{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T06:19:02Z","timestamp":1775283542586,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":51,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T00:00:00Z","timestamp":1564012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS1815674"],"award-info":[{"award-number":["IIS1815674"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000183","name":"Army Research Office","doi-asserted-by":"publisher","award":["W911NF-17-1-0412"],"award-info":[{"award-number":["W911NF-17-1-0412"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,7,25]]},"DOI":"10.1145\/3292500.3330985","type":"proceedings-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T13:17:26Z","timestamp":1564147046000},"page":"1389-1398","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Riker"],"prefix":"10.1145","author":[{"given":"Jie","family":"Zhao","sequence":"first","affiliation":[{"name":"The Ohio State University, Columbus, OH, USA"}]},{"given":"Ziyu","family":"Guan","sequence":"additional","affiliation":[{"name":"Xidian University, Xi'an, China"}]},{"given":"Huan","family":"Sun","sequence":"additional","affiliation":[{"name":"The Ohio State University, Columbus, OH, USA"}]}],"member":"320","published-online":{"date-parts":[[2019,7,25]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"265","article-title":"Tensorflow: a system for large-scale machine learning","volume":"16","author":"Abadi M.","year":"2016","journal-title":"OSDI"},{"key":"e_1_3_2_1_2_1","unstructured":"Q. Ai V. Azizi X. Chen and Y. Zhang. 2018. Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation. arXiv preprint arXiv:1805.03352 (2018).  Q. Ai V. Azizi X. Chen and Y. Zhang. 2018. Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation. arXiv preprint arXiv:1805.03352 (2018)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130140"},{"key":"e_1_3_2_1_4_1","volume-title":"et almbox","author":"Baeza-Yates R.","year":"1999"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"D. Bau B. Zhou A. Khosla A. Oliva and A. Torralba. 2017. Network dissection: Quantifying interpretability of deep visual representations. arXiv preprint arXiv:1704.05796 (2017).  D. Bau B. Zhou A. Khosla A. Oliva and A. Torralba. 2017. Network dissection: Quantifying interpretability of deep visual representations. arXiv preprint arXiv:1704.05796 (2017).","DOI":"10.1109\/CVPR.2017.354"},{"key":"e_1_3_2_1_6_1","volume-title":"SMART: TREC 3. NIST special publication sp","author":"Buckley C.","year":"1995"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210203"},{"key":"e_1_3_2_1_8_1","unstructured":"Ohio Supercomputer Center. 1987. Ohio Supercomputer Center. http:\/\/osc.edu\/ark:\/19495\/f5s1ph73  Ohio Supercomputer Center. 1987. Ohio Supercomputer Center. http:\/\/osc.edu\/ark:\/19495\/f5s1ph73"},{"key":"e_1_3_2_1_9_1","unstructured":"Q. Chen X. Zhu Z. Ling S. Wei H. Jiang and D. Inkpen. 2016b. Enhanced lstm for natural language inference. arXiv preprint arXiv:1609.06038 (2016).  Q. Chen X. Zhu Z. Ling S. Wei H. Jiang and D. Inkpen. 2016b. Enhanced lstm for natural language inference. arXiv preprint arXiv:1609.06038 (2016)."},{"key":"e_1_3_2_1_10_1","volume-title":"Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In NIPS. 2172--2180.","author":"Chen X.","year":"2016"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"K. Cho B. Van Merri\u00ebnboer C. Gulcehre D. Bahdanau F. Bougares H. Schwenk and Y. Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014).  K. Cho B. Van Merri\u00ebnboer C. Gulcehre D. Bahdanau F. Bougares H. Schwenk and Y. Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014).","DOI":"10.3115\/v1\/D14-1179"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"F. Diaz B. Mitra and N. Craswell. 2016. Query expansion with locally-trained word embeddings. arXiv preprint arXiv:1605.07891 (2016).  F. Diaz B. Mitra and N. Craswell. 2016. Query expansion with locally-trained word embeddings. arXiv preprint arXiv:1605.07891 (2016).","DOI":"10.18653\/v1\/P16-1035"},{"key":"e_1_3_2_1_13_1","unstructured":"F. Doshi-Velez and B. Kim. 2017. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608 (2017).  F. Doshi-Velez and B. Kim. 2017. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608 (2017)."},{"key":"e_1_3_2_1_14_1","unstructured":"M. Du N. Liu and X. Hu. 2018. Techniques for interpretable machine learning. arXiv preprint arXiv:1808.00033 (2018).  M. Du N. Liu and X. Hu. 2018. Techniques for interpretable machine learning. arXiv preprint arXiv:1808.00033 (2018)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"J. L. Fleiss and J. Cohen. 1973. The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educational and psychological measurement Vol. 33 3 (1973) 613--619.  J. L. Fleiss and J. Cohen. 1973. The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educational and psychological measurement Vol. 33 3 (1973) 613--619.","DOI":"10.1177\/001316447303300309"},{"key":"e_1_3_2_1_16_1","volume-title":"Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning. arXiv preprint arXiv:1806.00069","author":"Gilpin L. H.","year":"2018"},{"key":"e_1_3_2_1_17_1","unstructured":"B. Goodman and S. Flaxman. 2016. European Union regulations on algorithmic decision-making and a\" right to explanation\". arXiv preprint arXiv:1606.08813 (2016).  B. Goodman and S. Flaxman. 2016. European Union regulations on algorithmic decision-making and a\" right to explanation\". arXiv preprint arXiv:1606.08813 (2016)."},{"key":"e_1_3_2_1_18_1","volume-title":"Explainable artificial intelligence (xai)","author":"Gunning D.","year":"2017"},{"key":"e_1_3_2_1_19_1","unstructured":"Matthew H. and Ines M. 2017. spaCy 2: Natural language understanding with Bloom embeddings convolutional neural networks and incremental parsing. To appear (2017).  Matthew H. and Ines M. 2017. spaCy 2: Natural language understanding with Bloom embeddings convolutional neural networks and incremental parsing. To appear (2017)."},{"key":"e_1_3_2_1_20_1","unstructured":"S. Jain and B. C. Wallace. 2019. Attention is not Explanation. arXiv preprint arXiv:1902.10186 (2019).  S. Jain and B. C. Wallace. 2019. Attention is not Explanation. arXiv preprint arXiv:1902.10186 (2019)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/582415.582418"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0306-4573(00)00016-9"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080660"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2983323.2983876"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/383952.383972"},{"key":"e_1_3_2_1_26_1","volume-title":"The mythos of model interpretability. arXiv preprint arXiv:1606.03490","author":"Lipton Z. C.","year":"2016"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2063576.2063584"},{"key":"e_1_3_2_1_28_1","volume-title":"Deep learning: A critical appraisal. arXiv preprint arXiv:1801.00631","author":"Marcus G.","year":"2018"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767755"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2883044"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"A. P. Parikh O. T\"ackstr\u00f6m D. Das and J. Uszkoreit. 2016. A decomposable attention model for natural language inference. arXiv preprint arXiv:1606.01933 (2016).  A. P. Parikh O. T\"ackstr\u00f6m D. Das and J. Uszkoreit. 2016. A decomposable attention model for natural language inference. arXiv preprint arXiv:1606.01933 (2016).","DOI":"10.18653\/v1\/D16-1244"},{"key":"e_1_3_2_1_32_1","volume-title":"Multimodal Explanations: Justifying Decisions and Pointing to the Evidence. In CVPR .","author":"Park D. H.","year":"2018"},{"key":"e_1_3_2_1_33_1","volume-title":"Glove: Global vectors for word representation. In EMNLP. 1532--1543.","author":"Pennington J.","year":"2014"},{"key":"e_1_3_2_1_34_1","unstructured":"J. P\u00e9rez-Iglesias J. P\u00e9rez-Ag\u00fcera V. Fresno and Y. Z. Feinstein. 2009. Integrating the probabilistic models BM25\/BM25F into Lucene. arXiv preprint arXiv:0911.5046 (2009).  J. P\u00e9rez-Iglesias J. P\u00e9rez-Ag\u00fcera V. Fresno and Y. Z. Feinstein. 2009. Integrating the probabilistic models BM25\/BM25F into Lucene. arXiv preprint arXiv:0911.5046 (2009)."},{"key":"e_1_3_2_1_35_1","volume-title":"EMNLP Workshop BlackboxNLP. 125--135","author":"Pryzant R."},{"key":"e_1_3_2_1_36_1","volume-title":"Software Framework for Topic Modelling with Large Corpora. In LREC Workshop on New Challenges for NLP Frameworks. 45--50","author":"R."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_2_1_38_1","unstructured":"S. Riezler A. Vasserman I. Tsochantaridis V. Mittal and Y. Liu. 2007. Statistical machine translation for query expansion in answer retrieval. In ACL . 464--471.  S. Riezler A. Vasserman I. Tsochantaridis V. Mittal and Y. Liu. 2007. Statistical machine translation for query expansion in answer retrieval. In ACL . 464--471."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"R. R. Selvaraju M. Cogswell A. Das R. Vedantam D. Parikh and D. Batra. 2017. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization.. In ICCV . 618--626.  R. R. Selvaraju M. Cogswell A. Das R. Vedantam D. Parikh and D. Batra. 2017. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization.. In ICCV . 618--626.","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"crossref","unstructured":"R. Sharp M. Surdeanu P. Jansen M. A. Valenzuela-Esc\u00e1rcega P. Clark and M. Hammond. 2017. Tell me why: Using question answering as distant supervision for answer justification. In CoNLL . 69--79.  R. Sharp M. Surdeanu P. Jansen M. A. Valenzuela-Esc\u00e1rcega P. Clark and M. Hammond. 2017. Tell me why: Using question answering as distant supervision for answer justification. In CoNLL . 69--79.","DOI":"10.18653\/v1\/K17-1009"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"G. Shen Y. Yang and Z. H. Deng. 2017. Inter-Weighted Alignment Network for Sentence Pair Modeling. In EMNLP. 1190--1200.  G. Shen Y. Yang and Z. H. Deng. 2017. Inter-Weighted Alignment Network for Sentence Pair Modeling. In EMNLP. 1190--1200.","DOI":"10.18653\/v1\/D17-1122"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220048"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"M. Wan and J. McAuley. 2016. Modeling ambiguity subjectivity and diverging viewpoints in opinion question answering systems. In ICDM. 489--498.  M. Wan and J. McAuley. 2016. Modeling ambiguity subjectivity and diverging viewpoints in opinion question answering systems. In ICDM. 489--498.","DOI":"10.1109\/ICDM.2016.0060"},{"key":"e_1_3_2_1_44_1","unstructured":"F. Wang and C. Rudin. 2015. Falling rule lists. In AISTATS .  F. Wang and C. Rudin. 2015. Falling rule lists. In AISTATS ."},{"key":"e_1_3_2_1_45_1","unstructured":"M. Wang N. A. Smith and T. Mitamura. 2007. What is the Jeopardy model? A quasi-synchronous grammar for QA. In EMNLP-CoNLL .  M. Wang N. A. Smith and T. Mitamura. 2007. What is the Jeopardy model? A quasi-synchronous grammar for QA. In EMNLP-CoNLL ."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/243199.243202"},{"key":"e_1_3_2_1_47_1","volume-title":"Wikiqa: A challenge dataset for open-domain question answering. In EMNLP . 2013--2018.","author":"Yang Y.","year":"2015"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159718"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080831"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"crossref","unstructured":"X. Zhang S. Li L. Sha and H. Wang. 2017. Attentive Interactive Neural Networks for Answer Selection in Community Question Answering.. In AAAI. 3525--3531.   X. Zhang S. Li L. Sha and H. Wang. 2017. Attentive Interactive Neural Networks for Answer Selection in Community Question Answering.. In AAAI. 3525--3531.","DOI":"10.1609\/aaai.v31i1.11006"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"crossref","unstructured":"J. Zhao Y. Su Z. Guan and H. Sun. 2017. An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective. In EMNLP .  J. Zhao Y. Su Z. Guan and H. Sun. 2017. An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective. In EMNLP .","DOI":"10.18653\/v1\/D17-1131"}],"event":{"name":"KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Anchorage AK USA","acronym":"KDD '19","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330985","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330985","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330985","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:26:04Z","timestamp":1750206364000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330985"}},"subtitle":["Mining Rich Keyword Representations for Interpretable Product Question Answering"],"short-title":[],"issued":{"date-parts":[[2019,7,25]]},"references-count":51,"alternative-id":["10.1145\/3292500.3330985","10.1145\/3292500"],"URL":"https:\/\/doi.org\/10.1145\/3292500.3330985","relation":{},"subject":[],"published":{"date-parts":[[2019,7,25]]},"assertion":[{"value":"2019-07-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}