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- 1. Text classification with Lucene/Solr and LibSVM By Majirus FANSI, Phd @majirus Agile Software Developer
- 2. Motivation: Guiding user search ● Search engines are basically keyword-oriented – What about the meaning? ● Synonym search needs listing the synonyms ● More-Like-This component is about more like THIS ● Category search for better user experience – Deals with the cases where user keywords are not in the collection – User searches for « emarketing », you returns documents on « webmarketing »
- 3. Outline ● Text Categorization ● Introducing Machine Learning ● Why SVM? ● How Solr can help ? Putting it all Together is our aim
- 4. Text classification or Categorization ● ● ● Aims – Classify documents into a fixed number of predefined categories ● Each document can be in multiple, exactly one, or no category at all. Applications – Classifying emails (Spam / Not Spam) – Guiding user search Challenges – Building text classifiers by hand is difficult and time consuming – It is advantageous to learn classifiers from examples
- 5. Machine Learning ● Definition (by Tom Mitchell - 1998) “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E” ● Experience E: watching the label of a document ● Task T: classify a document ● Performance P: probability that a document is correctly classified.
- 6. Machine Learning Algorithms ● ● Usupervised learning – Let the program learn by itself ● Market segmentation, social network analysis... Supervised learning – Teach the computer program how to do something – We give the algorithm the “right answers” for some examples
- 7. Supervised learning problems – Regression ● ● – Predict continuous valued output Ex: price of the houses in Corbeil Essonnes Classification ● Predict a discrete valued output (+1, -1)
- 8. Supervised learning: Working m training examples Training Set (X, Y) (X(i),Y(i)) : ith training example X's : input variable or features Y's : output/target variable It's the job of the learning algorithm to produce the model h Training algorithm Feature vector (x) h(x) Hypothesis h Predicted value (y)
- 9. Classifier/Decision Boundary ● ● ● ● Carves up the feature space into volumes Feature vectors in volumes assigned to the same class Decision regions separated by surfaces Decision boundary linear if a straight line in the dimensional space – A line in 2D, a plane in 3D, a hyperplane in 4+D
- 10. Which Algorithm for text classifier
- 11. Properties of text ● ● ● ● High dimensional input space – More than 10 000 features Very few irrelevant features Document vectors are sparse – few entries which are non zero Most text categorization problems are linearly separable No need to map the input features to a higher dimension space
- 12. Classification algorithm /choosing the method ● ● Thorsten Joachims compares SVM to Naive Bayes, Rocchio, K-nearest neighbor and C4.5 decision tree SVM consistently achieve good performance on categorization task – It outperforms the other methods – Eliminates the need for feature selection – More robust than the other Thorsten Joachims, 1998. Text Categorization with SVM : Learning with many relevant features
- 13. SVM ? Yes But... « The research community should direct efforts towards increasing the size of annotated training collections, while deemphasizing the focus on comparing different learning techniques trained only on small training corpora » Banko & Brill in « scaling very very large corpora for natural language disambiguation »
- 14. What is SVM - Support Vector Machine? ● ● « Support Vector Networks » Cortes & Vapnik, 1995 SVM implements the following idea – Maps the input vectors into some high dimensional feature space Z ● Through some non linear mapping choosing a priori – In this feature space a linear decision surface is constructed – Special properties of the decision surface ensures high generalization ability of the learning machine
- 15. SVM - Classification of an unknown pattern classification w1 sv1 w2 sv2 X wN svk Support vectors zi in feature space Input vector in feature space Non-linear transformation x Input vector, x
- 16. SVM - decision boundary ● ● Optimal hyperplane – Training data can be separated without errors – It is the linear decision function with maximal margin between the vectors of the two classes Soft margin hyperplane – Training data cannot be separated without errors
- 17. Optimal hyperplane
- 18. Optimal hyperplane - figure x2 Op tim al hy pe rp la ne Optimal margin x1
- 19. SVM - optimal hyperplane ● ● Given the training set X of (x1, y1), (x2, y2), … (xm, ym) ; yi Є{-1, 1} X is linearly separable if there exists a vector w and a scalar b s.t. w.x i +b≥1 if y i =1(1) w.x i +b≤−1 if y i =−1(2) ● Vectors xi for which yi (w.xi+b) = 1 is termed support vectors – Used to construct the hyperplane – if the training vectors are separated without errors by an optimal hyperplane E [Pr (error)]≤ ● (1), (2)⇒ y i ( w.xi +b)≥1(3) E[number of support vectors] (4) number of training vectors w . z +b =0(5) The optimal hyperplane – Unique one which separates the training data with a maximal margin 0 0
- 20. SVM - optimal hyperplane – decision function ● Let us consider the optimal hyperplane w 0 . z +b 0=0(5) ● The weight w0 can be written as some linear combination of SVs w 0= ∑ αi z i (6) support vectors ● The linear decision function I(z) is of the form I ( z )=sign( ∑ α z . z+ b )(7) i i 0 support vectors ● zi.z is the dot product between svs zi and vector z
- 21. Soft margin hyperplane
- 22. Soft margin Classification ● Want to separate the training set with a minimal number of errors Φ (ξ)=∑ ξ ; ξ ≥0 ; for small σ> 0(5) m i=1 s.t. ● ● σ i i y i (w.xi + b)≥1−ξ i ; i=1,... , m(6) The functional (5) describes the number of training errors Removing the subset of training errors from training set ● Remaining part separated without errors ● By constructing an optimal hyperplane
- 23. SVM - soft margin Idea ● Soft margin svm can be expressed as m 1 2 w +C ∑ ξi (7) w , b ,ξ 2 i =1 min s.t. y i (w.xi + b)≥1−ξ i ● ξ i≥0 (8) For sufficiently large C, the vector w0 and constant b0, that minimizes (7) under (8) determine the hyperplane that – minimizes the sum of deviations, ξ, of training errors – Maximizes the margin for correctly classified vectors
- 24. SVM - soft margin figure x2 ξ=0 se pa ra ξ=0 to r 0<ξ<1 soft margin ξ>1 x1
- 25. Constructing text classifier with SVM
- 26. Constructing and using the text classifier ● ● ● ● Which library ? – Efficient optimization packages are available ● SVMlight, LibSVM From text to features vectors – Lucene/solr helps here Multi-class classification vs One-vs-the-rest Using the categories for semantic search ● Dedicated solr index with the most predictive terms
- 27. SVM library ● ● ● SVMlight – By Thorsten Joachim LibSVM – By Chan & Lin from Taiwan university – Under heavy development and testing – Library for java, C, python,...,Package for R language LibLinear – By Chan, Lin & al. – Brother of LibSVM – Recommended by LibSVM authors for large-scale linear classification
- 28. LibLinear ● ● A Library for Large Linear Classification – Binary and Multi-class – implements Logistic Regression and linear SVM Format of training and testing data file is : – <label> <index1>:<value1><index2>:<value2>... – Each line contains an instance and is ended by a 'n' – <label> is an integer indicating the class label – The pair <index>:<value> gives a feature value ● <index> is an integer starting from 1 ● <value> is a real number – Indices must be in ascending order
- 29. LibLinear input and dictionary ● ● Example input file for training 1 101:1 123:5 234:2 -1 54:2 64:1 453:3 – Do not have to represent the zeros. Need a dictionary of terms in lexicographical order 1 .net 2 aa ... 6000 jav ... 7565 solr
- 30. Building the dictionary ● ● Divide the overall training data into a number of portions – Using knowledge of your domain ● Software development portion ● marketing portion... – Avoid a very large dictionary ● A java dev position and a marketing position share few common terms Use Expert boolean queries to load a dedicated solr core per domain – description:python AND title:python
- 31. Building the dictionary with Solr ● What do we need in the dictionary – Terms properly analyzed ● LowerCaseFilterFactory, StopFilterFactory, ● ASCIIFoldingFilterFactory, SnowballPorterFilterFactory – ● Terms that occurs in a number of documents (df >min) ● Rare terms may cause the model to overfit Terms are retrieved from solr – Using solr TermVectorsComponent
- 32. Solr TermVectorComponent ● SearchComponent designed to return information about terms in documents – tv.df returns the document frequency per term in the document – tv.tf returns document term frequency info per term in the document ● Used as feature value – tv.fl provides the list of fields to get term vectors for ● Only the catch-all field we use for classification
- 33. Solr Core configuration ● Set termvectors attribute on fields you will use – <field name="title_and_description" type="texte_analyse" indexed="true" stored="true" termVectors="true" termPositions="true" termOffsets="true"/> Normalize your text and use stemming during the analysis Enable TermVectorComponent in solrconfig – ● – <searchComponent name="tvComponent" class="org.apache.solr.handler.component.TermVectorComponent"/> – Configure a RequestHandler to use this component ● <lst name="defaults"> <bool name="tv">true</bool> </lst> ● <arr name="last-components"> <str>tvComponent</str> </arr>
- 34. Constructing Training and Test sets per model
- 35. Feature extraction ● Domain expert query is used to extract docs for each category – – – TVC returns the terms info of the terms in each document Each term is replaced by its index from the dictionary ● This is the attribute Its tf info is used as value ● Some use presence/absence (or 1/0) ● Others tf-idf term_index_from_dico:term_freq is an input feature
- 36. Training and Test sets partition ● ● ● We shuffle documents set so that high score docs do not go to the same bucket We split the result list so that – 60 % to the training set (TS) ● Here are positive examples (the +1s) – 20 % to the validation set (VS) ● Positive in this model, negative in others – 20 % is used for other classes training set (OTS) ● These are negative examples to others Balanced training set (≈50 % of +1s and ≈50 % of -1s) – The negatives come form other's 20 % OTS
- 37. Model file ● Model file is saved after training – One model per category – It outlines the following ● solver_type L2R_L2LOSS_SVC ● nr_class 2 ● label 1 -1 ● nr_feature 8920 ● bias 1.000000000000000 ● w -0.1626437446641374 w.xi + b ≥ 1 if yi = 1 ● ● ● 0 7.152404908494515e-05
- 38. Most predictives terms ● ● Model file contains the weight vector w Use w to compute the most predictves terms of the model – Give an indication as to whether the model is good or not ● You are the domain expert – Useful to extend basic keyword search to semantic search
- 39. Toward semantic search - Indexing ● ● Create a category core in solr – Each document represents a category ● One field for the category ID ● One multi-valued field holds its top predictives terms At indexing time – Each document is sent to the classification service – The service returns the categories of the document – Categories are saved in a multi-valued field along with other domain-pertinents document fields
- 40. Toward semantic search - searching ● At search time – User query is run on the category core ● What about libShortText – The returned categories are used to extend the initial query ● A boost < 1 is assigned to the category
- 41. References ● ● ● ● ● Cortes and Vapnik, 1995. Support-Vector Networks Chang and Lin, 2012. LibSVM : A Library for Support Vector Machines Fan, Lin, et al. LibLinear, 2012 : A Library for Large Linear Classification Thorsten Joachims, 1998. Text Categorization with SVM : Learning with many relevant features Rifkin and Klautau, 2004. In Defense of One-Vs-All classification
- 42. A big thank you ● Lucene/Solr Revolution EU 2013 organizers ● To Valtech Management ● To Michels, Maj-Daniels, and Marie-Audrey Fansi ● To all of you for your presence and attention
- 43. Questions ?
- 44. To my wife, Marie-Audrey, for all the attention she pay to our family

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