著者

則 のぞみ ボレガラ ダヌシカ 鹿島 久嗣

出版者

人工知能学会

雑誌

人工知能学会全国大会論文集 (ISSN:13479881)

巻号頁・発行日

vol.26, 2012

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ソーシャルWebサービスにおいて、他のユーザとのコミュニケーションや、コンテンツへのアノテーションなどといったユーザの行動は重要な役割を果たす。ユーザの行動は、ユーザ、文書、キーワード、場所など、複数の異種オブジェクトを巻き込んだ関係データとして表現されるため、これらを限定されたデータから予測することは困難である。本研究では、観測データの疎性に対して頑強な多オブジェクト間関係の予測法を提案する。

著者

則 のぞみ ボレガラ ダヌシカ 鹿島 久嗣

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.29, no.1, pp.168-176, 2014-01-05 (Released:2014-01-07)

参考文献数

24

被引用文献数

1 1

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Many phenomena in the real world can be represented as multinomial relations, which involve multiple and heterogeneous objects. For instance, in social media, users' various actions such as adding annotations to web resources or sharing news with their friends can be represented by multinomial relations which involve multiple and heterogeneous objects such as users, documents, keywords and locations. Predicting multinomial relations would improve many fundamental applications in various domains such as online marketing, social media analyses and drug development. However, the high-dimensional property of such multinomial relations poses one fundamental challenge, that is, predicting multinomial relations with only a limited amount of data. In this paper, we propose a new multinomial relation prediction method, which is robust to data sparsity. We transform each instance of a multinomial relation into a set of binomial relations between the objects and the multinomial relation of the involved objects. We then apply an extension of a low-dimensional embedding technique to these binomial relations, which results in a generalized eigenvalue problem guaranteeing global optimal solutions. We also incorporate attribute information as side information to address the ``cold start"problem in multinomial relation prediction. Experiments with various real-world social web service datasets demonstrate that the proposed method is more robust against data sparseness as compared to several existing methods, which can only find sub-optimal solutions.

著者

後藤 友和 グエン トアンドゥク ボレガラ ダヌシカ 石塚 満

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.26, no.6, pp.649-656, 2011 (Released:2011-09-09)

参考文献数

13

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Relational similarity can be defined as the similarity between two semantic relations R and R' that exist respectively in two word pairs (A,B) and (C,D). Relational search, a novel search paradigm that is based on the relational similarity between word pairs, attempts to find a word D for the slot ? in the query {(A,B), (C,?)} such that the relational similarity between the two word pairs (A, B) and (C, D) is a maximum. However, one problem frequently encountered by a Web-based relational search engine is that the inherent noise in Web text leads to incorrect measurement of relational similarity. To overcome this problem, we propose a method for verifying a relational search result that exploits the symmetric properties in proportional analogies. To verify a candidate result D for a query {(A, B), (C, ?)}, we replace the original question mark by D to create a new query {(A,B),(?,D)} and verify that we can retrieve C as a candidate for the new query. The score of C in the new query can be seen as a complementary score of D because it reflects the reliability of D in the original query. Moreover, transformations of words in proportional analogies lead to relational symmetries that can be utilized to accurately measure the relational similarity between two semantic relations. For example, if the two word pairs (A,B) and (C, D) show a high degree of relational similarity then the two word pairs (B,A) and (D,C) also have a high degree of relational similarity. We apply this idea in relational search by using symmetric queries such as {(B, A), (D, ?)} to create six queries for verifying a candidate answer D to improve the reliability of the verification process. Our experimental results on the Scholastic Aptitude Test (SAT) analogy benchmark show that the proposed method improves the accuracy of a relational search engine by a wide margin.

著者

ボレガラ ダヌシカ

雑誌

人工知能

巻号頁・発行日

vol.30, no.5, pp.709-712, 2015-09-01

著者

横手 健一 ボレガラ ダヌシカ 石塚 満

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.28, no.2, pp.220-229, 2013 (Released:2013-02-15)

参考文献数

33

被引用文献数

1 2

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Predicting entailment between two given texts is an important task on which the performance of numerous NLP tasks such as question answering, text summarization, and information extraction depend.The degree to which two texts are similar has been used extensively as a key feature in much previous work in predicting entailment. However, using similarity scores directly, without proper transformations, results in suboptimal performance. Given a set of lexical similarity measures, we propose a method that jointly learns both (a) a set of non-linear transformation functions for those similarity measures and, (b) the optimal non-linear combination of those transformation functions to predict textual entailment. Our method consistently outperforms numerous baselines, reporting a micro-averaged F-score of 46.48 on the RTE-7 benchmark dataset. The proposed method is ranked 2-nd among 33 entailment systems participated in RTE-7, demonstrating its competitiveness over numerous other entailment approaches. Although our method is statistically comparable to the current state-of-the-art, we require less external knowledge resources.

著者

則 のぞみ ボレガラ ダヌシカ 石塚 満

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.30, no.4, pp.613-625, 2015-07-01 (Released:2015-07-03)

参考文献数

29

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We propose a method to predict users' interests by exploiting their various actions in social media. Actions performed by users in social media such as Twitter and Facebook have a fundamental property: user action involves multiple entities - e.g. sharing URLs with friends, bookmarking and tagging web pages, clicking a favorite button on a friend's post etc. Consequently, it is appropriate to represent each user's action at some point in time as a higher-order relation. We propose ActionGraph, a novel graph representation to model users' higher-order actions. Each action performed by a user at some time point is represented by an action node. ActionGraph is a bipartite graph whose edges connect an action node to its involving entities, referred to as object nodes. Using real-world social media data, we empirically justify the proposed graph structure. We show that the prediction accuracy can be improved by adequately aggregating various actions. Moreover, our experimental results show that the proposed ActionGraph outperforms several baselines, including standard tensor analysis PARAFAC, a previously proposed state-of-the-art LDA-based method and other graph-based variants, in a user interest prediction task. Although a lot of research have been conducted to capture similarity between users or between users and resources by using graph, our paper indicates that an important factor for the prediction performance of the graph mining algorithm is the choice of the graph itself. In particular, our result indicates that in order to predict users activities, adding more specific information about users activities such as types of activities makes the graph mining algorithm more effective.

著者

則 のぞみ ボレガラ ダヌシカ 鹿島 久嗣

出版者

人工知能学会

雑誌

人工知能学会全国大会論文集 (ISSN:13479881)

巻号頁・発行日

vol.28, 2014

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本論文では,関係データ予測におけるデータ過疎の問題を解決するために,複数の情報源からの関係データを統合する予測手法を提案する.提案手法では複数種類の関係データをそれぞれハイパーグラフにおける接続行列に変換し,非線形写像を適用する.現実のデータセットを用いた実験により,提案手法が一種類の関係データに基づく既存手法や複数種類のテンソル同時分解などを上回る予測精度を持つことを示す.