This is the second workshop on query representation and understanding at sigir. Mixture model with multiple centralized retrieval algorithms for result merging in federated search dzung hong department of computer science purdue university 250 n. Information retrieval with verbose queries proposal for a tutorial at sigir 15 conference. However, these approaches may suffer from the complexity of natural. A study of poisson query generation model for information. Sigir 2019 tutorial part iv shuo zhang and krisztian balog. Deep learning for matching in search and recommendation. Information retrieval with verbose queries proposal for. Query representation and understanding workshop 2011 qru 11 acm sigir 2011, beijing, china rishiraj saha roy and niloy ganguly iit kharagpur india monojit choudhury microsoft research india. August 23, 2012 query representation and understanding 2011 qru 11 9. Machine learning for querydocument matching in web search.
Query representation and understanding workshop acm sigir. We start with introducing the basic tools in deep learning for information retrieval and natural language processing, including word embedding 25, 27, 19, 20, recurrent neural network rnn 26, 9, 6, convolutional neural network cnn 7, 10, 31, as well as training of deep neural network models. Finding similar queries based on query representation analysis. We study their effectiveness under various learning scenarios pointwise and pairwise models and using different input representations i. Largescale graph mining and learning for information retrieval bin gao, taifeng wang, and tieyan liu microsoft research asia. Proceedings of the 35th international acm sigir conference. Proceedings of the 35th international acm sigir conference on research. These proceedings contain the papers of the sigir 2012 workshop on open source. Largescale graph mining and learning for information. Relevancebased word embedding proceedings of the 40th. In this paper, we focus on selecting queries in order to most rapidly increase ranker retrieval performance. This paper brings in recent neural techniques to model search queries 3. Large scale machine learning for query document matching in.
Proceedings of the 3rd joint workshop on bibliometricenhanced information retrieval and natural language processing for digital libraries birndl 2018 colocated with the 41st international acm sigir conference on research and development in information retrieval sigir. Query hypergraphs, query representation, retrieval models. Pdf parameterized neural network language models for. Sigir 2019 interacting with text user selects and annotates text in documents annotations then used as the basis for new queries effective retrieval requires the system to use this feedback effectively in query generation and ranking lee and croft, generating queries from userselected text. Michael published more than 20 research papers on infor.
Entities provide a wealth of rich features that can be used for. We propose employing the reranking approach in query segmentation, which first employs a generative model to create the top k candidates and then. Dec 21, 2012 read information retrieval with query hypergraphs, acm sigir forum on deepdyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips. We are delighted to welcome you to the 35th edition of sigir, the acm international conference on research and development in information retrieval. A new approach to query segmentation for relevance ranking in. An instantiation of the dual cmeans for src, which takes advantage of external resources such as query logs to improve clustering accuracy, labeling quality and partitioning shape. Sigir 2012 portland, oregon, usa august 1216, 2012 industry track. Contextsensitive translation for crosslanguage information retrieval ferhan ture1,jimmylin2,3, douglas w. A distributional semantics approach andre freitas, fabricio f. Machine learning for query document matching in web search hang li huawei technologies 1 sigir 2012 tutorial august 12, 2012.
Research carnegie mellon school of computer science. Distributed representations of words and phrases and their compositionality. Integrating query, thesaurus, and documents through a. Sep 28, 2014 in this paper, we try to determine how best to improve stateoftheart methods for relevance ranking in web searching by query segmentation. Originally presented as a halfday tutorial at sigir 12. Coadvised master researchshort paper chenyan xiong and jamie callan. Entropybiased models for query representation on the.
This is similar to the cnf interface of wikiquery except. Sigir 2012 assuming that documents have been classified into classes. Sigir 09, july 1923, 2009, boston, massachusetts, usa. Query understanding methods generally take place before the search engine retrieves and ranks results. Jianyun nie, michel simard, pierre isabelle, and richard durand. Twostage language models for information retrieval chengxiang zhai. The 43rd international acm sigir conference on research and development in information retrieval. It is related to natural language processing but specifically focused on the understanding of search queries. Information retrieval with query hypergraphs, acm sigir. Entity and knowledge baseoriented information retrieval. The 35th international acm sigir conference on research and development in information retrieval. Mixture model with multiple centralized retrieval algorithms for result merging in federated search dzung hong. Proceedings of the sigir 2012 workshop on open source.
Pdf frontiers, challenges, and opportunities for information. Kevyn is also an affiliate faculty member of the artificial intelligence lab and the michigan institute for data science midas. Large scale machine learning for query document matching in web search hang li huawei technologies. Request pdf query representation and understanding workshop this report summarizes the events of the sigir 2010 workshop on query representation and understanding, which was held on july 23rd. It is also related to a successful series of workshops on query representation and understanding held at sigir 2010 and 2011. Posterpaper in proceedings of the 35th annual acm sigir conference sigir 2012. These themes were then summarized and published in the sigir forum article frontiers, challenges, and opportunities for information retrieval. Kevyn collinsthompson is an associate professor at the university of michigan ann arbor, with appointments in the school of information and dept. Query understanding is the process of inferring the intent of a search engine user by extracting semantic meaning from the searchers keywords. Information retrieval with query hypergraphs, acm sigir forum. Entity query feature expansion using knowledge base links jeffrey dalton, laura dietz, james allan. Overview of the first workshop on knowledge graphs and. Query representation document representation semantic matching matching can be conducted at different levels ranking result 20.
In contrast, in this dissertation we focus on longer, verbose queries with more. Michael coorganized a successful series of workshops on query representation and understanding held at sigir 2010 and 2011. In proceedings of the 22nd annual international acm sigir conference on research and development in information retrieval, sigir 99, pages 7481, new york, ny, usa. At the end, in spite of a tight budget, the conference obtained a small surplus. Xiaobing xue, yu tao, daxin jiang and hang li, automatically mining question reformulation patterns from search log data, in proceedings of the 50th annual meeting of association for computational linguistics acl12, to appear, 2012. Pd is assumed to be uniform each document is equally likely to be drawn for a query what can influence the probability of a document being relevant to an unseen query. Neural ranking models with weak supervision proceedings. Largescale graph mining and learning for information retrieval. We focus on the postretrieval query performance prediction qpp task. Higher mean shortest path in query networks peripheral units can independently form queries more difficult to understand the context of a previously unseen unit high surprise factor august 23, 2012 query representation and understanding 2011 qru 11 10 airedale terrier tumor where download prison break. Search tasks, document ranking, query suggestion, neural ir models. Sigir workshop on timeaware information access, 2012. As someone who has been in information retrieval for some time now and who also has done a stint in an academic research lab and works on an open source search engine that has a huge commercial base, but mixed coverage in academia more later, i was a little unsure of what to expect in heading to my first ever sigir conference in portland, or last week.
Machine learning for query document matching in web search 18. This tutorial is completely new with rich content of the recent technologies, including 1 the newly developed deep. Answering natural language queries over linked data graphs. Report on the sigir 2015 workshop on reproducibility, inexplicability, and generalizability of results rigor. Integrating query, thesaurus, and documents through a common vkual representation richard h. Sigir 2012 tutorial august 12, 2012 portland oregon jun xu. To the best of our knowledge, no previous tutorials have been offered on this research topic. Kevyn collinsthompsons homepage university of michigan. We create the first intrinsic evaluation for query intent repre. Raw query representation set of wordsentites raw table representation semantic vector representations. Sigir 12, august 1216, 2012, portland, oregon, usa. A new annotated dataset websrc401 based on the trec web track 2012 for full src evaluation over the web. Cox computer science department university college london, uk. Nordlys proceedings of the 40th international acm sigir.
This paper presents a new representation for documents and queries. We extrinsically evaluate our learned word representation models using two ir tasks. Information retrieval with verbose queries proposal for a. Large scale machine learning for query document matching in web search hang li huawei technologies mmds 2012 stanford university work was done at microsoft research, with former colleagues and interns. Experimental methods for information retrieval who we are tutorial. A machine learning framework for ranking query suggestions. Large scale machine learning for query document matching. Crosslanguage information retrieval based on parallel texts and automatic mining of parallel texts from the web. Query performance prediction qpp may be defined as the problem of predicting the effectiveness of a search system for a given query and a collection of documents without any relevance judgments. Query expansion is about enriching the query representation while holding the document representation static. Jun 29, 20 in order to understand user intents behind their queries, many researchers study similar query finding. Salton award lecture information retrieval as engineering. The conference continues its tradition of being the premier forum for research and development information retrieval, the computer science discipline behind what many call search.
Active query selection for learning rankers microsoft. Elo to estimate which queries should be selected but is limited to rankers that predict absolute graded relevance. Entropybiased models for query representation on the click graph hongbo deng department of cse the chinese university of hk shatin, nt, hong kong. The annual sigir conference is the major international forum for the presentation of new research results, and the demonstration of new systems and techniques, in the broad field of information retrieval ir. Query performance prediction using passage information. Sigir 2011 workshop on query representation and understanding. To train our models, we used over six million unique queries and the top ranked documents retrieved in response to each query, which are assumed to be relevant to the query. Pdf a conceptual representation of documents and queries for. International acm sigir conference on research and. Short paper jing chen, chenyan xiong, and jamie callan. Document length document quality pagerank, hits, etc. The previous approaches mainly either generate related terms or find relevant queries based on the coclicked urls. Parameterized neural network language models for information retrieval.
Document expansion by query prediction rodrigo nogueira,1 wei yang,2 jimmy lin,2 and kyunghyun cho3. Sigir 2012 welcomes contributions related to any aspect of ir theory and foundation, techniques, and applications. The logical db view interprets query processing as the task of. An empirical study of learning to rank for entity search. Query representation and understanding workshop request pdf. Context attentive document ranking and query suggestion arxiv. Query representation document representation semantic. Assisted query formulation for multimodal medical casebased retrieval. Scholarly paper browsing system based on pdf restructuring and text annotation. Query focused scientific paper summarization with localized sentence representation. Mixture model with multiple centralized retrieval algorithms. Modeling higherorder term dependencies in information retrieval. Entity linking the query provides very precise indicators but may also miss many of the relevant entities entity expansion in prf may make a query noisy approach 1 jeffrey dalton, laura dietz, james allan. Workshop report query representation and understanding workshop w.
An uncertaintyaware query selection model for evaluation. Ranking on largescale graph problem definition given a largescale directed graph and its rich. Query segmentation is meant to separate the input query into segments, typically natural language phrases. Proceedings of the 35th international acm sigir conference on. Frontiers, challenges, and opportunities for information. Sigir 2019 web search generally viewed as placing most of the burden for successful search on the user e.
We further train a set of simple yet effective ranking models based on feedforward neural networks. Imprecision is mainly caused by the imperfection in the representation of the semantics and pragmatics of the objects stored, which are typically multimedia documents. Research frontiers in information retrieval report from. We introduce and address the task of onthey table generation. Query representation document representation semantic matching. Connecting query and documents through external semistructured data. An uncertaintyaware query selection model for evaluation of ir systems mehdi hosseini, ingemar j. Query representation and understanding workshop 2011 qru 11. The first joint international workshop on entityorientedand. Hierarchical target type identification for entityoriented queries proc. Pdf this article presents a vector space model approach to representing. Largescale photo retrieval by facial attributes and canvas layout yuheng lei, yanying chen, borchun chen, lime iida, winston h. The objective for the workshop was to bring together academic researchers and industry practitioners working on entityoriented search to discuss tasks and challenges, and to uncover the next frontiers for.
Query representation for crosstemporal information retrieval. These workshops have the goal of bringing together the differ ent strands of research on query understanding, increasing the dialogue between researchers. Entity queryfeature expansion using knowledge base links. In those tutorials, the traditional machine learning approaches to the semantic matching problem were introduced under the web search scenario.
Hang li noahs ark lab huawei technologies mla 2012 tsinghua university nov. Recently, the click graph has shown its utility in describing the relationship between queries and urls. The importance of interaction in information retrieval. Entity query feature expansion using knowledge base links. In this work, we demonstrate how to easily adapt elo. In this paper, we explore an alternative approach based on enriching the docu. Specifically, we make a new use of passage information for this task. In order to understand user intents behind their queries, many researchers study similar query finding. Exploitingtermdependencewhile handlingnegationinmedicalsearch. Proceedings of the 35th annual international acm sigir conference sigir12, to appear, 2012. Query representation and understanding workshop 2011. Xiaobing xue, yu tao, daxin jiang and hang li, automatically mining question reformulation patterns. Implies to a musthave reference comparison the query likelihood method used to create the initial ranking the query likelihood model is a special case of our approach.