Exploring Scientific Literature Search through Topic Models

With the fast growing amount of scientific literature, browsing through it can be a dicult task: formulating a precise query may be problematic as new research areas emerge quickly and different terms are often used to describe the same concept. To tackle some of these issues, we built a system for exploratory scientific search based on topic models. An initial short user study shows that through visualizing the relationship between keyphrases, documents and authors, the system allows the user to better explore the document search space compared to traditional systems based solely on search query.

[1]  W. Bruce Croft,et al.  Investigating Retrieval Performance with Manually-Built Topic Models , 2007, RIAO.

[2]  Wei Li,et al.  Pachinko allocation: DAG-structured mixture models of topic correlations , 2006, ICML.

[3]  Chong Wang,et al.  Reading Tea Leaves: How Humans Interpret Topic Models , 2009, NIPS.

[4]  Dorota Glowacka,et al.  Directing exploratory search: reinforcement learning from user interactions with keywords , 2013, IUI '13.

[5]  Ping Wang,et al.  PULP: A System for Exploratory Search of Scientific Literature , 2016, SIGIR.

[6]  Dorota Glowacka,et al.  Beyond Relevance: Adapting Exploration/Exploitation in Information Retrieval , 2016, IUI.

[7]  Dorota Glowacka,et al.  Is exploratory search different? A comparison of information search behavior for exploratory and lookup tasks , 2016, J. Assoc. Inf. Sci. Technol..

[8]  Li Chen,et al.  A user-centric evaluation framework for recommender systems , 2011, RecSys '11.

[9]  Dorota Glowacka,et al.  Using Topic Models to Assess Document Relevance in Exploratory Search User Studies , 2017, CHIIR.

[10]  Dorota Glowacka,et al.  Improving Controllability and Predictability of Interactive Recommendation Interfaces for Exploratory Search , 2015, IUI.

[11]  James Allan,et al.  A Comparative Study of Utilizing Topic Models for Information Retrieval , 2009, ECIR.

[12]  Tovi Grossman,et al.  Citeology: visualizing paper genealogy , 2012, CHI EA '12.

[13]  Gary Marchionini,et al.  Exploratory search , 2006, Commun. ACM.

[14]  Ram Akella,et al.  Incorporating Statistical Topic Models in the Retrieval of Healthcare Documents , 2015, CLEF.

[15]  Dorota Glowacka,et al.  Balancing Exploration and Exploitation: Empirical Parameterization of Exploratory Search Systems , 2015, CIKM.

[16]  M. Narasimha Murty,et al.  On Finding the Natural Number of Topics with Latent Dirichlet Allocation: Some Observations , 2010, PAKDD.

[17]  J. B. Brooke,et al.  SUS: A 'Quick and Dirty' Usability Scale , 1996 .

[18]  Kevin Li,et al.  Faceted metadata for image search and browsing , 2003, CHI '03.

[19]  Thomas L. Griffiths,et al.  Learning author-topic models from text corpora , 2010, TOIS.

[20]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[21]  Xin Fu,et al.  Elicitation of term relevance feedback: an investigation of term source and context , 2006, SIGIR.