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The Impact of Result Diversification on Search Behaviour and Performance: Raw Entity Data

The Impact of Result Diversification on Search Behaviour and Performance: Raw Entity Data

This GitHub Gist contains a package of files pertaining to the raw data created for the user study The Impact of Result Diversification on Search Behaviour and Performance by David Maxwell (d.maxwell.1@research.gla.ac.uk), Leif Azzopardi (leif.azzopardi@strath.ac.uk) and Yashar Moshfeghi (yashar.moshfeghi@strath.ac.uk). The article was published in the Information Retrieval Journal.

Summary

Using the TREC 2005 Robust Track (and TREC AQUAINT newswire collection), the study reported examined how user behaviour and performance varied when subjects used either a diversified (or not) retrieval systems while performing either an ad-hoc or aspectual retrieval task. While existing data available for the collection used was sufficient for the ad-hoc retrieval task, data to the best of our knowledge were not available for the aspectual tasks.

Considering entities within each of the documents identified as relevant in the TREC 2005 Robust Track for the topics used, we manually assessed each of the documents that were marked relevant by TREC assessors (via the TREC 2005 Robust Track QRELs file) for the five topics trialled in this study. The five topics were:

  • 341, Airport Security;
  • 347, Wildlife Extinction;
  • 367, Piracy;
  • 408, Tropical Storms; and
  • 435, Curbing Population Growth.

By examining the topic descriptions provided as part of the TREC 2005 Robust Track, we deduced and agreed upon different entities to extract for each topic's set of relevant documents. Unique entities were assigned their own identifier number. From here, after agreement (refer to the paper for more information), we were then in a position to generate a TREC diversity format QRELs file, which can, in turn, be used by evaluation tools such as ndeval.

Package (Gist) Contents

In this package, you will find the following files:

  • trec2005_5topics.qrels, the raw TREC 2005 Robust Track QRELs file for the five topics considered;
  • desc_t341.txt, the topic description for topic 341;
  • desc_t347.txt, the topic description for topic 347;
  • desc_t367.txt, the topic description for topic 367;
  • desc_t408.txt, the topic description for topic 408;
  • desc_t435.txt, the topic description for topic 435;
  • entities.csv, the raw entities identified (along with their topic and unique identifier number);
  • trec2005_5topics_rels.dqrels, the resultant TREC diversity format QRELs file for relevant documents;
  • trec2005_5topics_nonrels.dqrels, a TREC diversity format QRELs file for non-relevant documents (i.e. a TREC judgement of 0); and
  • trec2005_5topics_combined.dqrels, a TREC diversity format QRELS file combining both relevant and non-relevant documents.

Note that the entire package can be downloaded as a ZIP file; click the Download ZIP file at the top right of this page.

File Structures

Topic descriptions are simple text files with the source topic title (first line) and description (second line onwards). These are provided for reference and are provided unchanged from the TREC 2005 Robust Track. trec2005_topics.qrels is also the TREC 2005 Robust Track QRELS file, but filtered to include only judgements for the five topics trialled.

The file entities.csv is a *NIX-formatted comma-separated value file (CSV). It consists of three columns: topic, entityid and entitytext. Assume that topic and entityid columns contain only integers, with the final entitytext column representing the entity as a string (e.g. the name of a seaworthy vessel for topic 367). Spaces and other non-alphanumeric characters (except commas) are permissible in the final column. topic represents the topic for the given entity, with entityid representing the unique identifier we assigned to each entity. These are not unique at a per-topic level. Note that entities.csv also contains a header line on the fist line.

The IDs provided in the entityid column then correspond to the entities listed in the trec2005_5topics_*.dqrels files. As previously mentioned, these are formatted as TREC diversity format files, with four columns separated by spaces. No headers are provided. On each line, the four columns represent:

  1. the topic number;
  2. the entity number (entityid);
  3. the AQUAINT document identifier; and
  4. 1 (denoting that the given entity appears within the listed document).

This structure is repeated over all three of the .dqrels files.

Why Judge Non-Relevant Documents?

Documents listed in the source QRELs file that were judged to be non-relevant (i.e. judgement of 0) for the five topics considered were also examined. Taking the list of entities for a given topic (i.e. from entities.csv), we automatically examined each non-relevant document, looking for an exact term match for each entity. If a match was found within a document, the entity was assigned to that document. This was done as to introduce noise into our diversity QRELs. The combined diversity QRELs file was therefore used to perform ranking with the diversifying algorithm employed in the study. This was done to avoid rankings becoming too perfect, where only relevant material would have bubbled up to the top.

Want to Use the Dataset?

We would love for you to use the dataset in your own experiments -- that's why we are releasing it! All we ask is that you cite our journal paper when referencing the dataset.

Questions?

We'll be happy to answer any questions that you have with this dataset. Just send one of us an e-mail. David Maxwell (lead author) should be your primary point of contact.

Airport Security
A relevant document would discuss how effective government orders to better scrutinize passengers and luggage on international flights and to step up screening of all carry-on baggage has been.
A relevant document would contain reports on what new steps airports worldwide have taken to better scrutinize passengers and their luggage on international flights and to step up screening of all carry-on baggage. With the increase in international terrorism and in the wake of the TWA Flight 800 disaster, articles on airport security relating in particular to additional steps taken by airports to increase flight safety would be relevant. The mere mention of enhanced security does not constitute relevance. Additional steps refer to something beyond just passenger and carry-on screening using the normal methods. Examples of new steps would be additional personnel, sophisticated monitoring and screening devices, and extraordinary measures to check luggage in the baggage compartment.
Wildlife Extinction
The spotted owl episode in America highlighted U.S. efforts to prevent the extinction of wildlife species. What is not well known is the effort of other countries to prevent the demise of species native to their countries. What other countries have begun efforts to prevent such declines?
A relevant item will specify the country, the involved species, and steps taken to save the species.
piracy
What modern instances have there been of old fashioned piracy, the boarding or taking control of boats?
Documents discussing piracy on any body of water are relevant. Documents discussing the legal taking of ships or their contents by a national authority are non-relevant. Clashes between fishing vessels over fishing are not relevant, unless one vessel is boarded.
tropical storms
What tropical storms (hurricanes and typhoons) have caused significant property damage and loss of life?
The date of the storm, the area affected, and the extent of damage/casualties are all of interest. Documents that describe the damage caused by a tropical storm as "slight", "limited", or "small" are not relevant.
curbing population growth
What measures have been taken worldwide and what countries have been effective in curbing population growth?
A relevant document must describe an actual case in which population measures have been taken and their results are known. The reduction measures must have been actively pursued; that is, passive events such as disease or famine involuntarily reducing the population are not relevant.
topic entityid entitytext
367 9 gts katie
367 6 mv mercs uhana
367 11 alfa bg
367 15 loishan
367 7 alondra rainbow
367 13 dolphine iv
367 4 nur star
367 5 tenyu
367 14 dolphine ix
367 8 cheung son
367 16 marine master
367 3 mv glory cape
367 10 randy-mar
367 12 obo engin
367 2 virgin pearl
367 1 achille lauro
367 0 petro ranger
367 17 global mars
347 148 peacock
347 91 antelope
347 163 greater one-horned rhino
347 130 ginseng
347 167 loggerhead turtle
347 161 choco frog
347 144 bear
347 149 heron crocodile
347 66 south china tiger
347 86 yangtze alligator
347 92 tibetan gazelle
347 36 javan rhino
347 113 siberian tiger
347 80 condor
347 106 swan
347 147 lion deer
347 139 red-crowned crane
347 32 basking shark
347 125 python
347 153 otistards
347 54 crested ibis
347 96 bare-headed geese
347 49 sambur
347 71 frontino bear
347 87 uganda kob
347 5 flamingo
347 121 striped hyena
347 75 chucuto monkey
347 145 moose lynx
347 64 saiga tatarica
347 126 bengal florican
347 68 margarita
347 21 hawksbill sea turtle
347 28 gray partridge
347 76 jaguar
347 100 giant salamander
347 83 parrot
347 159 sea turtle
347 85 green macaw
347 42 piping plover
347 1 gaur
347 103 snow leopard
347 88 hartebeest
347 143 kakapo
347 62 david's deer
347 150 turtle
347 154 gorsachius magnificus
347 112 rhesus monkey
347 99 fish
347 11 tree gecko
347 136 panda
347 59 sturgeon
347 29 thrush
347 24 water vole
347 48 snub-nosed monkey
347 146 ethiopian wolf
347 97 white rhino
347 58 wild camel
347 13 rhino
347 151 wild south china tiger
347 124 monitor lizard
347 133 sable
347 114 brown-eared pheasant
347 140 african grey parrot
347 102 chinese alligator
347 0 whale shark
347 34 squirrel-sized monkey
347 37 giant panda
347 123 gangetic dolphin
347 104 african lion
347 60 binturong
347 101 ibis
347 84 cadenalito
347 63 wild horse
347 30 aleutian canada geese
347 53 eared pheasant
347 105 ostrich
347 31 peregrine falcon
347 116 fish-eating crocodile
347 35 lynx
347 38 wolf
347 129 white stork
347 15 orangutan
347 95 brown-headed gull
347 120 four-horned antelope
347 160 wild ibis
347 164 malaysian rhino
347 132 pilose antler
347 152 clouded leopard
347 22 asian tiger
347 82 arpia eagle
347 67 cachicamo
347 98 boar
347 110 ranodon sibiricus
347 73 herring-eating whale
347 47 red deer
347 44 manchurian tiger
347 109 assam roof turtle
347 65 alligator
347 155 bufo stejnegeri
347 23 bumblebee
347 20 golden monkey
347 7 cuon javanicus
347 9 syrian brown bear
347 61 black-beaked gull
347 156 kiwi
347 135 hazel grouse
347 128 black stork
347 122 pangolin
347 3 tiger
347 115 great ape
347 165 brown bear
347 166 harbor porpoise
347 79 matacan deer
347 118 salt water crocodile
347 72 otter
347 8 arabian leopard
347 39 beluga whale
347 43 northern cricket frog
347 51 white lipped deer
347 162 mata mata turtle
347 2 red cow
347 108 black rhino
347 27 skylark
347 45 roe deer
347 12 sumatran rhino
347 138 white-lipped deer
347 41 burrowing owl
347 158 shark
347 107 northern white rhino
347 69 capuchino monkey
347 18 river terrapin
347 4 deer
347 94 pseudois nayaur
347 131 marten
347 19 african rhino
347 134 black bear
347 25 high brown fritillary butterfly
347 50 black-necked crane
347 141 mountain deer
347 33 greater bamboo lemur
347 17 leatherback turtle
347 127 lesser florican
347 55 tibetan wild ass
347 74 hump-backed whale
347 119 white flag dolphin
347 117 mud crocodile
347 16 elephant
347 111 salamander
347 40 swift fox
347 6 hornbill
347 14 tapir
347 142 elongated loach
347 137 leopard
347 77 cuanguaro
347 78 manatee
347 70 rabona lapa
347 157 whale
347 81 stone-crested pauji
347 93 argali sheep
347 26 pipistrelle bat
347 56 tibetan antelope
347 57 wild yak
347 10 blue-necked ostrich
347 52 blue sheep
347 46 spotted deer
347 90 donkeys
347 89 buffalo
341 1 ben gurion international airport
341 2 lagos airport
341 6 los angeles international airport
341 11 chicago o'hare international airport
341 13 laguardia international airport
341 4 john f kennedy international airport
341 8 george bush intercontinental airport
341 12 miami international airport
341 0 leonardo da vinci international airport
341 7 kansas city international airport
341 9 boston logan international airport
341 10 atlanta international airport
341 5 newark international airport
341 3 san francisco international airport
435 1 pakistan
435 21 myanmar
435 7 egypt
435 2 india
435 12 uganda
435 17 kenya
435 4 japan
435 13 brazil
435 10 italy
435 15 bangladesh
435 19 south africa
435 14 nepal
435 9 mongolia
435 20 ethiopia
435 24 rwanda
435 5 gaza
435 16 nigeria
435 11 iran
435 22 zambia
435 6 zimbabwe
435 3 china
435 18 tanzania
435 0 indonesia
435 8 vietnam
435 25 sri lanka
435 23 jordan
408 20 tropical storm harvey
408 21 hurricane cindy
408 13 hurricane floyd
408 4 typhoon zeb
408 2 hurricane ivan
408 7 typhoon elvis
408 12 hurricane bret
408 38 typhoon ditang
408 35 cyclone eline
408 27 typhoon gloria
408 28 hurricane fran
408 10 hurricane earl
408 40 tropical storm kaemi
408 31 typhoon linda
408 25 hurricane irene
408 17 hurricane iniki
408 11 tropical storm frances
408 16 typhoon kirogi
408 8 hurricane bonnie
408 36 indian cyclone orissa
408 22 tropical storm emily
408 14 hurricane dennis
408 26 tropical storm frankie
408 33 tropical storm nicole
408 32 tropical cyclone alan
408 23 tropical storm arlene
408 0 hurricane georges
408 6 hurricane mitch
408 30 tropical storm cida
408 18 hurricane bertha
408 24 hurricane lenny
408 39 kai-tak storm
408 9 tropical storm charley
408 37 tropical storm edeng
408 3 hurricane karl
408 29 tropical storm marcos
408 42 typhoon wukong
408 34 typhoon york
408 15 hurricane gert
408 19 hurricane andrew
408 1 hurricane jeanne
408 5 typhoon babs
408 41 typhoon maria
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367 17 XIE20000803.0091 1
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347 0 APW19980603.0561 1
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347 37 NYT19991013.0096 1
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347 37 NYT19991014.0096 1
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347 37 XIE19960118.0177 1
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347 30 APW19990729.0236 1
347 30 APW19990730.0127 1
347 53 XIE19960115.0239 1
347 105 XIE19970617.0276 1
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