Fairness-aware Classifier with Prejudice Remover RegularizerToshihiro Kamishima
Fairness-aware Classifier with Prejudice Remover Regularizer
Proceedings of the European Conference on Machine Learning and Principles of Knowledge Discovery in Databases (ECMLPKDD), Part II, pp.35-50 (2012)
Article @ Official Site: http://dx.doi.org/10.1007/978-3-642-33486-3_3
Article @ Personal Site: http://www.kamishima.net/archive/2012-p-ecmlpkdd-print.pdf
Handnote: http://www.kamishima.net/archive/2012-p-ecmlpkdd-HN.pdf
Program codes : http://www.kamishima.net/fadm/
Conference Homepage: http://www.ecmlpkdd2012.net/
Abstract:
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. Several researchers have recently begun to attempt the development of analysis techniques that are aware of social fairness or discrimination. They have shown that simply avoiding the use of sensitive features is insufficient for eliminating biases in determinations, due to the indirect influence of sensitive information. In this paper, we first discuss three causes of unfairness in machine learning. We then propose a regularization approach that is applicable to any prediction algorithm with probabilistic discriminative models. We further apply this approach to logistic regression and empirically show its effectiveness and efficiency.
Consideration on Fairness-aware Data Mining
IEEE International Workshop on Discrimination and Privacy-Aware Data Mining (DPADM 2012)
Dec. 10, 2012 @ Brussels, Belgium, in conjunction with ICDM2012
Article @ Official Site: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2012.101
Article @ Personal Site: http://www.kamishima.net/archive/2012-ws-icdm-print.pdf
Handnote: http://www.kamishima.net/archive/2012-ws-icdm-HN.pdf
Workshop Homepage: https://sites.google.com/site/dpadm2012/
Abstract:
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair regarding sensitive features such as race, gender, religion, and so on. Several researchers have recently begun to develop fairness-aware or discrimination-aware data mining techniques that take into account issues of social fairness, discrimination, and neutrality. In this paper, after demonstrating the applications of these techniques, we explore the formal concepts of fairness and techniques for handling fairness in data mining. We then provide an integrated view of these concepts based on statistical independence. Finally, we discuss the relations between fairness-aware data mining and other research topics, such as privacy-preserving data mining or causal inference.
Fairness-aware Learning through Regularization Approach
The 3rd IEEE International Workshop on Privacy Aspects of Data Mining (PADM 2011)
Dec. 11, 2011 @ Vancouver, Canada, in conjunction with ICDM2011
Article @ Official Site: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2011.83
Article @ Personal Site: http://www.kamishima.net/archive/2011-ws-icdm_padm.pdf
Handnote: http://www.kamishima.net/archive/2011-ws-icdm_padm-HN.pdf
Workshop Homepage: http://www.zurich.ibm.com/padm2011/
Abstract:
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect people's lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be socially and legally fair from a viewpoint of social responsibility; namely, it must be unbiased and nondiscriminatory in sensitive features, such as race, gender, religion, and so on. Several researchers have recently begun to attempt the development of analysis techniques that are aware of social fairness or discrimination. They have shown that simply avoiding the use of sensitive features is insufficient for eliminating biases in determinations, due to the indirect influence of sensitive information. From a privacy-preserving viewpoint, this can be interpreted as hiding sensitive information when classification results are observed. In this paper, we first discuss three causes of unfairness in machine learning. We then propose a regularization approach that is applicable to any prediction algorithm with probabilistic discriminative models. We further apply this approach to logistic regression and empirically show its effectiveness and efficiency.
The Independence of Fairness-aware Classifiers
IEEE International Workshop on Privacy Aspects of Data Mining (PADM), in conjunction with ICDM2013
Article @ Official Site:
Article @ Personal Site: http://www.kamishima.net/archive/2013-ws-icdm-print.pdf
Handnote : http://www.kamishima.net/archive/2013-ws-icdm-HN.pdf
Program codes : http://www.kamishima.net/fadm/
Workshop Homepage: http://www.cs.cf.ac.uk/padm2013/
Abstract:
Due to the spread of data mining technologies, such technologies are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. The goal of fairness-aware classifiers is to classify data while taking into account the potential issues of fairness, discrimination, neutrality, and/or independence. In this paper, after reviewing fairness-aware classification methods, we focus on one such method, Calders and Verwer's two-naive-Bayes method. This method has been shown superior to the other classifiers in terms of fairness, which is formalized as the statistical independence between a class and a sensitive feature. However, the cause of the superiority is unclear, because it utilizes a somewhat heuristic post-processing technique rather than an explicitly formalized model. We clarify the cause by comparing this method with an alternative naive Bayes classifier, which is modified by a modeling technique called "hypothetical fair-factorization." This investigation reveals the theoretical background of the two-naive-Bayes method and its connections with other methods. Based on these findings, we develop another naive Bayes method with an "actual fair-factorization" technique and empirically show that this new method can achieve an equal level of fairness as that of the two-naive-Bayes classifier.
Correcting Popularity Bias by Enhancing Recommendation NeutralityToshihiro Kamishima
Correcting Popularity Bias by Enhancing Recommendation Neutrality on
The 8th ACM Conference on Recommender Systems, Poster
Article @ Official Site: http://ceur-ws.org/Vol-1247/
Article @ Personal Site: http://www.kamishima.net/archive/2014-po-recsys-print.pdf
Abstract:
In this paper, we attempt to correct a popularity bias, which is the tendency for popular items to be recommended more frequently, by enhancing recommendation neutrality. Recommendation neutrality involves excluding specified information from the prediction process of recommendation. This neutrality was formalized as the statistical independence between a recommendation result and the specified information, and we developed a recommendation algorithm that satisfies this independence constraint. We correct the popularity bias by enhancing neutrality with respect to information regarding whether candidate items are popular or not. We empirically show that a popularity bias in the predicted preference scores can be corrected.
Absolute and Relative Clustering
4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering (Multiclust 2013)
Aug. 11, 2013 @ Chicago, U.S.A, in conjunction with KDD2013
Article @ Official Site: http://dx.doi.org/10.1145/2501006.2501013
Article @ Personal Site: http://www.kamishima.net/archive/2013-ws-kdd-print.pdf
Handnote: http://www.kamishima.net/archive/2013-ws-kdd-HN.pdf
Workshop Homepage: http://cs.au.dk/research/research-areas/data-intensive-systems/projects/multiclust2013/
Abstract:
Research into (semi-)supervised clustering has been increasing. Supervised clustering aims to group similar data that are partially guided by the user's supervision. In this supervised clustering, there are many choices for formalization. For example, as a type of supervision, one can adopt labels of data points, must/cannot links, and so on. Given a real clustering task, such as grouping documents or image segmentation, users must confront the question ``How should we mathematically formalize our task?''To help answer this question, we propose the classification of real clusterings into absolute and relative clusterings, which are defined based on the relationship between the resultant partition and the data set to be clustered. This categorization can be exploited to choose a type of task formalization.
Enhancement of the Neutrality in Recommendation
Workshop on Human Decision Making in Recommender Systems, in conjunction with RecSys 2012
Article @ Official Site: http://ceur-ws.org/Vol-893/
Article @ Personal Site: http://www.kamishima.net/archive/2012-ws-recsys-print.pdf
Handnote : http://www.kamishima.net/archive/2012-ws-recsys-HN.pdf
Program codes : http://www.kamishima.net/inrs
Workshop Homepage: http://recex.ist.tugraz.at/RecSysWorkshop2012
Abstract:
This paper proposes an algorithm for making recommendation so that the neutrality toward the viewpoint specified by a user is enhanced. This algorithm is useful for avoiding to make decisions based on biased information. Such a problem is pointed out as the filter bubble, which is the influence in social decisions biased by a personalization technology. To provide such a recommendation, we assume that a user specifies a viewpoint toward which the user want to enforce the neutrality, because recommendation that is neutral from any information is no longer recommendation. Given such a target viewpoint, we implemented information neutral recommendation algorithm by introducing a penalty term to enforce the statistical independence between the target viewpoint and a preference score. We empirically show that our algorithm enhances the independence toward the specified viewpoint by and then demonstrate how sets of recommended items are changed.
Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, a...Toshihiro Kamishima
Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, and Theoretical Aspects
Invited Talk @ Workshop on Fairness, Accountability, and Transparency in Machine Learning
In conjunction with the ICML 2015 @ Lille, France, Jul. 11, 2015
Web Site: http://www.kamishima.net/fadm/
Handnote: http://www.kamishima.net/archive/2015-ws-icml-HN.pdf
The goal of fairness-aware data mining (FADM) is to analyze data while taking into account potential issues of fairness. In this talk, we will cover three topics in FADM:
1. Fairness in a Recommendation Context: In classification tasks, the term "fairness" is regarded as anti-discrimination. We will present other types of problems related to the fairness in a recommendation context.
2. What is Fairness: Most formal definitions of fairness have a connection with the notion of statistical independence. We will explore other types of formal fairness based on causality, agreement, and unfairness.
3. Theoretical Problems of FADM: After reviewing technical and theoretical open problems in the FADM literature, we will introduce the theory of the generalization bound in terms of accuracy as well as fairness.
Joint work with Jun Sakuma, Shotaro Akaho, and Hideki Asoh
Fairness-aware Classifier with Prejudice Remover RegularizerToshihiro Kamishima
Fairness-aware Classifier with Prejudice Remover Regularizer
Proceedings of the European Conference on Machine Learning and Principles of Knowledge Discovery in Databases (ECMLPKDD), Part II, pp.35-50 (2012)
Article @ Official Site: http://dx.doi.org/10.1007/978-3-642-33486-3_3
Article @ Personal Site: http://www.kamishima.net/archive/2012-p-ecmlpkdd-print.pdf
Handnote: http://www.kamishima.net/archive/2012-p-ecmlpkdd-HN.pdf
Program codes : http://www.kamishima.net/fadm/
Conference Homepage: http://www.ecmlpkdd2012.net/
Abstract:
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. Several researchers have recently begun to attempt the development of analysis techniques that are aware of social fairness or discrimination. They have shown that simply avoiding the use of sensitive features is insufficient for eliminating biases in determinations, due to the indirect influence of sensitive information. In this paper, we first discuss three causes of unfairness in machine learning. We then propose a regularization approach that is applicable to any prediction algorithm with probabilistic discriminative models. We further apply this approach to logistic regression and empirically show its effectiveness and efficiency.
Consideration on Fairness-aware Data Mining
IEEE International Workshop on Discrimination and Privacy-Aware Data Mining (DPADM 2012)
Dec. 10, 2012 @ Brussels, Belgium, in conjunction with ICDM2012
Article @ Official Site: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2012.101
Article @ Personal Site: http://www.kamishima.net/archive/2012-ws-icdm-print.pdf
Handnote: http://www.kamishima.net/archive/2012-ws-icdm-HN.pdf
Workshop Homepage: https://sites.google.com/site/dpadm2012/
Abstract:
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair regarding sensitive features such as race, gender, religion, and so on. Several researchers have recently begun to develop fairness-aware or discrimination-aware data mining techniques that take into account issues of social fairness, discrimination, and neutrality. In this paper, after demonstrating the applications of these techniques, we explore the formal concepts of fairness and techniques for handling fairness in data mining. We then provide an integrated view of these concepts based on statistical independence. Finally, we discuss the relations between fairness-aware data mining and other research topics, such as privacy-preserving data mining or causal inference.
Fairness-aware Learning through Regularization Approach
The 3rd IEEE International Workshop on Privacy Aspects of Data Mining (PADM 2011)
Dec. 11, 2011 @ Vancouver, Canada, in conjunction with ICDM2011
Article @ Official Site: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2011.83
Article @ Personal Site: http://www.kamishima.net/archive/2011-ws-icdm_padm.pdf
Handnote: http://www.kamishima.net/archive/2011-ws-icdm_padm-HN.pdf
Workshop Homepage: http://www.zurich.ibm.com/padm2011/
Abstract:
With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect people's lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be socially and legally fair from a viewpoint of social responsibility; namely, it must be unbiased and nondiscriminatory in sensitive features, such as race, gender, religion, and so on. Several researchers have recently begun to attempt the development of analysis techniques that are aware of social fairness or discrimination. They have shown that simply avoiding the use of sensitive features is insufficient for eliminating biases in determinations, due to the indirect influence of sensitive information. From a privacy-preserving viewpoint, this can be interpreted as hiding sensitive information when classification results are observed. In this paper, we first discuss three causes of unfairness in machine learning. We then propose a regularization approach that is applicable to any prediction algorithm with probabilistic discriminative models. We further apply this approach to logistic regression and empirically show its effectiveness and efficiency.
The Independence of Fairness-aware Classifiers
IEEE International Workshop on Privacy Aspects of Data Mining (PADM), in conjunction with ICDM2013
Article @ Official Site:
Article @ Personal Site: http://www.kamishima.net/archive/2013-ws-icdm-print.pdf
Handnote : http://www.kamishima.net/archive/2013-ws-icdm-HN.pdf
Program codes : http://www.kamishima.net/fadm/
Workshop Homepage: http://www.cs.cf.ac.uk/padm2013/
Abstract:
Due to the spread of data mining technologies, such technologies are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. The goal of fairness-aware classifiers is to classify data while taking into account the potential issues of fairness, discrimination, neutrality, and/or independence. In this paper, after reviewing fairness-aware classification methods, we focus on one such method, Calders and Verwer's two-naive-Bayes method. This method has been shown superior to the other classifiers in terms of fairness, which is formalized as the statistical independence between a class and a sensitive feature. However, the cause of the superiority is unclear, because it utilizes a somewhat heuristic post-processing technique rather than an explicitly formalized model. We clarify the cause by comparing this method with an alternative naive Bayes classifier, which is modified by a modeling technique called "hypothetical fair-factorization." This investigation reveals the theoretical background of the two-naive-Bayes method and its connections with other methods. Based on these findings, we develop another naive Bayes method with an "actual fair-factorization" technique and empirically show that this new method can achieve an equal level of fairness as that of the two-naive-Bayes classifier.
Correcting Popularity Bias by Enhancing Recommendation NeutralityToshihiro Kamishima
Correcting Popularity Bias by Enhancing Recommendation Neutrality on
The 8th ACM Conference on Recommender Systems, Poster
Article @ Official Site: http://ceur-ws.org/Vol-1247/
Article @ Personal Site: http://www.kamishima.net/archive/2014-po-recsys-print.pdf
Abstract:
In this paper, we attempt to correct a popularity bias, which is the tendency for popular items to be recommended more frequently, by enhancing recommendation neutrality. Recommendation neutrality involves excluding specified information from the prediction process of recommendation. This neutrality was formalized as the statistical independence between a recommendation result and the specified information, and we developed a recommendation algorithm that satisfies this independence constraint. We correct the popularity bias by enhancing neutrality with respect to information regarding whether candidate items are popular or not. We empirically show that a popularity bias in the predicted preference scores can be corrected.
Absolute and Relative Clustering
4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering (Multiclust 2013)
Aug. 11, 2013 @ Chicago, U.S.A, in conjunction with KDD2013
Article @ Official Site: http://dx.doi.org/10.1145/2501006.2501013
Article @ Personal Site: http://www.kamishima.net/archive/2013-ws-kdd-print.pdf
Handnote: http://www.kamishima.net/archive/2013-ws-kdd-HN.pdf
Workshop Homepage: http://cs.au.dk/research/research-areas/data-intensive-systems/projects/multiclust2013/
Abstract:
Research into (semi-)supervised clustering has been increasing. Supervised clustering aims to group similar data that are partially guided by the user's supervision. In this supervised clustering, there are many choices for formalization. For example, as a type of supervision, one can adopt labels of data points, must/cannot links, and so on. Given a real clustering task, such as grouping documents or image segmentation, users must confront the question ``How should we mathematically formalize our task?''To help answer this question, we propose the classification of real clusterings into absolute and relative clusterings, which are defined based on the relationship between the resultant partition and the data set to be clustered. This categorization can be exploited to choose a type of task formalization.
Enhancement of the Neutrality in Recommendation
Workshop on Human Decision Making in Recommender Systems, in conjunction with RecSys 2012
Article @ Official Site: http://ceur-ws.org/Vol-893/
Article @ Personal Site: http://www.kamishima.net/archive/2012-ws-recsys-print.pdf
Handnote : http://www.kamishima.net/archive/2012-ws-recsys-HN.pdf
Program codes : http://www.kamishima.net/inrs
Workshop Homepage: http://recex.ist.tugraz.at/RecSysWorkshop2012
Abstract:
This paper proposes an algorithm for making recommendation so that the neutrality toward the viewpoint specified by a user is enhanced. This algorithm is useful for avoiding to make decisions based on biased information. Such a problem is pointed out as the filter bubble, which is the influence in social decisions biased by a personalization technology. To provide such a recommendation, we assume that a user specifies a viewpoint toward which the user want to enforce the neutrality, because recommendation that is neutral from any information is no longer recommendation. Given such a target viewpoint, we implemented information neutral recommendation algorithm by introducing a penalty term to enforce the statistical independence between the target viewpoint and a preference score. We empirically show that our algorithm enhances the independence toward the specified viewpoint by and then demonstrate how sets of recommended items are changed.
Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, a...Toshihiro Kamishima
Future Directions of Fairness-Aware Data Mining: Recommendation, Causality, and Theoretical Aspects
Invited Talk @ Workshop on Fairness, Accountability, and Transparency in Machine Learning
In conjunction with the ICML 2015 @ Lille, France, Jul. 11, 2015
Web Site: http://www.kamishima.net/fadm/
Handnote: http://www.kamishima.net/archive/2015-ws-icml-HN.pdf
The goal of fairness-aware data mining (FADM) is to analyze data while taking into account potential issues of fairness. In this talk, we will cover three topics in FADM:
1. Fairness in a Recommendation Context: In classification tasks, the term "fairness" is regarded as anti-discrimination. We will present other types of problems related to the fairness in a recommendation context.
2. What is Fairness: Most formal definitions of fairness have a connection with the notion of statistical independence. We will explore other types of formal fairness based on causality, agreement, and unfairness.
3. Theoretical Problems of FADM: After reviewing technical and theoretical open problems in the FADM literature, we will introduce the theory of the generalization bound in terms of accuracy as well as fairness.
Joint work with Jun Sakuma, Shotaro Akaho, and Hideki Asoh
Efficiency Improvement of Neutrality-Enhanced RecommendationToshihiro Kamishima
Efficiency Improvement of Neutrality-Enhanced Recommendation
Workshop on Human Decision Making in Recommender Systems, in conjunction with RecSys 2013
Article @ Official Site: http://ceur-ws.org/Vol-1050/
Article @ Personal Site: http://www.kamishima.net/archive/2013-ws-recsys-print.pdf
Handnote : http://www.kamishima.net/archive/2013-ws-recsys-HN.pdf
Program codes : http://www.kamishima.net/inrs/
Workshop Homepage: http://recex.ist.tugraz.at/RecSysWorkshop/
Abstract:
This paper proposes an algorithm for making recommendations so that neutrality from a viewpoint specified by the user is enhanced. This algorithm is useful for avoiding decisions based on biased information. Such a problem is pointed out as the filter bubble, which is the influence in social decisions biased by personalization technologies. To provide a neutrality-enhanced recommendation, we must first assume that a user can specify a particular viewpoint from which the neutrality can be applied, because a recommendation that is neutral from all viewpoints is no longer a recommendation. Given such a target viewpoint, we implement an information-neutral recommendation algorithm by introducing a penalty term to enforce statistical independence between the target viewpoint and a rating. We empirically show that our algorithm enhances the independence from the specified viewpoint.
Model-based Approaches for Independence-Enhanced RecommendationToshihiro Kamishima
Model-based Approaches for Independence-Enhanced Recommendation
IEEE International Workshop on Privacy Aspects of Data Mining (PADM), in conjunction with ICDM2016
Article @ Official Site: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2016.0127
Workshop Homepage: http://pddm16.eurecat.org/
Abstract:
This paper studies a new approach to enhance recommendation independence. Such approaches are useful in ensuring adherence to laws and regulations, fair treatment of content providers, and exclusion of unwanted information. For example, recommendations that match an employer with a job applicant should not be based on socially sensitive information, such as gender or race, from the perspective of social fairness. An algorithm that could exclude the influence of such sensitive information would be useful in this case. We previously gave a formal definition of recommendation independence and proposed a method adopting a regularizer that imposes such an independence constraint. As no other options than this regularization approach have been put forward, we here propose a new model-based approach, which is based on a generative model that satisfies the constraint of recommendation independence. We apply this approach to a latent class model and empirically show that the model-based approach can enhance recommendation independence. Recommendation algorithms based on generative models, such as topic models, are important, because they have a flexible functionality that enables them to incorporate a wide variety of information types. Our new model-based approach will broaden the applications of independence-enhanced recommendation by integrating the functionality of generative models.
WSDM2016勉強会 https://atnd.org/events/74341
論文:Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior
著者:F. Kooti and K. Lerman and L. M. Aiello and M. Grbovic and N. Djuric
論文リンク: http://dx.doi.org/10.1145/2835776.2835831
Variational Template Machine for Data-to-Text Generationharmonylab
公開URL:https://openreview.net/forum?id=HkejNgBtPB
出典:Rong Ye, Wenxian Shi, Hao Zhou, Zhongyu Wei, Lei Li : Variational Template Machine for Data-to-Text Generation, 8th International Conference on Learning Representations(ICLR2020), Addis Ababa, Ethiopia (2020)
概要:Table形式の構造化データから文章を生成するタスク(Data-to-Text)において、Variational Auto Encoder(VAE)ベースの手法Variational Template Machine(VTM)を提案する論文です。Encoder-Decoderモデルを用いた既存のアプローチでは、生成文の多様性に欠けるという課題があります。本論文では多様な文章を生成するためにはテンプレートが重要であるという主張に基づき、テンプレートを学習可能なVAEベースの手法を提案します。提案手法では潜在変数の空間をテンプレート空間とコンテンツ空間に明示的に分離することによって、正確で多様な文生成が可能となります。また、table-textのペアデータだけではなくtableデータのないraw textデータを利用した半教師あり学習を行います。
2. 文献情報
BishanYang and Claire Cardie.
Extracting Opinion Expressions with semi- Markov Conditional Random Fields.
In Proceedings of the 2012 Joint Conference on EMNLP and CoNLL, pp. 1335-1345. (2012)
2014/6/5 文献紹介 2
※本スライド中の数式,図表はすべて文献より引用
6. 背景| 意見抽出
•
The International Committee of the Red Cross, [as usual][ESE], [has refused to make any statements][DSE].
•
The Chief Minister [said][DSE]that [the demon they have reared will eat up their own vitals][ESE].
2014/6/5 文献紹介 6
13. Semi-CRFs
•
正しいsegmentationは抽出すべきentities とそうでないsegmentの列で定義される
2014/6/5 文献紹介 13
(The,NONE),(Chief,NONE),(Minister,NONE), (said,DSE),(that,NONE),(the demon they have reared will eat up their own vitals,ESE),(.,NONE)
例
18. 提案手法| segmentation
•
訓練データ各文に対して正しい segmentationを得る
2014/6/5 文献紹介 18
(The ChiefMinister,NONE),(said,DSE),
(that,NONE),(the demon they have reared will eat up their own vitals,ESE),(.,NONE)
例
31. 実験| 評価尺度
•
precision, recall, F-measure
–
意見表現の境界線は明確ではない
Binary-Overlap (Brecket al. 2007)
Proportional-Overlap(Johansson and Moschitti2010)
2014/6/5 文献紹介 31
Eric Breck, YejinChoi, and Claire Cardie. Identifying expressions of opinion in context. IJCAI ’07. (2007)
Richard Johansson and Alessandro Moschitti. Syntactic and semantic structure for opinion expression detection. In Proceedings of CoNLL’10. (2010)