Retrieving Informative Content from Web Pages with Conditional Learning of Support Vector Machines and Semantic Analysis

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1 Retrieving Informative Content from Web Pages with Conditional Learning of Support Vector Machines and Semantic Analysis Piotr Ladyżyński (1) and Przemys law Grzegorzewski (1,2) (1) Faculty of Mathematics and Computer Science Warsaw University of Technology Plac Politechniki 1, Warsaw, Poland (2) Systems Research Institute, Polish Academy of Sciences Newelska 6, Warsaw, Poland Abstract. We propose a new system which is able to extract informative content from the news pages and divide it into prescribed sections. The system is based on the machine learning classifier incorporating different kind of information (styles, linguistic information, structural information, content semantic analysis) and conditional learning. According to empirical results the suggested system seems to be a promising tool for extracting information from web. Keywords: Conditional learning, machine learning, semantic analysis, sparse matrices, support vector machines, web information extraction. 1 Introduction News web pages are organized in distinct segments such as menus, comments, advertisements areas, navigation bars and the main informative segments article texts, summarizations, titles, authors names. Distinguishing informative content from redundant blocks plays enormous role in systems which require fast and online monitoring of thousands of published information (see Fig. 1). For example, imagine a system for predicting stock price fluctuations based on the analysis of content published in financial news web pages or social networking sites. Such a system should be supported with filtered texts. Another example is a system which gathers automatically morning business information from all important news pages, categorize it and present as one application. Retrieving such amount of information manually will by probably impossible and too expensive. 2 Related work The broad literature devoted to the problem is evidence of its importance. Most of the proposed systems are based on heuristics or templates prepared manually. Gujjar et al. [?] and Lin, Ho at al. [?] constructed a decision rule by examining

2 2 P. Ladyżyński and P. Grzegorzewski Fig. 1: An exemplar news web site from wiadomosci.wp.pl.informative content (title, summary, article title) is selected within thick black lines areas. node text content size and entropy. Castro Reis et al. [?] created extraction templates by the analysis of HTML tree structure and label text passages that match the extraction templates ([?] shows a similar approach). Another approach presenting matching unseen sites to the templates is proposed in [?] - [?]. Such solutions may work even well for one domain but have no ability to adapt to different sites ( with different structure) without manual intervention to modify rules or templates. Moreover, such rigid rules will work properly for sites with well organized structure (for example large information portals where HTML tree structure is based on a machine generated code) but will behave poorly on sites which often change their layout (blogs, small hand-developed portals ). Little modification of content structure in analyzed site often results in necessity of templates modification. Hence Ziegler et al. [?] extracted tree structure from HTML for linguistic and structural features and than used the Particle Swarm Intelligence machine learning technique to establish a classification rule. In the present paper we propose a solution utilizing the support vector machine (SVM). By sequence learning algorithm and sparse matrix processing our system is able to handle a training set of examples each consisting of attributes (learning SVM on such matrix in classic way requires 400TB of RAM memory). Moreover, to extend classifier s ability to capture HTML tree structure we use conditional learning transferring information on parents classification to children node in the HTML tree. The construction of a training set is based on capturing thousands of features which makes the solution robust to page layout modifications.

3 3 System architecture 3.1 Collecting data Retrieving Informative Content from Web Pages 3 Our goal is to construct a system which is able to retrieve specified blocks for a given domain from WWW sites. We would like to extract the following article segments from the news web page: 1. noise (non-informative segments), 2. main content, 3. title, 4. summary, 5. author s name, 6. readers comments. We have written a GUI application (SegmentSelector) in Java programming language for preparing a training set through manual classification of the nodes. More precisely, this application displays web page and unable a user to select text segments and assigning them to specified class (from 1 to 6). It is worth noting that our GUI application may help to make this process more even efficient. Namely, just after classifying manually only a few sites one may force the system to follow the process for successive sites keeping eye on the classification and reducing users activity to correct mistakes and misclassifications. 3.2 Attributes selection A typical web page in the form we can see in a browser is build from HTML code supported by styles files CSS. Each area in WWW page is represented in HTML source code tree by a certain node. Each node has a wide range (over 300) of attributes and layout features which we can obtain from the browser rendering engine. Examples are the font size, background color, position, height, width, margin, padding, border etc. Moreover, we also compute or aggregate some extra features along with feeding classifier with preprocessed text content of the node. Even the most sophisticated artificial intelligence method would work poorly if it would be fed with a feature set which do not separate learning examples. Therefore, when creating a training set, it is advisable to draw attention to the following aspects: Styles features. We can get styles attributes directly from a browser rendering engine. Some of them are quantitative - they are generally real numbers (position, font size, background color) while others are qualitative (bold, italic, text-decoration:none). For each node, Quantitative features for each node are collected in an array, while qualitative are stored as a string (which would be later transform into a sparse matrix required for the SVM classifier). Structural Features. Structural features contain information on the structure of HTML tree: tag-path, id-path, class-path For each node we define a string attribute by a sequence tag s names corresponding to given path (from the root to that node of the tree). Next we do the same for class and id parameters. The illustration is given in Fig.??, where html.div.p, 0.main article.kls 01 and 0.0.temat correspond to tag-path), id-path and id-path, respectively. These three attributes of the node will be used in further processing. It is worth noting that these structural attributes remain unchanged even if the graphical layout of the page would be modified.

4 4 P. Ladyżyński and P. Grzegorzewski Fig. 2: Tree structure of HTML source code of web page. Each node represents a specified segment in page layout. anchor-ratio high value of this ratio indicates that the text node probably does not contain the main content. format-tag-ratio formatting tags are HTML instructions (or set CSS styles) which change the text display format. We assume that main content nodes take higher value of this ratio. Linguistic features. We compute some word statistics in each examined node: word-count number of words, words-ratio fraction of words in the node beginning with uppercase (often in block containing author s name this feature is equal to 1), letters-count number of letters in given node, letters-ratio fraction of uppercase letters, average-sentence-length the average of letters in the sentence. Semantic analysis. We will also try to teach our SVM classifier the meaning of some sort of text in node. SVM should recognize some groups of words typical for a given type of node. As an example we can consider an advertisement block which usually contains phrase Google Ads. It seems that the simplest way for including information stored in the text content corresponding to given node is to treat each word as a separate string feature and include it to the list of all string features of that node. However, such solution may result in adding too many unique words to the feature space. Fortunately we can reduce the dimension of the data by choosing only words which are in some sense more informative than others (e.g. word molecular is much more informative than word are ). The importance of a word increases if it occurs many times. Let tf i,j = n i,j k n, (1) k,j where n i,j shows how many times word i occurs in node j and k n k,j is the number of all words in node j. On the other hand the importance of word decreases when it is common in the language: idf i = log D {j : t i d j }, (2)

5 Retrieving Informative Content from Web Pages 5 where D is the number of analyzed nodes containing text and {j : t i d j } is the number of documents containing term i. Now we can define a measure of importance of word i in node j: (tf idf) i,j = tf i,j idf i. (3) This way we can reduce the dimension of data by choosing only words with high values of (tf idf) i,j matrix. As an example, let us consider the portal wiadomosci.wp.pl. Using the distribution of importance we reduce the number of word attributes from to Training set preparation Let us consider a training set obtained from the news portals wiadomosci.wp.pl and businessweek.com by the manual indication of the text areas we would like to extract (class selection). Our web robot application collected articles from this sites for two months and displayed it in (SegmentSelector) for the manual classification. Each day after classification of new articles SVM classifier was retrained with new observations so each day the sites where classified better and only few small corrections were required. After two month we had nodes from wiadomosci.wp.pl and nodes from businessweek.com. As we have mentioned above we collect two types of features for each node: quantitative (real-valued features) and qualitative (string features like tag-path, words from text content, etc.). For wiadomoci.wp.pl we obtained 46 real-valued attributes for each node. However, there were differences in the number of qualitative features for each node, e.g. we got F styl = 283 different string features for styles, F struc = 8506 string features for structural features and F sem = string features for reduced dimensions from semantic analysis of the content. Next we gave a unique number (from 1 to 18789) for each string feature to generate the input training file in a sparse matrix representation. The results obtained for businessweek.com were similar. 3.4 Conditional Learning An information that our observations are derived from the tree structure is crucial for the classifier. Going down the tree we can classify parent node first and consider the parents class as a feature for the child nodes. Constructing the training set in this way we emulate a learning scheme which takes into consideration conditional a-posteriori distribution without direct estimation as in the case of the conditional random field (see. [?]). 3.5 SVM sequence learning with sparse matrices As we have mentioned above the SVM classifier is the heart of our system. Let y = (y 1,..., y N ) denote a class labels y i { 1, 1} and let (x i ) N i=1 denote vectors

6 6 P. Ladyżyński and P. Grzegorzewski of features. Training the SVM classifier is equivalent to finding the solution of the quadratic optimization problem: under boundary conditions: min w w 2 2 (4) y i (wx i + b) 1, (5) where w is a vector defining a separating hyperplane. Due to the size of our data all usual solving techniques are useless. For training our SVM classifier we use the kernalized subgradient sequential algorithm (see [?]): INPUT: S, λ, T INITIALIZE: Set α 1 = 0 for t = 1, 2,..., T do Choose i t {0,..., S } uniformly at random. for all j i t do α t+1 [j] = α t [j] end for if y it j α t[j]y j K(x it, x j ) 0 then α t+1 [i t ] = α t [i t ] + 1 else α t+1 [i t ] = α t [i t ] end if end for OUTPUT: α T +1 where K(.,.) is a kernel function (the gaussian kernel was successfully applied in our study). This algorithm was applied for training a classifier with two classes only. To enable a multi-class performance we have used the one-for-all strategy. 4 Results and conclusions We trained the SVM classifier with sparse features matrices of dimensions: for businessweek.com and for wiadomosci.wp.pl with the sparsity level equal to 0, 1%. With the grid search we found that σ = 18 for standard deviation in SVM Gauss kernel works well. Due to immense size of data we train SVM by only two passes through entire learning set which result in training time equal to about fourteen days on machine with 2, 4GHz processor. Results for distinguishing informative content from non-informative task for wiadomosci.wp.pl are shown in Table 1 while the performance in labelling the informative nodes is given in Table 2. Both semantic analysis and conditional learning technique resulted in significant improvement of classification results.

7 Retrieving Informative Content from Web Pages 7 noise content Prec. noise content (a) noise content Prec. noise content noise content Prec. noise content (c) Table 1: Crossvalidation tests for wiadomosci.wp.pl training set: (a) SVM without semantic analysis features and conditional learning, (b) SVM with Semantic Analysis features but without conditional learning, (c) SVM with full system architecture (b) Prec. (a) (b) (c) Table 2: Crossvalidation tests for businessweek.com training set: (a) SVM without semantic analysis features and conditional learning, (b) SVM with semantic analysis features but without conditional learning, (c) SVM with full system architecture, where: 1. noise content, 2. article main text 4, title, 3. summary, 5. author s name, 6. readers comments

8 8 P. Ladyżyński and P. Grzegorzewski We can see that comments block as its semantic and style similarity to main content of article is difficult to extract. Since a page structure varies for each domain it is extremely difficult to compare various systems trained on different data. However, the precision rate equal about 99% is quite promising in comparison of performance of systems proposed in previous works (e.g. 90% in [?] or 80% in [?]). That outstanding performance of the proposed system is a result the skilful application the SMV classifier implemented in a way that enables handling with immense training sets along with conditional learning and taking into consideration all possible types of features. Although the performance of our system quite satisfactory, some further improvements would be desirable. Firstly, we should try to upgrade classifier using boosting technique. Secondly, a more sophisticated semantic analysis technique (e.g. semantic patterns recognition) seems to be promising. Finally, it would be interesting to examine the proposed system for retrieving information from more difficult, irregular and mutable sites such as blogs. References 1. Arasu, A., Garcia-Molina, H.,University S.: Extracting structured data from web pages. In: ACM SIGMOD 03, pp ACM (2003) 2. Crescenzi, V., Mecca, G., Merialdo, P.: Roadrunner: Towards automatic data extraction from large web sites. In: 27th International Conference on Very Large Databases, pp VLDB (2001) 3. Lerman, K., Getoor, L., Minton, S., Knoblock, C.: Using the structure of web sites for automatic segmentation of tables. In: ACM SIGMOD 04, pp ACM (2004) 4. Castro Reis, D., Golgher, P.B., Silva, A.S., Laenderl, A.H.F.: Automatic web news extraction using tree edit distance. In: Proceedings of the 13th International World Wide Web Conference, pp New York, ACM Press (2004) 5. Geng, H., Gao, Q., Pan, J.: Extracting Content for News Web Pages based on DOM. In: IJCSNS International Journal of Computer Science and Network Security. VOL.7, No.2 (2007) 6. Vineel, G.: Web Page DOM Node Characterization and its Application to Page Segmentation. In: Internet Multimedia Services Architecture and Applications (IM- SAA). IEEE Press (2009) 7. Lin, S.H., Ho, J.M.: Discovering informative content blocks from web documents. In: KDD 02 Proceedings of the eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp ACM, New York (2002) 8. Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting nd labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, pp San Francisco (2000) 9. Shalev-Shwartz, S., Singer, Y., Srebro, Pegasos, N.: Primal Estimated sub-gradient Solver for SVM. In: ICML 07 Proceedings of the 24th International Conference on Machine Learning, pp New York (2007) 10. Ziegler, C.N., Skubacz, M.: Content extraction from news pages using particle swarm optimization on linguistic and structural features. In: Web Intelligence, pp IEEE Computer Society (2007)

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