- May 15, 2020
INFS7410 Project – Part 1 – v2 Note: these instructions have been modified on 26/08/2019Preamble The due date for this assignment is 29 August 2019 17:00 5 September 2019 17:00, EasternAustralia Standard Time (extended from 29/08).This project is worth 5% of the overall mark for INFS7410. A detailed marking sheet for thisassignment is provided at the end of this document.We recommend that you make an early start on this assignment, and proceed by steps. There area number of activities you make already tackle, including setting up the pipeline, manipulatingthe queries, implement some retrieval functions and perform evaluation and analysis. There aresome activities you do not know yet how to perform, in particular the implementation of the rankfusion algorithms: this will be the topic of the week 5 lecture and tutorials.Aim Project aim: The aim of this project is to implement a number of information retrieval methods,evaluate them and compare them in the context of a real use-case.Project Part 1 aim The aim of part 1 is to:setup the evaluation infrastructure, including collection and index, topics, qrelsimplement common information retrieval baselinesimplement ranking fusion methodsevaluate, compare and analyse baseline and ranking fusion methodsThe Information Retrieval Task: Ranking of studies forSystematic Reviews In this project we will consider the problem of ranking research studies identified as part of asystematic review. Systematic reviews are a widely used method to provide an overview of thecurrent scientific consensus, by bringing together multiple studies in a reliable, transparent way.We will use the CLEF 2017 and 2018 eHealth TAR (task 2) collections. In CLEF TAR 2017, the taskwe consider is referred to as subtask 1 (and is the only task); in CLEF TAR 2018, the task weconsider is referred to as subtask 2. We provide the CLEF 2017 and 2018 TAR task overviewpapers in the assignment folder in blackboard for your reference. These contain details about thetopics, the collection, the task, etc. These details are not necessary to complete the assignment,but nevertheless you may want to know more about this task, its importance, approaches thathave been tried, and so on.The task consists of, given as the starting point the results of the Boolean search created by theresearchers undertaking a systematic review, ranking the set of the provided documents (they arePMID – pubmed ID – in the files provided; for each PMID there is an associated title and abstract).The goal is to produce an ordering of the documents such that all the relevant documents areretrieved above the irrelevant ones. This is to be achieved through automatic methods that rankall abstracts, with the goal of retrieving relevant documents as early in the ranking as possible.There are two datasets to consider in this project. The CLEF 2017 TAR dataset; and the CLEF 2018TAR dataset. Each dataset consists of material for training, and. material for testing the developedinformation retrieval methods.What we provide you with We provide:for each dataset, a list of topics to be used for training. Each topic is organised into a file.Each topic contains a title and a Boolean query.for each dataset, a list of topics to be used for testing. Each topic is organised into a file. Eachtopic contains a title and a Boolean query.each topic file (both those for training and those for testing), includes a list of retrieveddocuments in the form of their PMIDs: these are the documents that you have to rank. Takenote: you do not need to perform the retrieval from scratch (i.e. execute the query againstthe whole index); instead you need to rank (order) the provided documents.for each dataset, and for each train and test partition, a qrels file, containing relevanceassessments for the documents to be ranked. This is to be used for evaluation.for each dataset, and for test partitions, a set of runs from retrieval systems thatparticipated to CLEF 2017/2018 to be considered for fusion.a Terrier index of the entire Pubmed collection. This index has been produced using theTerrier stopword list and Porter stemmer.a Java Maven project that contains the Terrier dependencies and a skeleton code to give youa start. NOTE: Tip #1 provides you with a restructured skeleton code to make the processingof queries more efficient.a template for your project report.What you need to produce You need to produce:correct implementations of the methods required by this project specificationscorrect evaluation, analysis and comparison of the evaluated methods, written up into areport following the provided templatea project report that, following the provided template, details: an explanation of the retrievalmethods used, an explanation of the evaluation settings followed, the evaluation of results(as described above), inclusive of analysis, a discussion of the findings.Required methods to implement In part 1 of the project you are required to implement the following retrieval methods:1. TF-IDF: you can create your own implementation using the Terrier API to extract indexstatistics, or use the implementation available through the Terrier API2. BM25: you can create your own implementation using the Terrier API to extract indexstatistics, or use the implementation available through the Terrier API3. The ranking fusion method Borda; you need to create your own implementation of this4. The ranking fusion method CombSUM; you need to create your own implementation of this5. The ranking fusion method CombMNZ; you need to create your own implementation of thisWe strongly reccommend you use the provided Maven project to implement these methods. Youshould have already attempted many of the implementations above as part of the tutorialexercises.In the report, detail how the methods were implemented, i.e. (i) which formula you implemented,(ii) if you did your own implementation or levereged Terrier’s ones (for TF-IDF and BM25).For ranking fusion methods, consider to fuse the runs from previous participants from CLEF2017/2018 we provide, and the TF-IDF and the BM25 runs you will produce.What queries to use We ask you to consider two types of queries for each topic (the second type is optional andattracts bonus points):1. for each topic, a query created from the topic title. For example, consider the example(partial) topic listed below: the query will be Rapid diagnostic tests for diagnosinguncomplicated P. falciparum malaria in endemic countries (you may considerperforming text processing).2. (OPTIONAL: 2% bonus if done) for each topic, a query created from the Boolean queryassociated with the topic. This Boolean query will be made up of the terms that appear inthe query, but will ignore any operator (e.g., will ignore and , or, Exp , / , etc.) and fieldrestrictions (e.g., .ti , .ab , .ti,ab , etc.). Note that some keywords in the Boolean queryhave been manually stemmed, e.g. diagnos* in the example topic below. As part of thequery creation process, we ask you to use the entrez API. For documentation on the entrezesearch API, please refer to the Entrez Programming Utilities Help reference available at:https://www.ncbi.nlm.nih.gov/books/NBK25499/#chapter4.ESearch. Example usage can befound at the following URL: https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=diagnos*. Note the terms in the TranslationStack field. These are theterms you would use to replace diagnosis* and therefore concatenate to form the query(along with the other terms).Above: example topic file More on the Entrez API The Entrez API provides access to the Pubmed search functionalities. In this part of the project wewill not use this API for retrieval. However, it also provide some additional method. One inparticular is useful for expanding terms in the Boolean query that have been “wildcarded”(manually stemmed): the TranslationStack . We have shown you above an example of how toobtain the output of the TranslationStack for a stem term. You will have to use this methodfor all terms in the Boolean query that contain the wildcard operator * . Practically, you will needto make a call to this API by constructing an appropriate URL, then request that URL, and finallyparsing the response to obtain the list of index terms to use to substitute the wildcarded termfrom the boolean query for inclusion in your text query. Note that it is likely that one wildcardedterm will give rise to many terms you will add to your query.Tips on making query processing efficient A number of tips have been provided in Blackboard to make the execution of queries moreefficient. Please consider these tips to reduce the execution time of the experiments. Required evaluation to perform In part 1 of the project you are required to perform the following evaluation:1. For all methods, train on the training set for the 2017 topics (train here means you use thisdata to tune any parameter of a retrieval model, e.g. and for BM25, runs to beconsidered for the rank fusion methods, etc.) and test on the testing set for the 2017 topics(using the parameter values you selected from the training set). Report the results of everymethod on the training and on the testing set, separately, into one table. Perform statisticalsignificance analysis across the results of the methods.2. Comment on the results reported in the previous table by comparing the methods on the2017 dataset.3. For all methods, train on the training set for the 2018 topics (train here means you use thisdata to tune any parameter of a retrieval model, e.g. and for BM25, runs to be Title: Rapid diagnostic tests for diagnosing uncomplicated P. falciparum malaria in endemic countries Query:1. Exp Malaria/2. Exp Plasmodium/3. Malaria.ti,ab4. 1or2or35. Exp Reagent kits, diagnostic/ 6. rapid diagnos* test*.ti,ab7. RDT.ti,ab8. Dipstick*.ti,abconsidered for the rank fusion methods, etc.) and test on the testing set for the 2018 topics(using the parameter values you selected from the training set). Report the results of everymethod on the training and on the testing set, separately, into one table. Perform statisticalsignificance analysis across the results of the methods.4. Comment on the results reported in the previous table by comparing the methods on the2018 dataset.5. Perform a topic-by-topic gains/losses analysis for both 2017 and 2018 results on the testingdatasets, by considering as baseline BM25, and as comparison each of TF-IDF, Borda,CombSUM and CombMNZ.6. Comment on trends and differences observed when comparing the findings from 2017 and2018 results. Is there a method that consistently outperform the others?7. Provide insights of when ranking fusion works, and when it does not, e.g. with respect toruns to be considered in the fusion process, queries, etc.In terms of evaluation measures, evaluate the retrieval methods with respect to mean averageprecision (MAP) using trec_eval . Remember to set the cut-off value ( -M , i.e. the maximumnumber of documents per topic to use in evaluation) to the number of documents to be re-ranked for each of the queries. Using trec_eval , also compute Rprecision (Rprec), which is theprecision after R documents have been retrieved (by default, R is the total number of relevantdocs for the topic).For all statistical significance analysis, use paired t-test; distinguish between p<0.05 and p<0.01.Perform the above analysis for: 1. queries created from topic files using the topic title; 2.(OPTIONAL) queries created from the topic files using the Boolean queries. Finish your analysis bycomparing the effectiveness difference between the methods using topic titles and those usingqueries extracted from the Boolean queries (OPTIONAL: to do only if you do consider Booleanqueries and want to obtain the bonus points).How to submit You will have to submit 3 files:1. the report, formatted according to the provided template, saved as PDF or MS Worddocument2. a zip file containing all the runs (result files) you have created for the implemented methods3. a zip file containing all the code to re-run your experiments. You do not need to include inthis zip file the runs we have given to you. You may need to include additional files e.g. if youmanually process the topic files into an intermediate format (rather than automaticallyprocess them from the files we provide you), so that we can re-run your experiments toconfirm your results and implementation.All items need to be submitted via the relevant Turnitin link in the INFS7410 Blackboard site, by 29August 2019 17:00, Eastern Australia Standard Time, unless you have been given an extension(according to UQ policy), before the due date of the assignment.INFS 7410 Project Part 1 – Marking Sheet – v2 Criterion % 7 100% 4 50% FAIL 1 0% IMPLEMENTATION The ability to: • Understand implement and execute common IR baseline • Understand implement and execute rank fusion methods • Perform text processing 2 • Correctly implements the specified baselines and the rank fusion methods • Implemented methods to deal with title queries • (OPTIONAL:) Implemented methods deal with Boolean queries, and wildcards are appropriately handled via expansion to possible forms using provided API (2% bonus) • Correctly implements the specified baselines and the rank fusion methods • No implementation • Implements only baselines, but not the rank fusion methods EVALUATION The ability to: • Empirically evaluate and compare IR methods • Analyse the results of empirical IR evaluation • Analyse the statistical significance difference between IR methods’ effectiveness 2 • Correct empirical evaluation has been performed • Uses all required evaluation measures • Correct handling of the tuning regime (train/test) • Reports all results for the provided query sets into appropriate tables • Provides graphical analysis of results on a query-by-query basis using appropriate gain-loss plots • Provides correct statistical significance analysis within the result table; and correctly describes the statistical analysis performed • Provides a written understanding and discussion of the results with respect to the methods • Provides examples of where fusion works, and were it does not, and why, e.g., discussion with respect to queries, runs. • Correct empirical evaluation has been performed • Uses all required evaluation measures • Correct handling of the tuning regime (train/test) • Reports all results for the provided query sets into appropriate tables • Provides graphical analysis of results on a query-by-query basis using appropriate gain-loss plots • Does not perform statistical significance analysis, or errors are present in the analysis • No or only partial empirical evaluation has been conducted, e.g. only on a topic set, or a subset of topics • Only report a partial set of evaluation measures • Fails to correctly handle training and testing partitions, e.g. train on test, reports only overall results WRITE UP Binary score: 0/1 The ability to: • use fluent language with correct grammar, spelling and punctuation • use appropriate paragraph, sentence structure • use appropriate style and tone of writing • produce a professionally presented document, according to the provided template 1 • Structure of the document is appropriate and meets expectations • Clarity promoted by consistent use of standard grammar, spelling and punctuation • Sentences are coherent • Paragraph structure effectively developed • Fluent, professional style and tone of writing. • No proof reading errors • Polished professional appearance • Written expression and presentation are incoherent, with little or no structure, well below required standard • Structure of the document is not appropriate and does not meet expectations • Meaning unclear as grammar and/or spelling contain frequent errors. • Disorganised or incoherent writing.