteria separately. Moreover, we achieved the best performance for returning a ranked list of relevant entities when using the combination of the Web-based and property-based entity ranking methods. key words: entity ranking, property identification, knowledge base, web search

Kcanvas: An Application for Modern PIM - Organizing Daily Fragments of Information and Telling Story of Personal Interest

Abstract: In our previous work, we introduced a web application named “Kcanvas” and how users received it. Kcanvas provides a huge canvas as a playful interface so that users can intuitively manage fragments of information that they are interested in in their everyday life in a casual manner. Subsequently we had face-to-face user observation sessions to get to know users better. The participants not only enjoyed composing their own canvases but also willingly told their personal interest hopping on fragments of information on their canvases, while they hesitated to make their canvases public on the Web. Throughout the user observation sessions, we are convinced that storytelling would be a key to organize daily fragments of information and there is a need to take care users’ambivalent feeling toward personal fragments of interest. In this paper, we explain the ambivalent feeling, redefine the structure of personal information space, and introduce Kcanvas’ newly added and enhanced functions for storytelling.