記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

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担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

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記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）  

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記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

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掲載種別：研究論文（国際会議プロシーディングス）  

Understanding how people's interest and emotional state change in response to news coverage of a particular topic and elucidating the characteristics of these changes can reveal the shifting nature of attention and emotion. We analyzed people's interest and emotional responses expressed via Twitter in response to news coverage of announcements of new cases of coronavirus disease 2019 (COVID-19) as a case study. As a measure of interest, we examined replies to tweets of news items posted by media outlets on Twitter, and classified the emotional content of each reply tweet using Plutchik's wheel of emotion. The analysis suggested that people were most interested in COVID-19 case numbers in April 2020, when the first wave of cases occurred and the first emergency declaration was issued, and in July 2020, when the second wave of cases emerged. The results revealed that fear was the most commonly expressed emotion. The ratio of fear-related tweets was highest in February and March 2020, a time at which new COVID-19 cases were confirmed in various locations and there was substantial public discussion regarding whether Japan would declare a state of emergency for the first time.

DOI： 10.1109/SCISISIS55246.2022.10001983

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記述言語：日本語   出版者・発行元：日本知能情報ファジィ学会  

新型コロナウイルスワクチンの開発会社や政府は，人々にワクチン接種への安心感を与えるために，ワクチンの効果や接種状況といった情報を日々発信している．しかし，ワクチンに対する関心やワクチンの接種状況は国や地域によって様々であるため，必ずしも人々に安心を感じてもらえないことがある．本稿では，Twitter上に投稿されたツイートを解析し，人々が新型コロナウイルスワクチンに対して持つ感情とその感情が表れる要因を分析した．日本，米国，英国，カナダ，オーストラリア，インドの6カ国を対象とし，プルチックの感情の輪で定義されている8種類の感情に基づいた機械学習による感情分類，および係り受け解析とバースト検知手法によるテキスト解析アプローチを適用した．感情分類の結果において，人々が持つ一般的な感情として，日本では恐れ，米国，英国，カナダ，オーストラリアでは怒りと嫌悪，インドでは喜びが表れていた．また，感情の時系列的変化において，バースト検出された係り受け関係に基づいて，特定の感情が盛り上がった期間におけるツイートを分析したところ，多くのユーザによりワクチン関連のニュースが投稿されたこと，1人のユーザにより同一内容のツイートが大量に投稿されたこと，ワクチンに関する同一の出来事でも個人の状況に応じて異なる感情が盛り上がる場合があるといったいくつかの特徴を発見した．

DOI： 10.3156/jsoft.34.3_592

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掲載種別：研究論文（国際会議プロシーディングス）  

Researchers and developers search for patents in fields related to their own research to obtain information on issues and effective technologies in those fields for use in their research. However, it is impossible to read through the full text of many patents, so a method that enables patent information to be grasped briefly is needed. In this study, we analyze the structure of U.S. patents with the aim of extracting important information. Using Japanese patents with structural tags such as "field", "problem", "solution", and "effect", and corresponding U.S. patents (patent families), we automatically created a dataset of 81,405 U.S. patents with structural tags. Furthermore, using this dataset, we conduct an experiment to assign structural tags to each sentence in the U. S. patents automatically. For the embedding layer, we use a language representation model, Bidirectional Encoder Representations from Transformer, pretrained on patent documents and construct a multi-label classifier that classifies a given sentence into one of four categories: "field", "problem", "solution", or "effect". Using a loss function that considers the unbalanced amount of data for each structural tag, we are able to classify sentences related to "field", "problem", "solution", and "effect"with precision of 0.6994, recall of 0.8291, and F-measure of 0.7426.

DOI： 10.1109/IIAIAAI55812.2022.00038

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掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1109/IIAIAAI55812.2022.00024

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その他リンク： https://dblp.uni-trier.de/db/conf/iiaiaai/iiaiaai2022.html#FukudaNS22

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掲載種別：研究論文（学術雑誌）  

This paper presents results of experiments in which annotators were asked to selectively reexamine their decisions when those decisions seemed inconsistent. The annotation task was binary topic classification. To operationalize the concept of annotation consistency, a text classifier was trained on all manual annotations made during a complete first pass and then used to automatically recode every document. Annotators were then asked to perform a second manual pass, limiting their attention to cases in which their first annotation disagreed with the text classifier. On average across three annotators, each working independently, 11% of first pass annotations were reconsidered, 46% of reconsidered annotations were changed in the second pass, and 71% of changed annotations agreed with decisions made independently by an experienced fourth annotator. The net result was that for an 11% average increase in annotation cost it was possible to increase overall chance corrected agreement with the annotation decisions of an experienced annotator (as measured by kappa) from 0.70 to 0.75.

DOI： 10.1002/pra2.301

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記述言語：日本語  

In an academic paper search, it is important that the search returns comprehensive results that are relevant to the user's information need by creating a Boolean query. However, it is difficult to anticipate all possible terms that authors of relevant papers might have used. We propose a Boolean-based search method based on topic analysis using latent Dirichlet allocation. Our method considers synonyms and expressions similar to the search terms, which a user might not anticipate. The sets retrieved by our method and by general ranking method are different because the purpose of the ranking method is to high rank the relevant papers and our method is focused on comprehensively collecting relevant papers. Therefore, it is able to that more high performance search result could be obtained by combining both sets.

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掲載種別：研究論文（国際会議プロシーディングス）  

When conducting a search for research papers, the search should return comprehensive results related to the user’s query. In general, a user inputs a Boolean query that reflects the information need, and the search engine ranks the research papers based on the query. However, it is difficult to anticipate all possible terms that authors of relevant papers might have used. Moreover, general query-based ranking methods emphasize how to rank the relevant documents at the top of the results, but require some means of guaranteeing the comprehensiveness of the results. Therefore, two ranking methods that consider the comprehensiveness of relevant papers are proposed. The first uses a topic-based Boolean query search. This search converts every word in the abstract set and query into a topic via topic analysis by Latent Dirichlet Allocation (LDA) and conducts a search at the topic level. The topic assigned to synonyms of a search term is expected to be the same as that assigned to the search term. Each paper is ranked based on the number of times it is matched with each topic-based Boolean query search executed for various LDA parameter settings. The second is a hybrid method that emphasizes better results from our topic-based ranking result and a general query-based ranking result. This method is based on the observation that the paper sets retrieved by our method and by a general ranking method will be different. Through experiments using the NTCIR-1 and -2 datasets, the effectiveness of our topic-based and hybrid methods are demonstrated.

DOI： 10.1007/978-3-030-27618-8_5

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掲載種別：研究論文（国際会議プロシーディングス）  

Prior work on automated annotation of human values has sought to train text classification techniques to label text spans with labels that reflect specific human values such as freedom, justice, or safety. This confounds three tasks: (1) selecting the documents to be labeled, (2) selecting the text spans that express or reflect human values, and (3) assigning labels to those spans. This paper proposes a three-stage model in which separate systems can be optimally trained for each of the three stages. Experiments from the first stage, document selection, indicate that annotation diversity trumps annotation quality, suggesting that when multiple annotators are available, the traditional practice of adjudicating conflicting annotations of the same documents is not as cost effective as an alternative in which each annotator labels different documents. Preliminary results for the second stage, selecting value sentences, indicate that high recall (94%) can be achieved on that task with levels of precision (above 80%) that seem suitable for use as part of a multi-stage annotation pipeline. The annotations created for these experiments are being made freely available, and the content that was annotated is available from commercial sources at modest cost.

DOI： 10.1007/978-3-030-15742-5_18

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掲載種別：研究論文（国際会議プロシーディングス）  

Archives hold printed historical documents, many of which have deteriorated. It is difficult to extract text from such images without errors using optical character recognition (OCR). This problem reduces the accuracy of information retrieval. Therefore, it is necessary to improve the performance of OCR for images of deteriorated documents. One approach is to convert images of deteriorated documents to clear images, to make it easier for an OCR system to recognize text. To perform this conversion using a neural network, data is needed to train it. It is hard to prepare training data consisting of pairs of a deteriorated image and an image from which deterioration has been removed; however, it is easy to prepare training data consisting of pairs of a clear image and an image created by adding noise to it. In this study, PDFs of historical documents were collected and converted to text and JPEG images. Noise was added to the JPEG images to create a dataset in which the images had noise similar to that of the actual printed documents. U-Net, a type of neural network, was trained using this dataset. The performance of OCR for an image with noise in the test data was compared with the performance of OCR for an image generated from it by the trained U-Net. An improvement in the OCR recognition rate was confirmed.

DOI： 10.1007/978-3-030-34058-2_31

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記述言語：英語  

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記述言語：英語  

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記述言語：英語  

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掲載種別：研究論文（国際会議プロシーディングス）  

In an academic paper search to confirm the originality of a user's research, it is important that the search returns comprehensive results relevant to the user's information need. To achieve comprehensive search results, users often relax initially restrictive search formula by adding synonyms and expressions similar to the search words with operator OR, and/or replacing AND with OR operations. However, it is difficult to anticipate all the terms that authors of relevant papers might have used. In addition, the replacement of AND with OR in search phrases can return a large number of unrelated papers. To overcome these issues, we propose a research paper search method based on topic analysis, which uses Boolean search based on the topics assigned to the search words in the search formula and the abstracts that contain any search word. Our method considers synonyms and expressions similar to the search words, which a user might not anticipate, while limiting the number of papers unrelated to the information need in the search result. To investigate the effectiveness of our method, we conducted experiments using the NTCIR-1 and 2 datasets, and confirmed that our method shows a reduction effect on unrelated papers, while maintaining high coverage.

DOI： 10.1109/IALP.2017.8300606

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掲載種別：研究論文（国際会議プロシーディングス）  

We are investigating ways of evaluating research impact as soon as possible after publication. Traditionally, the research impact or importance of academic journals has been evaluated using citation relations, such as the impact factor and the citation half-life. However, these citation-based methods require long periods to evaluate research impact and therefore are not suitable for evaluating the current impact of research papers at conferences. To solve this problem, we are studying the automatic evaluation of research impact using Twitter. Researchers participating in academic conferences often post their opinions or comments on Twitter. Here, research papers (presentations) that have many comments are considered to be outstanding and to have strong impact during the conference. In this paper, we propose a method for automatically aligning tweets with research papers. The procedure consists of the following three steps: (1) detecting valuable tweets, (2) aligning each valuable tweet with a research paper, and (3) calculating the research impact of each research paper by the number of aligned tweets. We conducted some experiments to confirm the effectiveness of our method. From the results, we obtained an MRR score of 0.223, which outperformed a baseline method.

DOI： 10.1109/DEXA.2015.64

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記述言語：日本語   出版者・発行元：日本図書館情報学会  

本研究では，科学研究費助成事業データベース（KAKEN）の分類体系に基づいて，学術論文を機械学習により横断的に分類する手法を提案する。これまで，論文を効率的に分類するための情報として，研究者名や雑誌名などが用いられてきた。我々は，これらの情報に加え，論文固有の特徴表現である要素技術とその効果に着目する。一般に，論文には，新しい技術（要素技術）を用いて得られた新たな研究成果（効果）が記述されている。このような研究動向を示す表現は，特定の研究分野の特徴を表す重要な手掛かりになる。提案手法の有効性を検証するために，KAKEN の研究課題データとCiNii articles の論文データを用いて実験を行った。そして，KAKEN の分類体系である「分野・分科・細目表」を対象とした時，それぞれ平均0.853，0.712，0.615 の分類精度が得られた。これらの値は，要素技術とその効果に関する表現を用いない場合より高いことから，本手法の有効性が確認された。

DOI： 10.20651/jslis.62.3_145

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掲載種別：研究論文（国際会議プロシーディングス）  

In scientometrics and citation analysis, several measures for evaluating the industrial relevance or the impact of academic research fields have been proposed. What seems to be lacking, however, is that these measures could not evaluate the recent industrial relevance or impact of each field, because most of them rely on citations of research papers in patents and vice versa. In this paper, we attempt to evaluate the industrial and social impact of research fields using document classification techniques. Our method classifies research papers and news articles using systems including the International Patent Classification (IPC) and the KAKEN classification index. Then it evaluates the industrial and social impact of each field by comparing the number of research papers with the number of patents or articles in the IPC categories and the projects funded in each KAKEN category.

DOI： 10.1109/IIAI-AAI.2014.29

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記述言語：日本語   出版者・発行元：情報処理学会  

産業と関連性が高い分野の研究者にとって，論文や特許などの技術文書を検索・分析することは，その分野の動向を知るうえで重要である．本研究では，このような作業を支援するため，論文と特許から技術動向に関する情報を抽出し，マップを自動作成して可視化する手法を提案する．技術動向マップの構築には，特定分野において使用された基礎的な要素技術とその効果に着目する．このような要素技術とその効果の変遷を知ることは，その分野における技術動向のあらましを把握する重要な情報となる．そこで本研究では，様々な研究分野における要素技術とその効果に関する表現を自動的に抽出するための手法を提案する．本研究の有効性を確かめるために，NTCIR-8特許マイニングタスクで提供されたデータを用いて実験を行った結果，本研究で提案した手法において，論文解析では再現率0.254，精度0.496が，特許解析では再現率0.441，精度0.537が得られた．
For a researcher in a field with high industrial relevance, retrieving and analyzing research papers and patents have become an important aspect of assessing the scope of the field. We propose a method for creating a technical trend map automatically from both research papers and patents. For the construction of the technical trend map, we focus on the elemental (underlying) technologies used in a particular field, and their effects. Knowledge of the history and effects of the elemental technologies used in a particular field is important for grasping the outline of technical trends in the field. Therefore, we have constructed a method that can recognize the application of elemental technologies and their effects in any research field. To investigate the effectiveness of our method, we conducted an experiment using the data in the NTCIR-8 Patent Mining Task. From our experimental results, we obtained recall and precision scores of 0.254 and 0.496, respectively, for the analysis of research papers. We also obtained recall and precision scores of 0.441 and 0.537, respectively, for the analysis of patents.

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その他リンク： http://id.ndl.go.jp/bib/024421636

掲載種別：研究論文（学術雑誌）  

To a researcher in a field with high industrial relevance, retrieving and analyzing research papers and patents are important aspects of assessing the scope of the field. Knowledge of the history and effects of the elemental technologies is important for understanding trends. We propose a method for automatically creating a technical trend map from both research papers and patents by focusing on the elemental (underlying) technologies and their effects. We constructed a method that can be used in any research field. To investigate the effectiveness of our method, we conducted an experiment using the data in the NTCIR-8 Workshop Patent Mining Task. The results of our experiment showed recall and precision scores of 0.254 and 0.496, respectively, for the analysis of research papers, and recall and precision scores of 0.455 and 0.507, respectively, for the analysis of patents. Those results indicate that our method for mapping technical trends is both useful and sound. © 2012 Satoshi Fukuda, Hidetsugu Nanba and Toshiyuki Takezawa.

DOI： 10.1045/july2012-fukuda

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