Agnieszka Stawinoga University of Naples Federico II. Simona Balbi University of Naples Federico II

Transcription

1 lds ay 2012, Florence he use of Network Analysis tools for dimensionality reduction in ext ining Agnieszka tawinoga agnieszka.stawinoga@unina.it University of Naples Federico II imona Balbi simona.balbi@unina.it University of Naples Federico II

2 Challenges in ext ining ext ining () is the non trivial process of identifying valid, novel potentially useful and ultimately understandable patterns in texts. HIGH DINIONALIY CHALLNG IN X INING - Development of new tools and strategies for detecting meaningful communities of terms in order to construct higher order data - Implementation of tools developed in other fields novel for text mining

3 NWORK ANALYI Networks are a form of "relational data", data whose properties cannot be reduced to the attributes of the individuals (nodes) involved. he object of analysis are the relations. he nodes represent individuals, and the links (ties or edges) represent a specific relationships between individuals. ode Relational information One-mode Network wo-mode Network - Adjacency matrix - Affiliation matrix Actor Attributes: In addition to relational information, social network data sets contain measurements on the characteristics of the actors ingle relation Dichotomous Weighted ultiple relation Nondirectional Directional

4 Networks ext Analysis Network ext Analysis is a method for encoding the relationships between words in a text and constructing a network of the linked words (Popping, 2000 ). the Reuters terror news network (Batagelj and rvar, 2002) Network of dictionary terms the graph of all geodesics from black to white (Batagelj et al. 2002)

5 Coding system in Bag of words: a coding system where a text (such as a document) is represented as an unordered collection of words, disregarding grammar and even word order. BAG OF WORD LXICAL ABL

6 DOCUN Lexical able R LXICAL ABL CAN B RAD A A VALUD AFFILIAION ARIX he value of cells indicate how many times each word appears in each document.

7 From Lexical able to one-mode network (1) R R D O C U N LXICAL ABL ( n p) DICHOOIZAION when interest is in presence / absence, NO frequency D O C U N LXICAL ABL A( n p) R R ARIX R x R W ( p p ) A A Interests in term co-occurrences

8 From Lexical able to one-mode network (2) R R ARIX R x R W ( p p ) GRAPHICAL RPRNAION OF ARIX G = (V;) V - the set of vertices which consists of p words the set of edges existing among vertices

9 NWORK ANALYI OOL: COPONN Components of a graph: sub-graphs connected within, but disconnected between sub-graphs. Network consists of 14 components: - 4 isolates (components of 1 node) -3 components of 2 nodes -3 components of 3 nodes -1 component of 6 nodes -2 components of 9 nodes -1 component of 28nodes

10 Betweenness centrality measures how often a node appears on the shortest path between two other random nodes in the network NWORK ANALYI OOL: BWNN CNRALIY g () i C (i)= jk, i j k B g j<k jk gjk(i) - the number of shortest paths between nodes j and k that contain the node i gjk - the number of shortest paths between nodes j and k (Freeman, 1979). High centrality index indicates a node which reaches other nodes on relatively short paths. A node with high betweenness centrality lies on a considerable fraction of shortest paths connecting pairs of other nodes. his measure is often considered as an indicator of the power and influence.

11 NWORK ANALYI OOL: BWNN CNRALIY BWNN CNRALIY IDNIFICAION OF H O RLVAN R WHICH CONNC DIFFRN COUNII WIHIN H NWORK ource:

12 NWORK ANALYI OOL: GO-NWORK An ego-network is a network formed by selecting a focal node ( ego ), including all actors ( alters ) that are connected to that node, and all the connections among those other actors. go-networks arise by "extracting" them from regular whole network data and illustrate local areas of larger networks. N-step neighborhood expands the definition of the size of ego's neighborhood by including all nodes to whom ego has a connection at a path length of N, and all the connections among all of these actors. go Alters

13 Our aim and strategy Goal: to propose a new strategy for dimensionality reduction by Network Analysis tools. Basing on the relational structure among terms, we propose to create higher order data (context, topics). trategy: 1. Preprocessing of the corpus and construction of the lexical table (nxp) 2. Construction of the co-occurrences matrix W (pxp) 3. Construction of the similarities matrix (pxp) and choice of a threshold 4. Representation of in the form of a binary network 5. xtraction of network components 6. Identification of central (betweenness ) nodes and building their ego nets 7. Creation of a new lexical table 1 (nxp 1 ) where p 1 <<p

14 Results Data: management commentary of one of the companies listed on Italian and American markets - Luxottica group, the world leader of eye-wears. he reference year is Preprocessing: Corpus: 44 sections and 21,266 tokens P 1. he corpus is normalized, and cleaned by stop words, showing 4538 types. P 2. he corpus is grammatically tagged, and the lexical-part-of-speech (nouns, adjectives) were selected. P 3. Graphical forms with attributes (NOUN, ADJCIV), the words peculiar to economic language (using the linguistic resource included in alac). P 4. Lemmatization of selected graphical forms.

15 Results R R D O C U N LXICAL ABL (44 332) R 1.DICHOOIZAION 2. A A R ARIX R x R R W( ) QUIVALNC INDX R ( ) JACCARD INDX R ( ) J R NORALIZAION OF CO-OCCURRNC R AOCIAION RNGH A ( )

16 Co-Occurrence Data Normalization: imilarity easures imilarity easures: quivalence index (a form of cosine index) Jaccard index Association strength quivalence index kj w 2 kj w w kk jj Jaccard index J kj w kj w w w kk jj kj Association strength Akj w kk kj w w jj wkk - the total number of occurrences of object k, wkj - the number of co-occurrences of k and j

17 Results R R QUIVALNC INDX ( ) 0,600 0,500 hreshold values and network density R R J JACCARD INDX ( ) 0,400 0,300 0,200 R R AOCIAION RNGH A ( ) 0,100 0,000 0,05 0,10 0,15 0,20 0,25 0,30 0,35 0,40 0,45 0,50 0,55 0,60 0,65 0,70 0,75 0,80 0,85 0,90 0,95 1,00 quivalence Index Jaccard index Association strength Chosen threshold value = 0.55

18 Results R R R J JACCARD INDX ( ) DICHOOIZAION HRHOLD VALU = 0,55 R ADJACNCY ARIX A( ) 332 nodes and 455 edges Network consists of 154 components: -137 isolates -7 components of 2 nodes -8 components of 3 nodes -1 component of 5 nodes -1 component of 7 nodes -1 component of 9 nodes -1 component of 136 nodes

19 Results o describe different context in the corpus and identify different topics we extract network components with 3 or more nodes ( 8 components of 3 nodes, 1 component of 5 nodes, 1 component of 7 nodes, 1 component of 9 nodes, 1 component of 136 nodes)

20 Results Components with 3, 5, 7 and 9 nodes

21 Results he main component of the network (136 nodes and 416 edges) Nodes with the highest betweenness centrality (99 th percentile of the whole distribution)

22 Results go-networks of the nodes with the highest betweenness centrality Basing on the proposed strategy we select 82 the most relevant terms

23 Results Lexical Correspondence Analysis is a principal axes method usually applied for the identification of the principal components of the association structure in a lexical table (Lebart et al., 1998) Correspondence Analysis First Factorial Plane (332 terms)

24 Results Correspondence Analysis First Factorial Plane: the selected terms (82)

25 Remarks on Contribution description of different context in the corpus and identification of different topics dimensionality reduction by passing from elementary data (terms) to higher order data (context, topics) selection of the most relevant terms viewed as new features prior to further analyses Future developments to put the strategy into the theoretical frame of feature selection to use the strategy for dealing with the problem of disambiguation, an important question in any Natural Language Processing

26 References 1. Balbi., tawinoga A., riunfo N. (2012). ext ining tools for extracting knowledge from Firms Annual Reports, JAD 2012 : 11 es Journées internationales d Analyse statistique des Données extuelles. (accepted for publication). 2. Batagelj V., rvar A. and Zaversnik. (2002). Network analysis of texts. Paper online: 3. V. Batagelj and A. rvar Reuters terror news network analysis with pajek. o appear in Jo 4. Batagelj V., rvar A., Zaveršnik. (2002). Network analysis of dictionaries. Jezikovne tehnologije / Language echnologies,. rjavec, J. Gros eds., Ljubljana, p Freeman L.C. (1979). Centrality in ocial Networks Conceptual Clarification. ocial Networks, 1: Hanneman R. and Riddle. (2005). Introduction to social network methods. Riverside, CA: University of California, Riverside Lebart L., alem A. and Berry L. (1998). xploring extual Data. Kluwer Academic Publishers. he Netherlands. 8. Popping R. (2000). Computer-Assisted ext Analysis. age. London. 9. van ck N. J., Waltman L. (2009) How to Normalize Co-Occurrence Data? An Analysis of ome Well-Known imilarity easures, R LI,

27 hank You! Acknowledgements his work is financially supported by the uropean Project BLU-.