Mapping Entity Sets in News Archives Across Time

We propose a novel way of utilizing and accessing information stored in news archives as well as a new style of investigating the history. Our idea is to automatically generate similar entity pairs given two sets of entities, one from the past and one representing the present. This allows performing...

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Vydané v:	Data Science and Engineering Ročník 4; číslo 3; s. 208 - 222
Hlavní autori:	Duan, Yijun, Jatowt, Adam, Bhowmick, Sourav S., Yoshikawa, Masatoshi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2019 Springer Springer Nature B.V SpringerOpen
Predmet:	Algorithm Analysis and Problem Complexity Archives & records Artificial Intelligence Chemistry and Earth Sciences Comparable entity mining Computer Science Data Mining and Knowledge Discovery Database Management Integer linear programming Integer programming Linear programming Mapping Physics Statistics for Engineering Systems and Data Security Temporal embeddings alignment Typicality analysis Integer linear programming Typicality analysis Comparable entity mining Temporal embeddings alignment
ISSN:	2364-1185, 2364-1541
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Abstract	We propose a novel way of utilizing and accessing information stored in news archives as well as a new style of investigating the history. Our idea is to automatically generate similar entity pairs given two sets of entities, one from the past and one representing the present. This allows performing entity-oriented mapping between different times. We introduce an effective method to solve the aforementioned task based on a concise integer linear programming framework. In particular, our model first conducts typicality analysis to estimate entity representativeness. It next constructs orthogonal transformation between the two entity collections. The result is a set of typical across-time comparables. We demonstrate the effectiveness of our approach on the New York Times dataset through both qualitative and quantitative tests.
AbstractList	We propose a novel way of utilizing and accessing information stored in news archives as well as a new style of investigating the history. Our idea is to automatically generate similar entity pairs given two sets of entities, one from the past and one representing the present. This allows performing entity-oriented mapping between different times. We introduce an effective method to solve the aforementioned task based on a concise integer linear programming framework. In particular, our model first conducts typicality analysis to estimate entity representativeness. It next constructs orthogonal transformation between the two entity collections. The result is a set of typical across-time comparables. We demonstrate the effectiveness of our approach on the New York Times dataset through both qualitative and quantitative tests. Abstract We propose a novel way of utilizing and accessing information stored in news archives as well as a new style of investigating the history. Our idea is to automatically generate similar entity pairs given two sets of entities, one from the past and one representing the present. This allows performing entity-oriented mapping between different times. We introduce an effective method to solve the aforementioned task based on a concise integer linear programming framework. In particular, our model first conducts typicality analysis to estimate entity representativeness. It next constructs orthogonal transformation between the two entity collections. The result is a set of typical across-time comparables. We demonstrate the effectiveness of our approach on the New York Times dataset through both qualitative and quantitative tests.
Audience	Academic
Author	Jatowt, Adam Yoshikawa, Masatoshi Bhowmick, Sourav S. Duan, Yijun
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Cites_doi	10.1145/1242572.1242646 10.1145/2505515.2505666 10.1145/2619088 10.1109/TKDE.2017.2754499 10.3115/v1/D14-1162 10.1145/1871437.1871730 10.1109/ICDM.2007.27 10.1145/1148170.1148215 10.1007/978-3-030-18576-3_21 10.1017/S0269888902000371 10.1038/nature06176 10.1002/int.4550060205 10.1145/1390334.1390386 10.1145/1645953.1646198 10.1080/00401706.1977.10489521 10.1038/nature06137 10.3115/v1/P15-1063 10.1145/1816123.1816135 10.3115/v1/E14-1049 10.3115/v1/N15-1104 10.18653/v1/P16-1141 10.3115/v1/N15-1028 10.1126/science.1136800 10.1145/988672.988687
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Copyright	The Author(s) 2019 COPYRIGHT 2019 Springer Data Science and Engineering is a copyright of Springer, (2019). All Rights Reserved. © 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543 Kanhabua N, Nørvåg K (2010) Exploiting time-based synonyms in searching document archives. In: JCDL. ACM, pp 79–88 LiebermanEMichelJBJacksonJTangTNowakMAQuantifying the evolutionary dynamics of languageNature2007449716371310.1038/nature06137 Ester M, Kriegel HP, Sander J, Xu X et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, vol 96, pp 226–231 Smith SL, Turban DH, Hamblin S, Hammerla NY (2017) Offline bilingual word vectors, orthogonal transformations and the inverted softmax. arXiv preprint arXiv:170203859 Duan Y, Jatowt A, Bhowmick SS, Yoshikawa M (2019) Typicality-based across-time mapping of entity sets in document archives. In: International conference on database systems for advanced applications. Springer, pp 350–366 Jindal N, Liu B (2006) Identifying comparative sentences in text documents. 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In: ACL vol 1, pp 645–655 PagelMAtkinsonQDMeadeAFrequency of word-use predicts rates of lexical evolution throughout indo-european historyNature2007449716371710.1038/nature06176 Lu A, Wang W, Bansal M, Gimpel K, Livescu K (2015) Deep multilingual correlation for improved word embeddings. In: NAACL HLT, pp 250–256 Sandhaus E (2008) The New York times annotated corpus overview. The New York Times Company, Research and Development, pp 1–22 CamposRDiasGJorgeAMJatowtASurvey of temporal information retrieval and related applicationsACM Comput Surv (CSUR)201547215 Hua M, Pei J, Fu AW, Lin X, Leung HF (2007) Efficiently answering top-k typicality queries on large databases. In: Proceedings of VLDB, VLDB endowment, pp 890–901 Feldman R, Fresco M, Goldenberg J, Netzer O, Ungar L (2007) Extracting product comparisons from discussion boards. In: Data mining, 2007. ICDM 2007. IEEE, pp 469–474 Řehůřek R, Sojka P (2010) Software framework for topic modelling with large corpora. 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References_xml	– reference: FreyBJDueckDClustering by passing messages between data pointsScience20073155814972976229217410.1126/science.1136800 – reference: Xing C, Wang D, Liu C, Lin Y (2015) Normalized word embedding and orthogonal transform for bilingual word translation. In: NAACL HLT, pp 1006–1011 – reference: DuboisDPradeHRossazzaJPVagueness, typicality, and uncertainty in class hierarchiesInt J Intell Syst19916216718310.1002/int.4550060205 – reference: TammaVBench-CaponTAn ontology model to facilitate knowledge-sharing in multi-agent systemsKnowl Eng Rev2002171416010.1017/S0269888902000371 – reference: HuangXWanXXiaoJLearning to find comparable entities on the webWeb Inf Syst Eng WISE201220121629 – reference: Hua M, Pei J, Fu AW, Lin X, Leung HF (2007) Efficiently answering top-k typicality queries on large databases. In: Proceedings of VLDB, VLDB endowment, pp 890–901 – reference: Mikolov T, Le QV, Sutskever I (2013) Exploiting similarities among languages for machine translation. arXiv preprint arXiv:13094168 – reference: Faruqui M, Dyer C (2014) Improving vector space word representations using multilingual correlation. In: EACL, pp 462–471 – reference: Reed SE, Zhang Y, Zhang Y, Lee H (2015) Deep visual analogy-making. In: Advances in neural information processing systems, pp 1252–1260 – reference: WangQMaoZWangBGuoLKnowledge graph embedding: a survey of approaches and applicationsIEEE Trans Knowl Data Eng201729122724274310.1109/TKDE.2017.2754499 – reference: Tahmasebi N, Gossen G, Kanhabua N, Holzmann H, Risse T (2012) Neer: an unsupervised method for named entity evolution recognition. In: COLING, pp 2553–2568 – reference: Yu HT, Jatowt A, Blanco R, Joho H, Jose J, Chen L, Yuan F (2017) A concise integer linear programming formulation for implicit search result diversification. In: Proceedings of the tenth ACM WSDM. ACM, pp 191–200 – reference: Gurobi Optimization I (2016) Gurobi optimizer reference manual. http://www.gurobi.com. Accessed 24 Aug 2018 – reference: Pennington J, Socher R, Manning C (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543 – reference: Chen YN, Metze F (2012) Two-layer mutually reinforced random walk for improved multi-party meeting summarization. In: SLT, 2012 IEEE. IEEE, pp 461–466 – reference: Duan Y, Jatowt A, Bhowmick SS, Yoshikawa M (2019) Typicality-based across-time mapping of entity sets in document archives. In: International conference on database systems for advanced applications. Springer, pp 350–366 – reference: Sarawagi S, Cohen WW (2005) Semi-Markov conditional random fields for information extraction. In: NIPS, pp 1185–1192 – reference: Berberich K, Bedathur SJ, Sozio M, Weikum G (2009) Bridging the terminology gap in web archive search. In: WebDB – reference: Hamilton WL, Leskovec J, Jurafsky D (2016) Diachronic word embeddings reveal statistical laws of semantic change. arXiv preprint arXiv:160509096 – reference: LiebermanEMichelJBJacksonJTangTNowakMAQuantifying the evolutionary dynamics of languageNature2007449716371310.1038/nature06137 – reference: McCallum A, Jensen D (2003) A note on the unification of information extraction and data mining using conditional-probability, relational models – reference: Jindal N, Liu B (2006) Mining comparative sentences and relations. In: AAAI, vol 22, pp 1331–1336 – reference: PagelMAtkinsonQDMeadeAFrequency of word-use predicts rates of lexical evolution throughout indo-european historyNature2007449716371710.1038/nature06176 – reference: Jiang Z, Ji L, Zhang J, Yan J, Guo P, Liu N (2013) Learning open-domain comparable entity graphs from user search queries. In: Proceedings of the 22nd ACM CIKM. ACM, pp 2339–2344 – reference: Kanhabua N, Nørvåg K (2010) Exploiting time-based synonyms in searching document archives. In: JCDL. ACM, pp 79–88 – reference: Sandhaus E (2008) The New York times annotated corpus overview. The New York Times Company, Research and Development, pp 1–22 – reference: Etzioni O, Cafarella M, Downey D, Kok S, Popescu AM, Shaked T, Soderland S, Weld DS, Yates A (2004) Web-scale information extraction in knowitall: (preliminary results). In: Proceedings of the 13th WWW. ACM, pp 100–110 – reference: Feldman R, Fresco M, Goldenberg J, Netzer O, Ungar L (2007) Extracting product comparisons from discussion boards. In: Data mining, 2007. ICDM 2007. IEEE, pp 469–474 – reference: Smith SL, Turban DH, Hamblin S, Hammerla NY (2017) Offline bilingual word vectors, orthogonal transformations and the inverted softmax. arXiv preprint arXiv:170203859 – reference: RodríguezMAEgenhoferMJDetermining semantic similarity among entity classes from different ontologiesIEEE TKDE2003152442456 – reference: Lu A, Wang W, Bansal M, Gimpel K, Livescu K (2015) Deep multilingual correlation for improved word embeddings. In: NAACL HLT, pp 250–256 – reference: Wan X, Yang J (2008) Multi-document summarization using cluster-based link analysis. In: Proceedings of ACM SIGIR. 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Snippet	We propose a novel way of utilizing and accessing information stored in news archives as well as a new style of investigating the history. Our idea is to... Abstract We propose a novel way of utilizing and accessing information stored in news archives as well as a new style of investigating the history. Our idea is...
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SubjectTerms	Algorithm Analysis and Problem Complexity Archives & records Artificial Intelligence Chemistry and Earth Sciences Comparable entity mining Computer Science Data Mining and Knowledge Discovery Database Management Integer linear programming Integer programming Linear programming Mapping Physics Statistics for Engineering Systems and Data Security Temporal embeddings alignment Typicality analysis
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Title	Mapping Entity Sets in News Archives Across Time
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