A comparison between Latent Semantic Analysis and Correspondence Analysis

Transcription

1 A comparison between Latent Semantic Analysis and Correspondence Analysis Julie Séguéla, Gilbert Saporta CNAM, Cedric Lab Multiposting.fr February 9th CARME

2 Outline 1 Introduction 2 Latent Semantic Analysis Presentation Method 3 Application in a real context Presentation Methodology Results and comparisons 4 Conclusion A comparison between LSA & CA February 9th CARME 2 / 29

3 Introduction Outline 1 Introduction 2 Latent Semantic Analysis 3 Application in a real context 4 Conclusion A comparison between LSA & CA February 9th CARME 3 / 29

4 Introduction Context Text representation for categorization task A comparison between LSA & CA February 9th CARME 4 / 29

5 Introduction Objectives Comparison of several text representation techniques through theory and application In particular, comparison between a statistical technique : Correspondence Analysis, and an information retrieval (IR) oriented method : Latent Semantic Analysis Is there an optimal technique for performing document clustering? A comparison between LSA & CA February 9th CARME 5 / 29

6 Latent Semantic Analysis Outline 1 Introduction 2 Latent Semantic Analysis Presentation Method 3 Application in a real context 4 Conclusion A comparison between LSA & CA February 9th CARME 6 / 29

7 Latent Semantic Analysis Presentation Uses of LSA LSA was patented in 1988 (US Patent 4,839,853) by Deerwester, Dumais, Furnas, Harshman, Landauer, Lochbaum and Streeter. Find semantic relations between terms Helps to overcome synonymy and polysemy problems Dimensionality reduction (from several thousands of features to dimensions) Applications Document clustering and document classication Matching queries to documents of similar topic meaning (information retrieval) Text summarization... A comparison between LSA & CA February 9th CARME 7 / 29

8 Latent Semantic Analysis Method LSA theory How to obtain document coordinates? 1) Document-Term matrix 2) Weighting T =.... f ij.... T W =.... l ij (f ij ) g j (f ij ).... 3) SVD 4) Document coordinates in the latent semantic space : T W = UΣV C = U k Σ k We need to nd the optimal dimension for nal representation A comparison between LSA & CA February 9th CARME 8 / 29

9 Latent Semantic Analysis Method Common weighting functions Local weighting Term frequency l ij (f ij ) = f ij Binary l ij (f ij ) = 1 if term j occurs in document i, else 0 Logarithm l ij (f ij ) = log(f ij + 1) Global weighting Normalisation g j (f ij ) = 1 i f 2 ij IDF (Inverse Document Frequency) g j (f ij ) = 1 + log( n n j ) n : number of documents n j : number of documents in which term j occurs Entropy g j (f ij ) = 1 i f ij f.j log( f ij f.j ) log(n) A comparison between LSA & CA February 9th CARME 9 / 29

10 Latent Semantic Analysis Method LSA vs CA Latent Semantic Analysis 1) T = [f ij ] i,j 2) T W = [l ij (f ij ) g j (f ij )] i,j 3) T W = UΣV 4) C = U k Σ k Correspondence Analysis 1) T = [f ij ] i,j [ ] 2) T W = f ij f i. f.j 3) T W = UΣV 3 ) Ũ = diag( 4) C = Ũ k Σ k i,j f.. f i. )U CA : l ij (f ij ) = f ij fi. and g j (f ij ) = 1 f.j A comparison between LSA & CA February 9th CARME 10 / 29

11 Application in a real context Outline 1 Introduction 2 Latent Semantic Analysis 3 Application in a real context Presentation Methodology Results and comparisons 4 Conclusion A comparison between LSA & CA February 9th CARME 11 / 29

12 Application in a real context Presentation Objectives Corpus of job oers Find the best representation method to assess "job similarity" between oers in a non-supervised framework Comparison of several representation techniques Discussion about the optimal number of dimensions to keep Comparison between two similarity measures A comparison between LSA & CA February 9th CARME 12 / 29

13 Application in a real context Presentation Data Oers have been manually labeled by recruiters into 8 categories during the posting procedure Distribution among job categories : Category Freq. % Category Freq. % Sales/Business Development Marketing/Product R&D/Science 69 5 Production/Operations Accounting/Finance Human Resources Logistics/Transportation Information Systems Total We keep only the "title"+"mission description" parts ("rm description" and "prole searched" are excluded) A comparison between LSA & CA February 9th CARME 13 / 29

14 Application in a real context Methodology Preprocessing of texts Lemmatisation and tagging Filtering according to grammatical category (we keep nouns, verbs and adjectives) Filtering terms occuring in less than 5 oers Vector space model ("bag of words") A comparison between LSA & CA February 9th CARME 14 / 29

15 Application in a real context Methodology Several representations are compared Representation method LSA, weighting : Term Frequency LSA, weighting : TF-IDF LSA, weighting : Log Entropy CA Dissimilarity measure Euclidian distance between documents i and i 1 - cosine similarity between documents i and i A comparison between LSA & CA February 9th CARME 15 / 29

16 Application in a real context Methodology Method of clustering Clustering steps Computing of dissimilarity matrix from document coordinates in the latent semantic space Hierarchical Agglomerative Clustering until a 8 class partition Computation of class centroids K-means clustering initialized from previous centroids A comparison between LSA & CA February 9th CARME 16 / 29

17 Application in a real context Methodology Measures of agreement between two partitions P 1, P 2 : two partitions of n objects with the same number of classes k N = [n ij ] i=1,..,k : corresponding contingency table j=1,..,k Rand index R = 2 i j n2 ij i n2 i. j n2.j + n2 n 2, 0 R 1 Rand index is based on the number of pairs of units which belong to the same clusters. It doesn't depend on cluster labeling. A comparison between LSA & CA February 9th CARME 17 / 29

18 Application in a real context Methodology Measures of agreement between two partitions Cohen's Kappa and F-measure values are depending on clusters' labels. To overcome label switching, we are looking for their maximum values over all label allocations. Cohen's Kappa κ opt = max { 1 } n i n ii 1 n 2 i n i.n.i, κ 1 n i 2 n i.n.i F -measure F opt = max { k k i n ii i n i. 1 k n ii n i. + 1 k i n ii n.i i n ii n.i }, 0 F 1 A comparison between LSA & CA February 9th CARME 18 / 29

19 Application in a real context Results and comparisons Correlation between coordinates issued from the dierent methods A comparison between LSA & CA February 9th CARME 19 / 29

20 Application in a real context Results and comparisons Clustering quality according to the method and the number of dimensions : Rand index A comparison between LSA & CA February 9th CARME 20 / 29

21 Application in a real context Results and comparisons Clustering quality according to the method and the number of dimensions : Cohen's Kappa A comparison between LSA & CA February 9th CARME 21 / 29

22 Application in a real context Results and comparisons Clustering quality according to the method and the number of dimensions : F-measure A comparison between LSA & CA February 9th CARME 22 / 29

23 Application in a real context Results and comparisons Clustering quality according to the dissimilarity function : LSA + Log Entropy A comparison between LSA & CA February 9th CARME 23 / 29

24 Application in a real context Results and comparisons Clustering quality according to the dissimilarity function : CA A comparison between LSA & CA February 9th CARME 24 / 29

25 Conclusion Outline 1 Introduction 2 Latent Semantic Analysis 3 Application in a real context 4 Conclusion A comparison between LSA & CA February 9th CARME 25 / 29

26 Conclusion Conclusions CA seems to be less stable than other methods but with cosine similarity, it provides better results under 100 dimensions As it is said in literature, cosine similarity between vectors seems to be more adapted to textual data than usual dot similarity : slight increase of eciency and more stability for agreement measures Optimal number of dimensions to keep? It is varying according to the type of text studied and the method used (around 60 dimensions with CA) We should prefer a dissimilarity measure which provides stable results with the number of dimensions kept (in the context of automated tasks, it's problematic if optimal dimension is depending too much on the collection of documents) A comparison between LSA & CA February 9th CARME 26 / 29

27 Conclusion Limitations & future work Limitations of the study Clusters obtained are compared with categories choosen by recruiters, which are sometimes subjective and could explain some errors We are working on a very particular type of corpus : short texts, variable length, sometimes very similar but not really duplicates Future work Test other clustering methods (the representation to adopt may depend on it) Repeat the study with a supervised algorithm for classication (index values are disappointing in unsupervised framework) Study the eect of using the dierent parts of job oers for classication A comparison between LSA & CA February 9th CARME 27 / 29

28 Some references Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science, 41, Greenacre, M. (2007). Correspondence Analysis in Practice, Second Edition. London : Chapman & Hall/CRC. Landauer, T. K., Foltz, P. W., & Laham, D. (1998). Introduction to Latent Semantic Analysis. Discourse Processes, 25, Landauer, T. K., McNamara, D., Dennis, S., & Kintsch, W. (Eds.) (2007). Handbook of Latent Semantic Analysis. Mahwah, NJ : Erlbaum. Picca, D., Curdy, B., & Bavaud, F. (2006). Non-linear correspondence analysis in text retrieval : a kernel view. In JADT'06, pp Wild, F. (2007). An LSA package for R. In LSA-TEL'07, pp

29 Thanks!