Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval最新文献_第4页

Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking 反事实在线:高效、公正的在线排名评价

Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval Pub Date : 2020-07-24 DOI: 10.1145/3409256.3409820

Harrie Oosterhuis, M. de Rijke

引用次数: 17

Understanding BERT Rankers Under Distillation 在蒸馏下理解BERT Rankers

Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval Pub Date : 2020-07-21 DOI: 10.1145/3409256.3409838

Luyu Gao, Zhuyun Dai, Jamie Callan

引用次数: 33

Using Sentiment Analysis for Pseudo-Relevance Feedback in Social Book Search 社交图书搜索中伪相关反馈的情感分析

Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval Pub Date : 2020-06-30 DOI: 10.1145/3409256.3409847

Amal Htait, S. Fournier, P. Bellot, L. Azzopardi, G. Pasi

{"title":"Using Sentiment Analysis for Pseudo-Relevance Feedback in Social Book Search","authors":"Amal Htait, S. Fournier, P. Bellot, L. Azzopardi, G. Pasi","doi":"10.1145/3409256.3409847","DOIUrl":"https://doi.org/10.1145/3409256.3409847","url":null,"abstract":"Book search is a challenging task due to discrepancies between the content and description of books, on one side, and the ways in which people query for books, on the other. However, online reviewers provide an opinionated description of the book, with alternative features that describe the emotional and experiential aspects of the book. Therefore, locating emotional sentences within reviews, could provide a rich alternative source of evidence to help improve book recommendations. Specifically, sentiment analysis (SA) could be employed to identify salient emotional terms, which could then be used for query expansion? This paper explores the employment ofSA based query expansion, in the book search domain. We introduce a sentiment-oriented method for the selection of sentences from the reviews of top rated book. From these sentences, we extract the terms to be employed in the query formulation. The sentence selection process is based on a semi-supervised SA method, which makes use of adapted word embeddings and lexicon seed-words.Using the CLEF 2016 Social Book Search (SBS) Suggestion TrackCollection, an exploratory comparison between standard pseudo-relevance feedback and the proposed sentiment-based approach is performed. The experiments show that the proposed approach obtains 24%-57% improvement over the baselines, whilst the classic technique actually degrades the performance by 14%-51%.","PeriodicalId":430907,"journal":{"name":"Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126542692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Efficient Test Collection Construction via Active Learning 通过主动学习构建有效的测试集

Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval Pub Date : 2018-01-17 DOI: 10.1145/3409256.3409837

Md. Mustafizur Rahman, Mucahid Kutlu, T. Elsayed, Matthew Lease

{"title":"Efficient Test Collection Construction via Active Learning","authors":"Md. Mustafizur Rahman, Mucahid Kutlu, T. Elsayed, Matthew Lease","doi":"10.1145/3409256.3409837","DOIUrl":"https://doi.org/10.1145/3409256.3409837","url":null,"abstract":"To create a new IR test collection at low cost, it is valuable to carefully select which documents merit human relevance judgments. Shared task campaigns such as NIST TREC pool document rankings from many participating systems (and often interactive runs as well) in order to identify the most likely relevant documents for human judging. However, if one's primary goal is merely to build a test collection, it would be useful to be able to do so without needing to run an entire shared task. Toward this end, we investigate multiple active learning strategies which, without reliance on system rankings: 1) select which documents human assessors should judge; and 2) automatically classify the relevance of additional unjudged documents. To assess our approach, we report experiments on five TREC collections with varying scarcity of relevant documents. We report labeling accuracy achieved, as well as rank correlation when evaluating participant systems based upon these labels vs. full pool judgments. Results show the effectiveness of our approach, and we further analyze how varying relevance scarcity across collections impacts our findings. To support reproducibility and follow-on work, we have shared our code onlinefootnoteurlhttps://github.com/mdmustafizurrahman/ICTIR_AL_TestCollection_2020/ .","PeriodicalId":430907,"journal":{"name":"Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125169965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11