Document Retrieval for Large Scale Content Analysis using Contextualized Dictionaries

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1 TKE 2014 Document Retrieval for Large Scale Content Analysis using Contextualized Dictionaries Gregor Wiedemann Andreas Niekler NLP Group Department of Computer Science University of Leipzig Augustusplatz Leipzig

2 Outline 1) Motivation 2) Dictionary creation with topic models 3) Contextualizing dictionaries 4) Retrieval with dictionaries 5) Evaluation 2

3 Motivation social science business intelligence content analysis media studies political science... How is European identity framed in newspapers? How (often) do policy makers refer to concepts of social or distributive justice? Is there a neoliberal economization of political justifications in the public policy debate? 3

4 How to find relevant documents Information Retrieval: obtaining documents relevant to an information need by querying a collection standard query: small key word set Puzzling question: How can analysts represent their (rather abstract) information need? small keyword set Idea: compilation of a reference collection of paradigmatic documents paradigmatic document = document knowingly containing information / language use of interest for CA purpose 4

5 Example use case Political science study on neoliberalism Is there a neoliberal economization of political justifications in the public policy debate? Target collection Reference collection 36 works of confessed neoliberals (Mont Pelerin Society) e.g. Milton Friedman, F.A. Hayek etc. 400,000 news paper articles from DIE ZEIT ( ) Hayek 5

6 Approach 3 steps of retrieval for content analysis purpose 1) extraction of ranked dictionary from reference collection 2) extraction of co-occurrence data from reference collection 3) relevancy scoring of documents in target collection with dictionary + co-occurrence data 6

7 dictionary creation dictionary automatically or manually compiled set of (several hundred) keywords representing conceptual / domain knowledge rank information automatic dictionary creation: unequal importance of terms term extraction task from reference collection e.g. TF/IDF, LL-measure of frequencies between two compara,... 7

8 dictionary creation via topic models Topic Models: statistical models (e.g. LDA, Blei et. al 2003) to extract latent semantic structure from collections distribution over K topics in documents distribution over words in topics p(w zk ) idea: term probability can be used to score weight of dictionary terms K tw n =log (tf (w n )) k=1 p(w n z k ) 8

9 example study Proba bility Top 10 Words mensch, freiheit, gesellschaft, gesetz, regeln, allgemein, grupp, ziel, bestimmt, regier [human, freedom, society, law, rules, common, group, aim, certain, govern] einkomm, gut, zeit, haushalt, konsum, kost, straftat, preis, wert, gleichung [income, goods, time, budget, consume, cost, offense, price, value, equation] steu, gut, offent, period, steuersatz, beschrank, staatlich, einnahm, steuerzahl, besteuer [tax, goods, public, period, tax rate, constraint, state, revenue, tax payer, taxation] polit, analys, regeln, okonom, theori, modell, verhalt, ansatz, frag, polit [political, analysis, rules, economic, theory, model, behaviour, approach, question, politics] Term... Extracting dictionary of 500 highest ranked terms Weigth einkomm [income] preis [price] gut [goods] polit [political] zeit [time] hoh [high] kost [cost] regeln [rules] mensch [human] offent [public] person [person] regier [government] wert [value] inflation [inflation] analys [analysis] bestimmt [certain] allgemein [common]

10 contextualizing dictionaries need for more subtle meaning representation in content analysis IR co-occurrence data captures meaning of terms (distributional semantics hypothesis) Term-Term-Matrix C: computation of significant co-ooccurrences of dictionary terms from reference collection sentence window dictionary of length N C dice measure (0;1) reflects syntagmatic relations 10

11 contextualizing dictionaries filtering for reference corpus specific co-occurrences computation of significant co-ooccurrences of dictionary terms from (large) randomly composed corpus (e.g. Leipzig Corpora Collection) term-term matrix D C '=max(c D,0) 11

12 Example study example term C C' öffentlich [public] beitrag [contribution] eltern [parents] gut privat meinung ausgabe schule leisten wichtig insbesondere sozial größen kind alter schule humankapital altruismus gut ausgabe meinung privat theorie leisten insbesondere größen sozial buch kind alter humankapital schule altruismus common co-occurrences in general langague get lower values in C' specific co-occurrences from reference collection stay high C' 12

13 Retrieval with dictionaries Vector Space Model Translate dictionary ranks into boosting factors 13

14 Retrieval with dictionaries Applying length normalization Applying contextual similarity Final scoring formula 14

15 Retrieval with dictionaries Applying length normalization Applying contextual similarity Final scoring formula 15

16 Example study score length year title 347, Pro und kontra Mehrwertsteuer [Pro's and con's of VAT] 321, Oelkrise und Konjunktur [Oil crisis and economy] 290, Energie muß billig sein [Energy has to be cheap] 289, Die Steuern senken [Lower the taxes] 287, Korrektur der Einkommensteuer [Correction of VAT] 281, Die Bauern im Nacken [The farmers at the neck] 279, Was ist uns die Mark wert? [What is the Mark worth to us?] 272, Steuern mit der Steuer [Governing with taxes] 264, Ohne Abkühlung keine Stabilität [No stability without slowdown] 262, Das sicherste Mittel [The most secure instrument] 261, Entlastung wovon? [Relief of what?] 254, Das Fernsehen und die Angst [Television and fear] 254, Nicht ernst gemeint: die Quote [Quotas not meant serious] 251, Eine Konfliktstrategie der Union [A conflict strategy of the EU] Selecting the top 2,000 ranks of retrieved documents for further Content Analysis 16

17 Evaluation I 2 purposes for evaluation 1) determining optimal α 2) assessing quality of - score context vs. score VSM - topic models vs. tf-idf for dictionary creation no gold standard data set for this retrieval task 2 alternative appoaches Approach 1: Generating pseudorels by data fusion (Nuray/Can 2006): create set of pseudorelevant documents from best ranked documents of most distinctive retrieval systems consider tf-idf / topic model + different α values as different systems evaluate mean average precisions (MAP) of all systems 17

18 Evaluation I 4 most distinctive systems: Dtf-idf + score VSM [α=0] Dtf-idf + score context [tf(w,s)=0] DTopicModel + score VSM [α=0] DTopicModel + score context [tf(w,s)=0] 54 documents as pseudorels 18

19 Evaluation II Approach 2: Precision at k evaluation of example retrieval by domain experts Conclusion 1. improvement of retrieval results by mixing of unigram and co-occurrence information from reference collections 2. further improvement by dictionary extraction with topic models approach enables domain experts to query large collections for texts representing rather abstract domain knowledge 19

20 Literature Alsumait, L., Barbara, D., Gentle, J., Domeniconi, C.: Topic signicance ranking of LDA generative models. In: ECML/PKDD '09: Part I. pp (2009) Biemann, C., Heyer, G., Quastho, U., Richter, M.: The leipzig corpora collection. Monolingual corpora of standard size. In: Corpus Linguistic 2007 (2007) Billhardt, H., Borrajo, D., Maojo, V.: Using term co-occurrence data for document indexing and retrieval. In: In Proceedings of the 22nd IRSG. pp (2000) Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, (2003) Bordag, S.: A comparison of co-occurrence and similarity measures as simulations of context. In: Proceedings of the 9th CICLing. pp (2008) Krippendor, K.: Content analysis: An introduction to its methodology. SAGE, 3 edn. (2013) Nuray, R., Can, F.: Automatic ranking of information retrieval systems using data fusion. Information Processing & Management 42(3), (2006) Peat, H., Willet, P.: The limitations of term co-occurrence data for query expansion in document retrieval systems. ASIS Journal 42(5), 378{383 (1991) 20

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