著者

長野 匡隼 中村 友昭 長井 隆行 持橋 大地 小林 一郎 高野 渉

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会全国大会論文集 第33回全国大会(2019)

巻号頁・発行日

pp.1L3J1101, 2019 (Released:2019-06-01)

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人は知覚した高次元の時系列情報を意味を持つ単語や単位動作に分節・分類することで認識している.ロボットが単語や動作を柔軟に学習するためにも,このような教師なしで分節・分類する能力は重要であると考えられる.本稿では教師なしで高次元の時系列データから特徴抽出すると同時に,単位系列に分節・分類が可能なHierarchical Dirichlet Processes-Variational Autoencoder-Gaussian Process-Hidden Semi-Markov Model (HVGH)を提案する.HVGHは,HDP-GP-HSMMにVariational Autoencoder(VAE)を導入したモデルであり,VAEとHDP-GP-HSMMのパラメータが相互に影響しあい学習される.VAEにより高次元データを分節化に適した低次元の潜在変数へと圧縮し,その潜在変数の遷移をガウス過程を用いて表現することで,高次元の複雑な時系列データの分節化を可能とする.実験では,様々なモーションキャプチャデータを用いて,提案手法が既存手法よりクラス数の推定精度及び分節・分類の精度が高いことが示す.

著者

三村 喬生 中村 友昭 松本 惇平 西条 寿夫 須原 哲也 持橋 大地 南本 敬史

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会全国大会論文集 第33回全国大会(2019)

巻号頁・発行日

pp.1C4J301, 2019 (Released:2019-06-01)

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非ヒト霊長類など社会集団を構成する動物種においても広く観察される視線・表情・姿勢・動作などの身体表現を用いた非言語表現は、社会的コミュニケーションの本質的要素を成していると考えられるが、有効な定量解析技術がなく、コミュニケーションダイナミクスの理解において課題となっている。本研究では、身体表現を高解像度かつ汎用的に解析する手法の開発および実装として、小型霊長類コモン・マーモセットの典型的な摂餌行動を対象とし、ログデータを取得と身体動作時系列の分節推移構造推定を行った。データ取得には深度カメラとオ ブジェクト検出器を組み合わせた新規のマーカーレス・3 次元 モーショントラック技術を開発・実装し身体部位のトラッキン グ情報を抽出した。分節推移構造の推定には、ガウス過程の導入により多次元連続量を取り扱える拡張を施した隠れセミマルコフモデルを用いた。結果、マーモセット行動エソグラムの高解像度な分離を得たことから、提案手法は疾患モデル動物の病態評価など幅広い応用が期待される。

著者

酒井 浩之 西沢 裕子 松並 祥吾 坂地 泰紀

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.1, pp.172-182, 2015-01-06 (Released:2015-01-06)

参考文献数

13

被引用文献数

4

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In this paper, we propose a method of extracting causal information from PDF files of the summary of financial statements of companies, e.g., ''The sales of smart phones was expanded continually''. Cause information is useful for investors in selecting companies to invest. We downloaded 106,885 PDF files of the summary of financial statements of companies from Web pages of the companies automatically. Our method extracts causal information from the PDF files by using clue expressions (e.g., ''was expanded'') and keywords relevant to a company. The clue expressions are extracted from the PDF files of the summary of financial statements of companies and articles concerning business performance of companies automatically. We developed the search system which is able to retrieve causal informations extracted by our method. The search system shows causal information containing a keyword inputted by users, and the summary of financial statements containing the retrieved causal information. We evaluated our method and it attained 83.91% precision and 55.04% recall, respectively. Moreover, we compared our method with Sakai et al's method originally proposed for extracting causal information from financial articles concerning business performance of companies and experimental results showed that our method outperforms Sakai et al's method.

著者

伊集 竜之 遠藤 聡志 山田 孝治 當間 愛晃 赤嶺 有平

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会全国大会論文集

巻号頁・発行日

vol.2015, pp.1M45, 2015

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<p>Twitterを利用するユーザの年齢層を推定する場合、ライフスタイルによってツイートの投稿時間帯が異なるため、投稿時間帯が推定に有効な情報の一つと考えられる。この情報を活用する場合、同年齢層内で複数のライフスタイルが存在することを考慮すべきである。そこで、本研究では各年齢層内で期間毎の投稿率を素性としたユーザクラスタを作成し、作成したクラスタを基礎とする推定手法の提案を行う。</p>

著者

池田 大介 高村 大也 奥村 学

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.25, no.1, pp.50-57, 2010 (Released:2010-01-06)

参考文献数

23

被引用文献数

2 6

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We propose a machine learning based method of sentiment classification of sentences using word-level polarity. The polarities of words in a sentence are not always the same as that of the sentence, because there can be polarity-shifters such as negation expressions. The proposed method models the polarity-shifters. Our model can be trained in two different ways: word-wise and sentence-wise learning. In sentence-wise learning, the model can be trained so that the prediction of sentence polarities should be accurate. The model can also combined with features used in previous work such as bag-of-words and n-grams. We empirically show that our method improves the performance of sentiment classification of sentences especially when we have only small amount of training data.

著者

椿 真史 新保 仁 松本 裕治

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.31, no.2, pp.O-FA2_1-10, 2016-03-01 (Released:2016-06-09)

参考文献数

40

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The notion of semantic similarity between text data (e.g., words, phrases, sentences, and documents) plays an important role in natural language processing (NLP) applications such as information retrieval, classification, and extraction. Recently, word vector spaces using distributional and distributed models have become popular. Although word vectors provide good similarity measures between words, phrasal and sentential similarities derived from composition of individual words remain as a difficult problem. To solve the problem, we focus on representing and learning the semantic similarity of sentences in a space that has a higher representational power than the underlying word vector space. In this paper, we propose a new method of non-linear similarity learning for compositionality. With this method, word representations are learnedthrough the similarity learning of sentences in a high-dimensional space with implicit kernel functions, and we can obtain new word epresentations inexpensively without explicit computation of sentence vectors in the high-dimensional space. In addition, note that our approach differs from that of deep learning such as recursive neural networks (RNNs) and long short-term memory (LSTM). Our aim is to design a word representation learning which combines the embedding sentence structures in a low-dimensional space (i.e., neural networks) with non-linear similarity learning for the sentence semantics in a high-dimensional space (i.e., kernel methods). On the task of predicting the semantic similarity of two sentences (SemEval 2014, task 1), our method outperforms linear baselines, feature engineering approaches, RNNs, and achieve competitive results with various LSTM models.

著者

宮崎 勝 藤沢 寛 中川 俊夫 岡田 将吾 新田 克己

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.2, pp.429-439, 2015-03-01 (Released:2015-01-30)

参考文献数

17

被引用文献数

1

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The spread of Video on Demand (VOD) services is driving the creation of video viewing environments in which users can watch whatever they like, whenever they like. The recent appearance of social networking services (SNSs), moreover, is bringing big changes to the world of media by enabling anyone to become a disseminator of information. We are studying a platform that combines VOD and SNS to create ``horizontal links'' between program viewers, and to facilitate encounters with new programs. To investigate user behaviors on this platform, we built an SNS site called ``teleda,'' that enables program viewing by VOD, and conducted a large-scale, three-month verification trial with about 1000 participants. In this paper, we report on the relational analysis of teleda users' viewing and communication behaviors. In order to clarify how the users' communication structures relate to the viewing and posting behaviors on this system, we described the communication structures in terms of network structures, and ran a correlation analysis of network indicators and user behavior indicators such as viewing and posting. As a result, we revealed that relationships between the users' communication structures, viewing behaviors, and posting actions are characteristics of each program's genre.

著者

小野 裕作 當間 愛晃 遠藤 聡志

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会全国大会論文集 第22回全国大会(2008)

巻号頁・発行日

pp.269, 2008 (Released:2009-07-31)

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ソーシャルブックマークの問題点として、タグの表記揺れやタグ付け自体に労力がかかる、などがある。 本研究はこれらを解決するために、ユーザーの履歴を利用してタグ付けを自動化するシステムの開発を目的とする。

著者

末吉 優 関 洋平

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

巻号頁・発行日

vol.32, no.1, pp.WII-K_1-12, 2017

被引用文献数

1

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<p>This paper proposes the following methods to search VOCALOID creators who publish music videos in Niconico video hosting service. For VOCALOID creator search, the user can utilize three clues: VOCALOID character name, music genre, and impressions. We defined the music genre by extending generic digital music genre with considering social tags annotated on VOCALOID music videos. We also implemented SVM-based music impression estimator utilizing viewer comments being over 0.8 points in F-values. We compared the proposal with three comparison methods in 12 search tasks and clarified the effectiveness of music genres and impressions.</p>

著者

ジメネス フェリックス 加納 政芳 吉川 大弘 古橋 武

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.31, no.3, pp.A-F93_1-10, 2016-05-01 (Released:2016-05-13)

参考文献数

33

被引用文献数

3

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This paper reports feasibility of collaborative learning with educational-support robots between human. We designed a robot to prompt the constructive interaction. Constructive interaction has been regarded as a foundation of collaborative learning. Constructive interaction occurs when two students are solving the same question. Therefore, the robot is designed that alternately perform the speaker motion and listener motion to occur constructive interaction with human.In the speaker motion, the robot explains a solving method to the partner and solves a question. Moreover, the robot improves its accuracy rate as learning progress. In the listener motion, the robot does not solve a question and paying attention to a partner who is solving the questions. The robot learns while solving a question issued by learning system with a college student. The college students learned in the learning system with the robot for one month and were videoed during that time to see how they learned. This results of the study suggest that robot, which alternately solves the question with a human and improves its accuracy rate as learning progress, prompts learners to learning by constructive interaction with robot in collaborative learning. This constructive interaction indicates that learners alternately solve the question with robot and listen to robot's speaking. However, learners interest in robot decrease when robot improves its accuracy rate at 100% and performs the same action. Additionally, the screen agent, which is designed same action as robot, does not prompt some learners to learning by constructive interaction because they feel lousy that screen agent could not solve the question correctly, so they ignore what the screen agent says. The same situation was occurred in some learners learning with robot

著者

吉田 光男 荒瀬 由紀

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.1, pp.161-171, 2015-01-06 (Released:2015-01-06)

参考文献数

27

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Query classification is an important technique for web search engines, allowing them to improve users' search experience. Specifically, query classification methods classify queries according to topical categories, such as celebrities and sports. Such category information is effective in improving web search results, online advertisements, and so on. Unlike previous studies, our research focuses on trend queries that have suddenly become popular and are extensively searched. Our aim is to classify such trend queries in a timely manner, i.e., classify the queries on the same day when they become popular, in order to provide a better search experience. To reduce the expensive manual annotation costs to train supervised learning methods, we focus on a label propagation method that belongs to the semi-supervised learning family. Specifically, the proposed method is based on our previous method that constructs a graph using a corpus, and propagates a small number of ground-truth categories of labeled queries in order to estimate the categories of unlabeled queries. We extend this method to cut ineffective edges to improve both classification accuracy and computational efficiency. Furthermore, we investigate in detail the effects of different corpora, i.e., web/blog/news search results, Tweets, and news pages, on the trend query classification task. Our experiments replicate the situation of an emerging trend query; the results show that web search results are the most effective for trend query classification, achieving a 50.1% F-score, which significantly outperforms the state-of-the-art method by 7.2 points. These results provide useful insights into selecting an appropriate dataset for query classification from the various types of data available.

著者

前田 安里紗 上間 大生 白水 菜々重 松下 光範

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.1, pp.204-215, 2015-01-06 (Released:2015-01-06)

参考文献数

18

被引用文献数

2

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The objective of this study is to support learning of Japanese onomatopoeia for foreigners who learn Japanese. In recent years, the number of such foreigners is increasing. There are a lot of onomatopoeia words in Japanese and many of them are difficult to translate because only the number of onomatopoeia words in foreign languages (e.g., Mandarin, Cantonese) are fewer than these in Japanese. To overcome the cultural difference, this paper proposes a digital picture book system for learning Japanese onomatopoeia. The system presents 32 onomatopoeia words to a user. The design criteria of the system is that: (1) adopts an interface of user participation, (2) presents a tiny story in which onomatopoeic words are associated with pictures, and (3) enables comparison of two synonymous/antomynic onomatopoeias. We conducted a user study with foreign learners and revealed that the proposed system improves understanding of semasiological differences between two confusing onomatopoeic words.

著者

荒堀 拓哉 片上 大輔 角所 考

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.179-186, 2013 (Released:2013-02-01)

参考文献数

17

被引用文献数

1

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In this research, we develop the operation information dissemination agent "smart caster 24 (TWENTY FOUR)" for the purpose of the design of the expressional model as a life-like agent which enables to watch for a long time. The smart caster 24 is a life-like agent who disseminates news information on the Internet in the form of human newscaster. We analyzed total of 90 news reading motions of 18 human active newscasters and designed the nonverbal expression model for the smart caster. In the results of news reading experiments, we found that the smart caster 24 presented same impression with human newscasters in some evaluation items.

著者

梶野 洸 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.27, no.3, pp.133-142, 2012 (Released:2012-03-27)

参考文献数

16

被引用文献数

18 32

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It has attracted considerable attention to use crowdsourcing services to collect a large amount of labeled data for machine learning, since crowdsourcing services allow one to ask the general public to label data at very low cost through the Internet. The use of crowdsourcing has introduced a new challenge in machine learning, that is, coping with low quality of crowd-generated data. There have been many recent attempts to address the quality problem of multiple labelers, however, there are two serious drawbacks in the existing approaches, that are, (i) non-convexity and (ii) task homogeneity. Most of the existing methods consider true labels as latent variables, which results in non-convex optimization problems. Also, the existing models assume only single homogeneous tasks, while in realistic situations, clients can offer multiple tasks to crowds and crowd workers can work on different tasks in parallel. In this paper, we propose a convex optimization formulation of learning from crowds by introducing personal models of individual crowds without estimating true labels. We further extend the proposed model to multi-task learning based on the resemblance between the proposed formulation and that for an existing multi-task learning model. We also devise efficient iterative methods for solving the convex optimization problems by exploiting conditional independence structures in multiple classifiers.

著者

谷口 忠大

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.1, pp.77-87, 2013 (Released:2013-01-05)

参考文献数

12

被引用文献数

1

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In this paper, we propose a new learning model for decentralized autonomous smart grid involving adaptive trading agents which can sell and buy electric power effectively in a local electric power network. We name the electric power network i-Rene (inter intelligent renewable energy network). The trading agents manage the amount of electric power generated by solar panels or other renewable energies by trading electric power stored in a storage battery in a house. The agent learns a trading strategy by maximizing its utility. Based on the proposed system, we evaluated its price formation and effectiveness of the adaptive trading method through simulations. Additionally, we propose a new variable consumption model for decentralized autonomous smart grid involving living people consuming electric power and the adaptive trading agents. To model demand side management which can control the amount of electric power consumption, developing variable consumption model is essential. We added a variable consumption model to the i-Rene model. We evaluated its price formation and effectiveness of the decentralized autonomous smart grid to equalize fluctuating demand.

著者

野口 尚孝 Hisataka Noguchi

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence (ISSN:09128085)

巻号頁・発行日

vol.20, no.4, pp.379-386, 2005-07-01

参考文献数

12

著者

中島 秀之

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.21, no.6, pp.502-513, 2006 (Released:2006-09-14)

参考文献数

45

被引用文献数

3 5

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As computer scientists, we have been trained in the methodology of natural science, which is analytic in its essence. Informatics, and particularly Artificial Intelligence, is not an analytic discipline. It is required to establish a constructive methodology.

著者

柳瀬 利彦 廣木 桂一 伊藤 昭博 柳井 孝介

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.5, pp.621-637, 2011 (Released:2011-07-20)

参考文献数

34

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We propose a computing platform for parallel machine learning. Learning from large-scale data has become common, so that parallelization techniques are increasingly applied to machine learning algorithms in order to reduce calculation time. Problems of parallelization are implementation costs and calculation overheads. Firstly, we formulate MapReduce programming model specialized in parallel machine learning. It represents learning algorithms as iterations of following two phases: applying data to machine learning models and updating model parameters. This model is able to describe various kinds of machine learning algorithms, such as k-means clustering, EM algorithm, and linear SVM, with comparable implementation cost to the original MapReduce. Secondly, we propose a fast machine learning platform which reduces the processing overheads at iterative procedures of machine learning. Machine learning algorithms iteratively read the same training data in the data application phase. Our platform keeps the training data in local memories of each worker during iterative procedures, which leads to acceleration of data access. We evaluate performance of our platform on three experiments. Our platform executes k-means clustering 2.85 to 118 times faster than the MapReduce approach, and shows 9.51 times speedup with 40 processing cores against 8 cores. We also show the performance of Variational Bayes clustering and linear SVM implemented on our platform.

著者

中崎 寛之 川場 真理子 横本 大輔 宇津呂 武仁 福原 知宏

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.25, no.5, pp.613-622, 2010 (Released:2010-08-06)

参考文献数

12

被引用文献数

1

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The overall goal of this paper is to cross-lingually analyze multilingual blogs collected with a topic keyword. The framework of collecting multilingual blogs with a topic keyword is designed as the blog feed retrieval procedure. In this paper, we take an approach of collecting blog feeds rather than blog posts, mainly because we regard the former as a larger information unit in the blogosphere and prefer it as the information source for cross-lingual blog analysis. In the blog feed retrieval procedure, we also regard Wikipedia as a large scale ontological knowledge base for conceptually indexing the blogosphere. The underlying motivation of employing Wikipedia is in linking a knowledge base of well known facts and relatively neutral opinions with rather raw, user generated media like blogs, which include less well known facts and much more radical opinions. In our framework, first, in order to collect candidates of blog feeds for a given query, we use existing Web search engine APIs, which return a ranked list of blog posts, given a topic keyword. Next, we re-rank the list of blog feeds according to the number of hits of the topic keyword as well as closely related terms extracted from the Wikipedia entry in each blog feed. We compare the proposed blog feed retrieval method to existing Web search engine APIs and achieve significant improvement. We then apply the proposed blog distillation framework to the task of cross-lingually analyzing multilingual blogs collected with a topic keyword. Here, we cross-lingually and cross-culturally compare less well known facts and opinions that are closely related to a given topic. Results of cross-lingual blog analysis support the effectiveness of the proposed framework.

著者

高野 雅典 高 史明 荻上 チキ 永田 夏来

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会全国大会論文集 第37回 (2023) (ISSN:27587347)

巻号頁・発行日

pp.2H1OS3a03, 2023 (Released:2023-07-10)

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著名人やインフルエンサーのネットハラスメント被害が問題になっている。ネットハラスメントは、著名人/インフルエンサーを精神的に苦しめ、社会にも悪影響を及ぼす。一方で著名人・インフルエンサーのネットハラスメント被害に関する研究は限られており、その影響も明らかでない。本研究では実態と課題を明らかにすることを目的として、日本の著名人・インフルエンサー(N=213)を対象に、ネットハラスメント被害、精神的ダメージ、加害者への対応についての調査を行った。本発表では、調査結果を報告し、課題解決のためのアクションについて考察する。