Text Classification with Lucene/Solr, Apache Hadoop and LibSVM

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In this session we will show how to build a text classifier using the Apache Lucene/Solr with libSVM libraries. We classify our corpus of job offers into a number of predefined categories. Each …

In this session we will show how to build a text classifier using the Apache Lucene/Solr with libSVM libraries. We classify our corpus of job offers into a number of predefined categories. Each indexed document (a job offer) then belongs to zero, one or more categories. Known machine learning techniques for text classification include naïve bayes model, logistic regression, neural network, support vector machine (SVM), etc. We use Lucene/Solr to construct the features vector. Then we use the libsvm library known as the reference implementation of the SVM model to classify the document. We construct as many one-vs-all svm classifiers as there are classes in our setting, then using the Hadoop MapReduce Framework we reconcile the result of our classifiers. The end result is a scalable multi-class classifier. Finally we outline how the classifier is used to enrich basic solr keyword search.

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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