Document Clustering for Mediated Information Access The WebCluster Project

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1 Document Clustering for Mediated Information Access The WebCluster Project School of Communication, Information and Library Sciences Rutgers University The original WebCluster project was conducted at the Robert Gordon University, Aberdeen, UK. It was supervised by Prof. David J. Harper and sponsored by Ubilab, Zurich. Current work is being conducted in collaboration with Ph.D. student Hyuk-Jin Lee and Prof. Nicholas J. Belkin. Exploratory Search Interfaces: Categorization, Clustering and Beyond Workshop at HCIL 2005, University of Maryland, June 2, 2004

2 WebCluster - Motivation Information Need (within some subject domain) WWW_SearchEngine Query Search engine Domain Gulfs information need query structured subject domain unstructured target collection (WWW)

3 Interaction in the library Information need 1. Select library 2. Consult catalog Information Need Formulation 3. Browse shelves 4. Use inter-library scheme

4 Can we simulate the library interaction? Information need Structured source collections 3. Search WWW 1. Select source collection Results Information Need Formulation Results 2. Explore source collection with ClusterBook

5 The mediated access interaction Information need WebCluster Specialised source Query Web search engine Topical documents Target collection (WWW)

6 Interaction model vs. prototype Structuring the source collection w Document clustering w Supervised classification w Manual (intellectual) classification Exploring the structured source collection w Metaphor Library, book, encyclopaedia w Visualization tool Folder metaphor, hyperbolic tree, themescape, cone trees, thematic maps w Search strategies supported Best match or cluster-based searching, browsing

7 Model vs. prototype Interaction model w Explicit (the user marks relevant documents) vs. implicit (cues on relevance are derived based on user behavior/actions) w Transparent (the user is aware) vs. opaque (the user is happy to see effect of magic ) w Automatic vs. manual/intellectual generation of the mediated query Query model w Language models (generative, Kullback-Leibler) w Probabilistic models w Rocchio or other RF-specific formulae

8 ClusterBook - Source collection

9 ClusterBook - Target collection

10 Informal experiments - Objectives - Test the users reaction to the mediated access concept Test the user satisfaction regarding the functionality of the system, and the relevance of the documents retrieved Formative usability testing - some volunteers were not only experienced searchers, but also had experience in evaluating IR systems Comparison of user generated queries vs. system generated queries Note. These experiments were run at different stages of the development

11 Informal experiments - Experimental procedure - Subjects received introduction to the system Task assigned: You are a trainee in a newspaper. You support the journalists by providing information for the topic of their articles. Sample topics: w The history of the Brasilian debt crisis w How are the quotas for growing coffee set and controlled on a world-wide basis? Source collection: a sub-collection of Reuters (newspaper articles) Steps followed by users (explicit scenario): w Formulate a query and record it w Browse source collection, select best cluster, edit query generated by system, submit it to the search engine w Submit to the same search engine the initial, self-generated query w Compare results of the two searches

12 Informal experiments - Results - Users found the mediation useful for unfamiliar topics The system nearly always proposed new, good query terms Users not always good at recognizing good query terms The system proposed bad query terms (not specific to the topic) the opaque scenario not viable unless the query formulation is improved The two-step process was questioned when: w the query formulation was considered easy, for a familiar topic w the documents of the source collection were considered sufficient to cover the information need Complete link, group average OK; single link bad Overall, the system is usable

13 Consequences of informal experiments Formal experiments are needed to verify the main assumptions: w The Cluster Hypothesis holds for a specialized collection w Good clusters can be found with the search strategies provided w Mediated queries can improve retrieval effectiveness The effect on retrieval performance of various parameters should be compared w Weighting schemes w Clustering methods w Search strategies

14 Critical issue: The label generation w Document representatives w searching w Cluster representatives w browsing w searching w mediation w Collection representatives w collection selection Wind Energy Power Generation Propulsion Fixed Plants... Coastal Wind Farms Inland Wind Farms Portable Generators... Pacific Rim Wind Farms Design of Coastal Wind Farms Desert Design of Wind Farms. Wind generators for yachts

15 Mediation experiment - simulations Objectives: w Test the potential of mediation to increase retrieval effectiveness w Test the effect on performance of a variety of parameters Cluster-based mediation (realistic mediation) Search engine Topic-based mediator (upperbound) Search engine Source collection Simple query generator (baseline) Target collection

16 Experimental setup Interactive track of TREC-8 w Offers relevance judgments for complex topics, with a multitude of aspects w Offers the experimental design for the user experiment w Six topics with 12 to 56 aspects each w Target collection: FT , with 210,158 articles w Source collection built based on relevance judgments: half of the relevant documents, their nearest neighbors, plus the documents judged non-relevant

17 Results the cluster hypothesis Aspectual cluster hypothesis confirmed by an extended version of the van Rijsbergen Sparck Jones separation test w Similarity between pairs of docs covering the same aspect is higher than between pairs of docs covering the same topics, which is higher than between pairs of docs in the collection Consequence confirmed: clustering groups documents in pockets of relevance

18 Results retrieval effectiveness Tf-Idf > KL > RelFreq as weighting schemes for document representation Adding disambiguation terms to the query increases recall, but decreases precision Nearest-neighbor mediation ( more like this ) highly significantly improves both recall and precision, even if just one exemplary document is offered for each topic aspect Cosine and Dice performs similarly

19 Mediation results Upperbound experiment (all relevant docs known in source) w Both recall and precision increase with query length w Query term weights strongly affect performance w No evidence that uniformity of term frequency affects performance Clustered source mediation w Best cluster mediation increases P, decreases R w Fuse and search strong increase in R and P w Search and fuse good R, terrible P!

20 Result presentation (within subjects) User experiment effectiveness of mediated information retrieval for Web searches Query formulation (between subjects) Linear (list) Structured (cluster) Unaided Baseline On the fly clustering Mediated Source-based mediation Source & target based mediation

21 User experiment no mediation

22 User experiment mediated access

23 User experiment mediated access

24 Contributions of WebCluster Proposes and explores system-based mediated access to very large heterogeneous document collections Explores the use of clustering for capturing the topical, semantic structure of a problem domain (as represented by a specialized collection) Explores the use of language models for building cluster and document representatives Offers a framework for building structured portals on the WWW Offers a framework for building collaborative environments

25 WebCluster - Other applications CD-ROM based collections w structured access to the collection itself w mediated access to WWW (via CD-ROM) Mediated access (portals) via hierarchically structured information sources Examples are: via large structured report (e.g. government reports), via structured collection of information (e.g. encyclopaedia), via intranet Multimedia information access w cluster multimedia source, e.g. annotated photographs w mediated access to other photographs (not annotated)

26 Other directions for WebCluster Clustered vs. categorized source collection Language model based labels vs. specialized terminology based on a domain ontology / thesaurus Different interaction and visualization metaphors w Spring-embedded algorithms for 2D representation of clusters Various inter-document similarities (faceted?) User profiles / personalization w Change of interest over time

27 Topic representation What (weighted) terms best describe a topic? w Applications: w Clustering generating cluster representatives w Mediation generating mediated queries w Machine generation w Simulation based on test topics and relevance judgments w Use various weighting formulae and cut-off points w Which representations are more effective? What do they have in common / specific? w Human (manual / intellectual generation) w Compare queries generated by searchers in TREC tasks Effectiveness Keyword vs. natural language queries

28 Questions?

29 Query formulation problems Vague information need Vocabulary mismatch Difficulty of query language syntax Lack of context, ambiguity of terms Lack of a search strategy No understanding of the underlying indexing/searching model Note. TREC experiments have shown that the quality of the query has a higher impact on retrieval effectiveness than weighting schemes or search algorithms.

30 Role of structure Reveals the semantic structure of the domain & its concepts Groups (semantically?) similar documents Supports exploration and concept formation Supports term disambiguation (context) (Has potential for efficient retrieval) (Has potential for effective retrieval) Science... Computing, Mathematics Physics Computing Computer Programming language Mathematics... Screen Keyboard Pascal C++ Algebra

31 R i = Browsing label (relative cluster representative) KL ( cluster, parent) = i p i, cluster log p p i, cluster i, parent Wind Energy Power Generation Propulsion... Fixed Plants Coastal Wind Farms Inland Wind Farms Portable Generators... Pacific Rim Wind Farms Design of Coastal Wind Farms Desert Wind Farms Design of. Wind generators for yachts

32 A i = Searching label (absolute cluster representative) KL ( cluster, collection) = i p i, cluster log p p i, cluster i, collection Wind Energy Power Generation Propulsion... Fixed Plants Coastal Wind Farms Inland Wind Farms Portable Generators... Pacific Rim Wind Farms Design of Coastal Wind Farms Desert Wind Farms Design of. Wind generators for yachts

33 E i = (1 ω) + (1 ω) ω Mediation label (Expanded cluster representative) A r 1 i,0 A + (1 ω) ω i, r 1 + r ω A i, r A i,1 2 + (1 ω) ω A i,2 Wind Energy +... Power Generation Propulsion... Fixed Plants Coastal Wind Farms Inland Wind Farms Portable Generators... Pacific Rim Wind Farms Design of Coastal Wind Farms Desert Wind Farms Design of. Wind generators for yachts

34 Topic model representations Exemplary representation Statistical analysis Statistical representation Language model Context analysis Keyword representation Typical terms, weighted Thresholding Mediated query

35 The cluster hypothesis Reminder: the original cluster hypothesis w Closely associated documents tend to be relevant to the same requests (van Rijsbergen) Aspectual cluster hypothesis: Highly similar documents tend to be relevant to the same topic. However, documents relevant to the same topic may be quite dissimilar if they cover distinct aspects of the topic. w Consequence: Clustering algorithms tend to group together documents that cover highly focused topics, or aspects of complex topic. Documents covering distinct aspects of complex topics tend to be spread over the cluster structure.

36 Aspects of relevance in the mediated access process

37 Distribution of relevant documents in clusters Clustering vs. Random 25% 20% 15% Recall 10% 5% 0% Clusters Clustering Random

38 Name w Transparent mediated access Targeted users w Experienced searchers WebCluster scenario#1 Web Search Engine WWW c 0 c 3 c 2 c 5 Specific w The users are aware of the mediation process, of the separation between the source and target collections w The users have the option to edit the query generated (proposed) by the system. They understand the indexing / searching model. W e b C l u s t e r c 0 c 1 c 2 c 3 c 4 c 5 Document from the source collection Document from the target collection (WWW)

39 WebCluster scenario#2 Name W e b C l u s t e r c 0 c 1 c 2 c 3 c 4 c 5 w Opaque mediated access Targeted users w Naive / beginner searchers Web Search Engine c 3 WWW c 0 c 2 c 5 Document from the source collection Document from the target collection (WWW) Specific w The users explore the structure of the domain, which contains sample documents, and have the option of asking for similar documents w The users are unaware of the mediation process - the query generation and target search are not visible

40 Initial user interface (Java AWT)

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