An Intelligent Tool for Building E-Learning Contend- Material Using Natural Language in Digital Libraries

Transcription

1 An Intellgent Tool for Buldng E-Learnng Contend- Materal Usng Natural Language n Dgtal Lbrares A. Drgas, J. Vrettaros NCSR Demokrtos DTE/YE, Department of technologcal applcaton, NetMeda Lab Ag. Paraskev 53, Athens, Greece Abstract: - In ths paper s developed an ntellgent searchng tool usng the Self-Organzng Map (SOM) algorthm, as a prototype e-content retreval tool. The proposed searchng tool has the ablty to adust and scale nto any e-learnng platform that requres concept-based queres. The SOM algorthm has been used successfully for the document organzaton as well as for document retreval. In the proposed methodology, maps are used for the automatc replacement of the unstructured, the half structured and the multdmensonal data of text n such a way that smlar entres n the map are represented near between them. The performance and the functonalty of the document organzaton and retreval tool employng the SOM archtecture, s presented. Furthermore, experments performed to test the tme performance of a learnng algorthm used for the drect creaton of teams of terms and texts enablng effcent searchng and retreval of the documents. Key-Words: - Leave one blank lne after the Abstract and wrte your Key-Words (6 - words) Introducton Wth the advent of new technologes and the World Wde Web (WWW), enormous quanttes of nformatve materal are nowadays avalable on-lne. Computers are ncreasngly changng from computng systems to portals, whch permt to access bg volumes of nformaton. In parallel, due to the nterest n reducng costs of educaton and stmulatng people to never stop learnng, e-learnng applcatons have recently developed n educatonal, ndustral and research nsttutons. However, e- learnng platforms present some dffcultes concernng the nstructor and the student nteracton. Effectve mnng and retreval of the e-content s the maor bottleneck of e-learnng applcaton. The lack of metadata and classfcaton of the e-content, force the development of powerful search engnes. The basc approaches for nformaton retreval and data mnng n textual documents collectons are: () searchng usng keywords or key documents, (2) exploraton of the collecton referrng to some organzaton or categorzaton of the documents, and (3) flterng of nterestng documents from the ncomng document stream. Keyword search systems can be automated rather easly whereas the organzaton of document collectons has tradtonally been carred out by hand. In manual organzaton carred out for example, n lbrares, classfcaton schemes are defned and each document s postoned nto one or several classes by a lbraran. Smlarly, n the current hypertext systems the lnks between related documents are most often added by hand. One of the tradtonal methods of searchng for texts that match a query s to ndex all the words (terms) that have appeared n the document collecton. The query tself, typcally a lst of approprate keywords, s compared wth the term lst of each document to fnd documents that match the query. In the exsted applcatons, the educatonal content s multlngual and heterogeneous. Therefore, smple keyword queres are not capable for effcent mnng of the avalable nformaton. In order to bypass the aforementoned bottlenecks, we propose the use of Artfcal Neural Networks (ANN). Due to ther wde range of applcatons, ANNs have been an actve research for the past decades [2]. A large varety of learnng algorthms have been evolved and beng employed n ANNS. A further categorzaton dvdes the network archtectures nto three dstnct categores: feedforward, feedbackward and compettve [2]. The self-organzng maps or Kohonen s feature maps are feedforward, compettve ANN that employ a layer of nput neurons and a sngle computatonal layer [7]. The neurons on the computatonal layer are fully connected to the nput layer and are arranged on an N-dmensonal lattce. In ths paper, we shall use the SOM algorthm to cluster contextually smlar documents nto classes.

2 The ablty of the SOM algorthm to produce spatally organzed representatons of the nput space can be utlzed n document organzaton, where organzaton refers to the representaton and storage of the avalable data. An archtecture based on the SOM algorthm that s capable of clusterng documents accordng to ther semantc smlartes s the so-called WEBSOM archtecture [4,5,6,7]. The WEBSOM conssts of two dstnct layers where the SOM algorthm s appled. The frst layer s used to cluster the words found n the avalable tranng documents nto semantcally related collectons. The second layer, whch s actvated after the completon of the frst layer, clusters the avalable documents nto classes that hgh probablty contans relevant documents wth respect to ther semantc content. Due to that, the WEBSOM archtecture regarded as a promnent canddate for document organzaton and retreval. The structure of the paper s as follows. In the frst secton s presented the basc structure of the SOM archtecture. The system archtecture s presented n the second secton. The software component constructed to llustrate the applcablty of the proposed archtecture s shown n the thrd secton. Fnally the performance of the tranng algorthm s llustrated n the fnal secton 2 SOM Algorthm The basc Self-organzng Map (SOM) can be vsualzed as a sheet-lke-neural-network array (fgure), the cells (or nodes) of whch becomes specfcally tuned to varous nput sgnal patterns or classes of patterns or classes of patterns n an orderly fashon. The learnng process s compettve and supervsed, meanng no teacher s needed to defne the correct output (or actually the cell nto whch the nput s mapped) for an nput. In the basc verson, only one map node (wnner) at a tme s actvated correspondng to each nput. The locatons of the responses n the array tend to become ordered n the learnng process as f some meanngful nonlnear coordnate system for the dfferent nput features were beng created over the network. Let us denote by X the set of vector-valued observatons, Nw T, Χ= { x R x = ( x, x2,..., xn ),2,... } w = N Where N w corresponds to the dmensonalty of the vectors that encode the N avalable observatons. Let also W denote the set of reference vectors of the neurons, that s, Nw W = { w R, l,2,..., L}, = Where the parameter k denotes dscrete tme and L s the number of neurons of the lattce. Fnally, let w() be located on a regular lattce that les on the hyperplane whch s determned by the two egenvectors that correspond to the largest egenvalues of the covarance matrx of x X (lnear ntalzaton [4]). Due to ts compettve nature, the SOM algorthm dentfes the best-matchng, wnnng reference vector w(k) (or wnner for short), to a specfc feature vector x wth respect to a certan dstance metrc. The ndex s of the wnnng reference vector s gven by: L s = arg mn x wl, l= where. denotes the Eucldean dstance. The reference vector of the wnner as well as the reference vectors of the neurons n ts neghbourhood are modfed towards x usng: w + a( k)[ x w ] N s, w ( k + ) = w N s where a(k) s the learnng rate and Ns denotes the neghbourhood of the wnner. A neghbourhood updatng, especally n the early teratons, s performed n order to acheve a global orderng of the nput space onto the lattce, whch s crucal for the good resoluton of the map [4]. The term basc SOM wll henceforth denote the on-lne algorthm proposed by kohonen [4] wthout any modfcaton of speed-up technques. The prevous equaton can be rewrtten as follows: w ( k + ) = w + a( k) c [ x w ], where c = f the th feature vector assgned to the th neuron durng the kth teraton, otherwse c =. 2. Salton s Vector Space Model The vector space model [8] has been wdely used n the tradtonal IR feld. Most search engnes also use smlarty measures based on ths model to rank web documents. The model creates a space n whch both documents and queres are represented by vectors. For a fxed collecton of documents an m- dmensonal vector s generated for each document and each query from sets of terms wth assocated weghts, where m s the number of unque terms n the document collecton. Then a vector smlarty functon, such as the nner product, can be used to compute the smlarty between a document and a query.

3 From N ewsgroup:comp.graphcs Subect:Need specs on Apple QT I need to get the specs opr at least a very verbose nterpretaton of the specs for QuckTme techncal artcles from magaznes and reference books would be nce too. I also need the specs n a format usable on a unx or MS-dos system. I can t do much wth the QuchTme they have on Text Representaton Baseball specs graphcs references hockey car clnton unx space qucktme computer Vector Representton Fg. The VSM. The text on the left s represented as the vector on the rght. Each lne of the vector s a dfferent word of the text. Each lne s record s the frequency of the word n the text In VWSM, weghts assocated wth terms are calculated based on the followng two numbers: Term frequency f, the number of occurrence of the term y n document x. Inverse document frequency, g = log( N / d ), where N s the total number of documents contanng the term y The smlarty sm vs ( q, x ), between a query q and a document x can be defned as the nner product of the query vector Q and the document vector X : sm ( q, x ) = Q X = vs m = u w m m 2 ( u ) = = ( w ) 2, Where m s the number of unque terms n the document collecton. Document weght w and query weght u are: w = f w = f log( N / d ) and log( N / d y s a term n q u = otherwse the man problem of the vector space model s the large vocabulary n any szable collecton of free text documents, whch results n a vast dmensonalty of the document vectors. In the followng, some methods for reducng the dmensonalty wll be dscussed. These are applcable to all cases where the documents are encoded usng the vector space model,.e. as the document-by-word matrx 2.2 Latent Semantc Indexng Latent Semantc Indexng (LSI) s one alternatve to the orgnal vector space model. LSI tres to make account to the co-occurrence of terms n documents when encodng the documents. One way of nterpretng the LSI s that t represents the th document by the vector n' = n x', k k k

4 where n k denotes agan the number of tmes the word k occurs n the th document. The x ' k s the code that the LSI forms of the kth word by nvestgatng the co-occurrence of the words wthn the documents. The term-by-document matrx, a matrx where each column s the word hstogram correspondng to one document, s decomposed nto a set of factors (egenvectors) usng the sngularvalue decomposton (SVD). The factors that have the least nfluence on the matrx are then dscarded. The motvaton behnd omttng the smallest factors s that they most lkely consst of nose. The vector x ' can then be formed by usng only the remanng factors, whereby the dmensonalty s reduced. 2.3 Random proecton A low-dmensonalty representaton for documents can be obtaned as a random proecton of the hghdmensonal representaton vector nto a much lower-dmensonal space [3]. The beneft compared wth alternatve methods such as the LSI s extremely fast computaton. The accuracy of the results s stll comparable. 2.4 Word Clusterng Clusterng methods can be used to reducng the number of data by groupng smlar tems together [3]. If smlar words can be clustered together, documents can be represented as hstograms of words clusters rather than of ndvdual words. Varous early approaches for categorzng words have been descrbed n [3]. In languages wth rgd word order, such as Englsh, the dstrbuton of words n the mmedate context of a word contans consderable amounts of nformaton regardng the syntactc and semantc propertes of the word. The SOM has been used to cluster words based on the dstrbutons of words n ther mmedate contexts [3]. 2.5 SOM computaton complexty The computatonal complexty of the SOM algorthm s only lnear n the number of data samples. However, the complexty depends quadratcally on the number of map unts. For document maps ntended for browsng the document collecton, the resoluton (number of map unts per number of documents) should be good, snce browsng s easer f there are representatons of only a few, say, ten documents n a map unt on the average. Hence, for such resoluton, the number of map unts has to be proportonal to the number of documents. For very large document collectons the resultng computatonal complexty mght become problematc. 3 System descrpton We determne the characterstcs of the ntellgent search based on the nature of the educatonal materal (e-content) that nterest the users (word docs, htms pages, plan text). The system has the capablty of document retreval from databases amng at the preparaton and presentaton of an e- learnng course. The system s capable of retrevng certan educatonal texts by the users accordng to ther physcal questons. In the followng paragraph we summarze the basc elements of the system. We export the descrptors from the text of the multmeda materal and transformng of these descrptors nto compound search descrptors, n sutable vectors of characterstcs. We concretze of the non supervsed learnng algorthm SOM for the successful nformaton retreval. The concrete methodology was peered aganst the method of smple equaton of keywords as well as the one that makes use of the smple metrc resemblance n the representaton space of the texts (e.g. calculaton of cosne between vectors and retreval of those nearest n the vector that represents a queston) because t provdes better retreval performance and releases the user from the need of creaton of complcated educatonal components. The bg advantage of the non supervsed search models s that content managers are not oblged to create huge learnng materal (examples of questons wth the connected answers). Takng nto consderaton that the user cannot as an expert n neural networks tranng, the software search module s supposed to supply hm/her wth the capablty to search the database ntellgently, va the combnaton of the automatc exported characterstcs and hs/her own keywords The proposed methodology for the creaton of the ntellgent search system s based on the SOM algorthm, descrbed n the prevous secton. In the concrete applcaton, the SOM maps are used fro the automatc placements of the unstructured or half structured and multdmensonal data of text n such a way that smlar entres n the map are represented near between them. Va a learnng process, the t the performance s llustrated n the followng secton, that fnal map allows the drect creaton of teams of terms and teams of texts so that the dstances between the dfferent data can be drectly used durng the search and retreval duraton. An example of a prevous successful applcaton of the SOM networks n nformaton retreval s the

5 web applcaton WebSOM [2]. Ths applcaton s based on the export of descrptors of texts from dfferent SOMs, whch replace the department of pretreatment, and representaton of texts, n combnaton wth a self-organzed of the retreved texts. It also provdes the capablty of a two dmenson depcton of texts relatve between them, so that the user has n hs dsposal a vsual representaton of the materal n relevant categores. As recent researchers have shown that the functonalsm of such vsualzaton consderng the help that t provdes n the fnal user s arguable, n the concrete work, we do not use the depcton of map. On the contrary, we provde the capablty of nformaton retreval from the database accordng to the content of texts and thus present the results n form of a lst n a declnng lne of resemblance so that we decrease the dffcultes faced by users. 3. Content retreval usng the SOM algorthm The SOM algorthm has been used to retreve educatonal materal from a database. The methodology of ths operaton s as follows. We export the descrptors from the text of the educatonal materal. For ths task responsble are the desgners of the database system. An automate method for transformaton of the descrptors of the materal as well as the compound search descrptors, n sutable vectors of characterstcs s used. To perform ths automatc operaton we have used the VSM algorthm descrbed prevously. The VSM provdes effcency of the search results. The next step s to learn the SOM wth the vectors of text characterstcs. The result s the clusterng of the used terms and texts n teams of relevant content. Followng ths step we must search for relevant texts wth the use of questons. After the creaton of the vector of characterstcs of the queston t s suppled n the entry of traned SOM network. The result s the calculaton of the nearest Eucldean dstance of teams of texts towards the queston based on the actvated neurons of the map. Ths team wll contan texts wth terms of approxmate weght and consequently wll present the hghest affnty of content wth the queston. Search of relevant texts n neghbor teams s the followng step. The attrbute of the self-organzaton allows the user to search dfferent relevant texts found n teams of neghbor neurons of map. A queston that s placed to the system, formulated as word or as a combnaton of words, actvates the processes of retreval of texts relatve wth the queston. The system seeks n the map of teams of terms thus neurons that correspond n the terms or n the combnaton of terms that exsts n the queston. Those texts represent n concrete neurons of the map of the teams of texts actvated by the terms, are selected and thus are presented n the user. Addtonally, t s possble to present texts by a concrete method of usng metrc resemblance between texts. 3.2 Tranng After the creaton of the vector maps, we perform tranng. The feature vectors are presented teratvely an adequate number of tmes to the neural network whch perform clusterng n an effort to buld word classes contanng semantcally related words. Ths s based on emprcal and theoretcal observatons that semantcally related words have more or less the same precedng and succeedng words. The above process yelds the so-called word categores map (WCM) []. After the computaton of the document vectors the SOM method s used to cluster them. It s expected that the constructed documents classes contaned contextually smlar documents. 4 Expermental Results The performance of the SOM algorthms n the proposed case study s llustrated n ths secton. The performance s measured usng the Mean Square Error (MSE) between the reference vectors and the document vectors assgned to each neuron n the tranng phase. Furthermore the recall-precson performance s measured usng query documents from a test set durng the recall phase s used as an ndrect measure of the qualty of the document organzaton provded by the SOM algorthm. Fg. 2 depcts the MSE curves durng the formaton of WCM usng the SOM archtecture. Smlar MSE curves are plotted n Fg. 2 that correspond to the tranng phase. Durng the formaton of the WCM, the number of tranng teratons needed by the SOM so that the MSE drops to the was nearly 8. e of ts ntal value

6 Fg. 2 MSE curves durng the formaton of WCM usng the SOM archtecture Fg.3 The average recall-precson curve of the SOM To measure the effectveness of a retreval system, two wdely used ratos are employed: the precson and the recall. Precson s defned as the proporton of retreved documents that are relevant: r P = n 2 Recall s the proporton of relevant documents that are retreved: r R = n as the volume of the retreved documents ncreases the above ratos are expected to change. The sequence of recall-precson pars obtaned yelds the so-called recall-precson curve. Each querydocument n the test set produces one recallprecson curve. An average over all the curves correspondng to query documents of the same topc obtaned from the test set produces the average recall-precson curve. If the recall level does not equal to one we proceed wth the second best wnner neuron and repeat the same procedure and so on. The comparson of the effectveness between the retreved documents utlzes that above-mentoned curve. Fg. 3 depcts the average recall-precson curve of the SOM. 4. Software component of ntellgent search The core of the system of the SOM algorthm and the VSM has been developed n ANSI C to ensure portablty and compatblty n platforms of dfferent type (Wndows, UNIX, Lnux, etc). the user nterface can be desgned overall round the basc and autonomous departments of the system n order to actvate, through a GUI, the system

7 Search Results How can I nstall a modem? Fg. 5 Screen shots of the software component O.D.I.S.S.E.A.S operatons and presentaton of search results (standalone applcatons) va Vsual Basc/C++, Delph or Web based enabled as CGI scrptng, ASP, JSP, PHP, etc. A basc ssue of the fnal nterface s the confdence estmaton. The user tends to need a score of the results of ts query n order to amelorate hs queerng style. The followng fgure present an embodment example of the search system nto the e-learnng platform O.D.I.S.S.E.A.S. [] (Open Dstance Interactve SyStem for Educatonal Applcatons) wth the JSP (Java Server Pages) technology. In order to prove the functonalty of the proposed system, we analysed a collecton of text and multmeda documents. Although the SOM algorthm can be appled only to text documents, n our collecton we nclude multmeda documents. Multmeda documents are analysed through the annotaton that s created for each pcture. Vdeo and sound. In ths way we have all the collecton n raw text format. They dffer from each other n ther topc and n the document average sze. The test collecton conssts of 57 documents, 65 are multmeda and 92 are textual. The average sze of the documents s 4.55 Kb and the bggest document sze s 3 Kb. Table Summary of the results obtaned from the analyss of the test collectons. Collecton Sze (Kb) Number of Average Docume Average Number Number of Number of Terms

8 Docume nt nt sze of sze Lexcal Form Textual ,789 32,456 Audo ,89 5,678 Image Vdeo ,54 7, Dscusson The proposed system has been evaluated by means of ts usablty by the e-learnng users. The e- learnng users are the teachers who upload new materal n the database and the students who download the teachng materal. In order to measure the applcablty of the system, we have set two questons to the users: Queston : how relevant (n percentage) are the retreved documents to the query compared to ordnary search engne? Queston 2: are you satsfed wth the degree of correlaton of the system? The results of the questons are depcted n Fg. 6. The only dsadvantage of the proposed system s that every tme a new document s uploaded n the database, the learnng process must run for the new document. Ths process s tme consumng and costly. In the future work we are plannng to mprove the applcablty of the system concernng the automatc learnng process. 5 Summary In ths paper s presented the use of the SOM algorthm along wth a tranng algorthm, for document retreval. The applcablty of the algorthm s llustrated n an e-learnng case study. A software component has been constructed to perform ntellgent search n the educatonal documents. The performance of the tranng algorthm usng the MSE measure has been presented. One of the basc ssues concernng the Intellgent Systems s ts ablty to adust and be nstalled nto any platform that requres methods of ntellgent retreval. The system was awarded after ts applcaton n varous tests. Its pedagogcal advantages were underlned not only by the students but also by the nstructors handlng the educatonal materal. The nstructors ganed valuable tme durng ther course as they could retreve nformaton usng smple queres whle students found the ntellgent system necessary at ther self-paced learnng. Moreover, n a future expanson, the system s expected to provde reasons of the confdence estmaton accompaned by the retreved texts. That means that the user wll be suppled wth reasons of the certan search results and the scope of ts query. References: [] A. Drgas, J. Vrettaros and S. Kouremenos. Open Dstance Interactve System for Educatonal Applcatons O.D.S.S.E.A.S. In Proceedngss of the general conference of Samos Technology and learnng n Hgher Educaton (2). [2] J. Honkela, K. Lagus and S. Kask. Selforganzng maps of large document collectons. In Deboeck, G. and Kohonen, T., edtors, Vsual Exploratons n Fnance wth self-organzng Maps, pp Sprnger, London (998) [3] K. Lagus and S. Kask. Keyword selecton method for characterzng text document maps. In Proceedngs of ICANN99, 9th nternatonal Conference on Artfcal Neural Networks, vol., pp , IEE, London (999). [4] T. Kohonen, S. Kask, K. Lagus, J. Honkela, V. Paatero and A. Saarela. Self organzaton of a massve text document collecton. In Oa, E. and Kask, S., edtors, Kohonen Maps pages 7-82, Elsever, Amsterdam (999). [5] K. Lagus, Text retreval usng self-organzed document maps. Techncal Report A6, Helsnk Unversty of Technology, Laboratory of computer and nformaton scence (2). [6] T. Kohonen, Self-organzaton and Assocatve Memory. Thrd edton, Sprger-Verlag. Berln Hedelberg (989). [7] T. Kohonen. Self-Organzaton Maps. Sprnger- Verlag erln Hedelberg [8] A. Salton. Automatc Text processng. Addton- Wesley Publshng Company, Inc, Readng, MA (995). [9] Kohonen. Self-organzaton of very large documents collectons. State of the art. In Nklasson, L., Boden, M., and Zemke, T., edtors, Proceedngs of ICANN98, 8th nternatonal Conference on Artfcal Neural Networks, vol., pp , IEE, London (998). [] G. Deerwester, T. Dumas, W. Furnas, K. Landauer, R. Harshman. Indexng By Latent Semantc Analyss. Journal of the Amercan Socety of Informaton Sceence (99).

9 [] Honkela, S. Kask, T. Kohonen and K. Lagus. Self-organzng maps of very large document collectons: Justfcaton for the WEBSOM method." In Balderahn, I., Mathar, R., and Schader, M., Edtors, Classfcaton, Data Analyss, and Data Hghways, pp Sprnger, Berln (998) [2] R. Perfett, G. Costantn, Assocatve memores on BBS neural networks: a hardwareorented learnng algorthm n WSEAS NNA- FSFS-EC 23, pp [3] I. Trantafyllou, G. Carayanns, Archtectures and Technques for Monolngual and Multlngual Informaton Retreval Systems n a SOM Framework, n WSEAS NNA-FSFS-EC 23 pp Queston Queston 2 Very relevant Very good Better Slghtly better Not Good Bad relevant slghtly relevant Not relevant Bad Fg. 6 The results of the two questons