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1 A Fuzzy Approach for ertinent Information Extraction from Web Resources International Journal of Computational Science (rint) (nline) Global Information ublisher A Fuzzy Approach for ertinent Information Extraction from Web Resources 1 Radhouane Boughamoura 1*, Mohamed Nazih mri 2, Habib Youssef 3 1 Computer Science Department, FSM, Route de Kairouan, 5000 Monastir, Tunisia bradhouane2@yahoo.fr 2 Computer Science Department, IEIM Rue Ibn El Jazzar, 5000 Monastir, Tunisia nazih.omri@ipeim.rnu.tn 3 Computer Science Department, ISITC Hammam Sousse, Tunisia habib.youssef@fsm.rnu.tn Abstract. Recent work in machine learning for information extraction has focused on two distinct sub-problems: the conventional problem of filling template slots from natural language text, and the problem of wrapper induction, learning simple extraction procedures ( wrappers ) for highly structured text such as Web pages. For suitable regular domains, existing wrapper induction algorithms can efficiently learn wrappers that are simple and highly accurate, but the regularity bias of these algorithms makes them unsuitable for most conventional information extraction tasks. This paper describes a new approach for wrapping semistructured Web pages. The wrapper is capable of learning how to extract relevant information from Web resources on the basis of user supplied examples. It is based on inductive learning techniques as well as fuzzy logic rules. Experimental results show that our approach achieves noticeably better precision and recall coefficient performance measures than SoftMealy, which is one of the most recently reported wrappers capable of wrapping semi-structured Web pages with missing attributes, multiple attributes, variant attribute permutations, exceptions, and typos. Keywords: Web wrapper, information extraction, inductive learning, wrapper induction, fuzzy logic. 1 This work is supported by the research unit RINCE. * Corresponding Author. bradhouane2@yahoo.fr. 1

2 International Journal of Computational Science 1 Introduction Information extraction (IE) [17] is the problem of converting text such as newswire articles or Web pages into structured data obects suitable for automatic processing. An example domain, first investigated in the Message Understanding Conference (MUC) [4], is a collection of newspaper articles describing terrorist incidents in Latin America. Given a news article, the goal might be to extract the name of the perpetrator and victim, and the instrument and location of the attack. Research in this and similar domains demonstrated the applicability of machine learning to IE [13, 15, 16, 24, 26]. The increasing importance of the Internet has brought attention to all kinds of automatic document processing, including IE. It has also given rise to a problem in which the kind of linguistically intensive approaches explored in MUC are difficult or unnecessary. Many documents from this realm, including , Usenet posts, and Web pages, rely on extra-linguistic structures, such as HTML tags, document formatting, and ungrammatical stereotypic language, to convey essential information. Therefore, most recent work in IE has focused on learning approaches that do not require linguistic information, but that can exploit other kinds of regularities. To this end, several distinct rule-learning algorithms [9, 19, 25] and multi-strategy approaches [7] have been shown to be effective. Recently, statistical approaches using hidden Markov models have achieved high performance levels [8, 10, 27]. At the same time, work on information integration [1, 11] has led to a need for specialized wrapper procedures for extracting structured information from database-like Web pages. Recent research [2, 12, 14, 20, 21] has shown that wrappers can be made to automatically learn from many kinds of highly regular documents, such as Web pages generated by CGI scripts. These wrapper induction techniques learn simple but highly accurate contextual patterns. For example, to retrieve a URL, the wrapper could simply extract the text between <A href= and >. However, wrapper induction is harder for pages with complicated content or less rigidly structured formatting, but recent algorithms [2, 12, 18] were capable of discovering small sets of such patterns and were highly effective at handling such irregularities in many domains. In this paper, we describe a fuzzy approach, a trainable IE system that performs information extraction in both traditional (natural text) and Web sources (machine-generated or rigidly structured text) domains. The solution we suggest is based on a new formalism for rule extraction and uses the expressive power of fuzzy logic during the process of extraction. ur approach learns extraction rules composed only of simple contextual patterns. It is flexible in the sense that it tolerates the existence of various anomalies in the pages such as, missing attributes, permutation of attributes, etc. The flexibility is achieved by following a fuzzy inductive learning approach. This paper is organized as follows. In Section 2 we give a short introduction to Information Extraction (IE). We then briefly review related literature in Section 3. Section 4 presents our proposed fuzzy IE system. Experimental results and discussions are given in Section 5. We conclude in Section 6. 2

3 A Fuzzy Approach for ertinent Information Extraction from Web Resources 2 Information Extraction Information extraction is a complex process. It consists of both a learning task and an extraction task. Most IE systems have the architecture illustrated in Figure 1. Wrapper induction system Wrappers Extraction System Database Web user Fig. 1. Architecture of an IE system ast work has focused mainly on the construction and learning of the extraction rule. The rule must be applicable to several fields while making few errors and extracting the maximum of relevant information in the document or Web page. However, documents such as Web pages are semi structured and present several anomalies. Any particular field in a Web page may present a varying structure as well as a varying context. Hence, it is difficult to construct a perfect rule that satisfies all conditions. The construction of a complex rule or the adoption of a complex learning algorithm does not resolve the difficulty. An extraction wrapper is a procedure that extracts useful information (in response to a user request) contained in a given document. The extracted information is then produced in a structured format defined by the wrapper. The wrapper shows useful information, i.e. useless information is hidden from the user. Several approaches have been proposed to help construct extraction wrappers. Some are completely manual, while others are automatic or semi automatic [23, 28]. Manual approaches to wrapper construction describe the Web structure with grammars. This approach requires expert interference to design the appropriate grammar as well as to maintain the wrapper when the structure of the information source changes. For semi automatic approaches the user instructs the system, via an interface, which information fields to extract. The system then constructs the adequate wrapper. These approaches do not require the intervention of an expert. However, any change in the structure of the information 3

4 International Journal of Computational Science source implies user intervention. ur approach is semi automatic and generates wrappers by induction. It uses a simple extraction rule that targets a single field. This has enabled us to design a reasonably simple learning algorithm. The extraction rule exploits the expressive power of fuzzy algebra to accommodate various anomalies that may be present in the field, which make our approach extremely flexible. Automatic approaches use learning techniques based on the use of heuristics, case based reasoning, etc. Inductive learning algorithms proceed either bottom-up (generalisation) or top-down (specialisation) [7]. A bottom-up approach starts by selecting one or several examples and constructing a hypothesis to cover them. Next, it tries to generalise the hypothesis to cover the rest of the examples. n the other hand, a top-down approach starts with a general hypothesis and then tries to refine it to cover all positive examples and none of the negative examples. 3 Related work Four of the most popular IE systems are WIEN (Wrapper Induction ENvironment) [21], BWI (Boosted Wrapper Induction) [5, 6, 7], WHISK [24, 25], and SoftMealy [2,3]. WIEN [20, 21, 22] is an IE system which automatically constructs wrappers based on a user supplied Web page examples. WIEN is capable of information extraction from Web pages having an array format. Several classes of wrappers have been designed enabling the extraction of the various tuples that are present in a Web page. A wrapper is composed of couples of strings delimiting the attributes of a tuple. Hence, each attribute containing the desired text fragment is delimited by a left delimiter and a right delimiter. The wrapper induction algorithm repetitively generates an extraction wrapper and tests it on the user supplied examples until it finds a wrapper that covers all the examples. The extraction process consists of locating for each attribute its left and right delimiters and extracting the information between the two delimiters. BWI [7] is a mono-attribute trainable information extraction system. The extraction algorithm learns separately two sets of boundary detectors: a set F to detect the start boundaries of the desired attribute and a set A to detect the end boundaries of the attribute. The learning attribute associates with each learned detector a confidence value, which is a function of the number of examples correctly covered and the number of miscovered examples. The confidence values are used to compute a weight for each example. The weights allow the definition of the learning rate of the supplied examples. Then, examples with low weights are considered not well learned and are given preferential treatment over examples with large weights, which are considered well learned. Extraction consists of seeking begin and end separators with an error below a given minimum error. WHISK [24, 25] is designed to handle text styles ranging from highly structured to free text, 4

5 A Fuzzy Approach for ertinent Information Extraction from Web Resources including text that is neither rigidly formatted nor composed of grammatical sentences. Such semistructured text has largely been beyond the scope of previous systems. When used in conunction with a syntactic analyzer and semantic tagging, WHISK can also handle extraction from free text such as news stories. SoftMealy [2, 3] is a multi-attribute extraction system based on a new formalism of wrapper representation. This representation is based on a Finite-State Transducer (FST) and contextual rules, which allow a wrapper to wrap semi-structured Web pages with missing attributes, multiple attribute values, variant attribute permutations, exceptions, and typos. The nodes (states) of the FST model the zones of the Web page and the transitions the possible zone separators. The FST in SoftMealy takes a sequence of the separators rather than the raw HTML string as input. Each distinct attribute permutation in the Web page can be encoded as a successful path and the state transitions are determined by matching contextual rules that describe the context delimiting two adacent attributes. 4 A Fuzzy Approach for ertinent Information Extraction from Web Resources In general, a Web page is composed of a sequence of tokens. A token may take the form of a simple character, an html tag, a string of digits, etc. A Web page consists of three main zones: a global zone, a record zone, and an attribute zone. The global zone contains the various tuples of the page. The record zone consists of the tuple to be extracted. The attribute zone is the text fragment sought and is encapsulated in the tuple (see Fig. 2). These concepts shall be illustrated with a concrete example later on (see Fig. 7). Web page Global Zone Record 1 Attribute 1 Attribute 2 Attribute n Record 2 Record n Fig. 2. Architecture of a Web page 5

6 International Journal of Computational Science A zone is marked by a Begin Separator and an End Separator of the zone. A separator is composed of two token sequences, DetectorL and DetectorR. Each token sequence is called Detector. Figure 3 illustrates the structure of a zone. Begin Separator End Separator Zone DetectorL DetectorR DetectorL DetectorR L L Fig. 3. Structure of a zone. L is the average length of a tuple 4.1 Token Classes Similar to the SoftMealy wrapper, before we start the extraction of the tuples, we segment an input HTML page into tokens. A token is denoted as t(v) where t is a token class and v is a string. For example, to the html tags < I > and < B > correspond the tokens Html(< I >) and Html(< B >), and to the numeric string 123 corresponds the token Num(123). Below we enumerate the token classes adopted and illustrate each with an example. All uppercase string : FSM CAlph( FSM ) An uppercase letter followed by a string with at least one lower case letter : rofessor C1Alph( rofessor ) A lower case letter followed by zero or more characters: and 0Alph( and ) Numeric string : 123 Num( 123 ) An opened HTML tag : < I > Html( < I > ) A closed HTML tag : < /I> /Html( < / I > ) unctuation symbol :, unc(, ) An opened HTML tag representing control characters: < HR > Spc ( < HR > ), A closed HTML tag representing control characters: < /BR > /Spc ( < /BR > ) An opened HTML tag representing element of a list: < DIV > Lst ( < DIV > ) A closed HTML tag representing element of a list: < /LI > /Lst ( < /LI > ) A generic string Any representing any other class. 6

7 A Fuzzy Approach for ertinent Information Extraction from Web Resources 4.2 verall Architecture of the Information Extraction System ur IE system is capable of learning how to extract relevant information from Web resources on the basis of user supplied examples. It is based on inductive learning techniques as well as fuzzy logic rules. It consists of three modules: a page labelling module, a learning module, and an extraction module. The first module performs page labelling. The main task of this module is the specification of the Web page structure. It indicates the beginning and end of each zone in the page. The module interacts with the user for the specification of the zone boundaries. It accepts as input a Web page and produces as output its label which is composed of a series of labels, one for each zone in the page. The learning module takes as input Web pages and their labels and constructs extraction rules for each zone. The learning of a zone consists of the determination of the extraction rule that will recognize the two separators at the beginning and end of the zone. To recognize the separator at the beginning or the end of a zone, we must identify the pertinent tokens among the token sequence to the left of the separator (DetectorL) and those to its right (DetectorR). The learning step consists of determining in the token sequence the positions of the pertinent tokens and their occurrences over a distance L, which is the tuple average length. Then, for each detector we determine a frequency matrix F where, f i, represents the number of occurrences of token at distance i. For example, Table 1 gives a frequency matrix obtained after learning DetectorL of the start separator of the global zone for three Web pages examples. We construct similarly the frequency matrix for all the detectors of all zones. Table 1. FrequencyL, the frequency matrix of DetectorL of the start separator of the global zone Tokens Distance C1Alph CAlph Num 0Alph unc /Spc Spc /Lst Lst /Html Html Any For example, FrequencyL(5,1) = 3 means that the token C1Alph has been observed in three examples at position (also distance) 5 in DetectorL. Next, we estimate the cost of each detector. The cost metric is used to estimate the error made by a detector that is learned as opposed to a detector that is extracted. This metric should be a function of the tokens positions and their occurrences. 7

8 International Journal of Computational Science [ ] Let and be, respectively, the set of the positions and the occurrences of the tokens found in the learning set. To estimate the cost of a token, we define the following two functions: f : Ν 0,1 : is a function characterizing the degree of truth of the position (distance) of token with respect to its separator. It is defined as follows (see Fig. 4): f () i = 1 if fi, > 0. (1) 0 f ( i) < 1 if fi, = 0 For example to determine f C1Alph we seek the column corresponding to C1Alph in the frequency matrix. Figure 4 shows f C1Alph. We observe in this figure that the degree of truth of the token position for C1Alph is equal to 1 at positions 0, 1, and 5 because the token is observed at these positions during the learning stage (see first column in Table 1). The degree of truth of the token position is assumed between 0 and 1 in the other positions since C1Alph is not observed in these positions during the learning stage. degree of truth of position 1,5 1 0, osition Fig. 4. Function specifying the degree of truth of the position of token C1Alph [ ] f : Ν 0,1 : is a function characterizing the degree of truth of the occurrence of the token. It is defined as follows (see Fig. 5): f i, f () i = if f > 0 i, number of learned instances. (2) f i ', f () i = if f = 0 i, number of learned instances With i ' is the nearest position of i such as f > 0 i ',. C1Alph For example, the degree of truth function of the occurrence of token C1Alph f is determined from the column of C1Alph in the frequency matrix. Figure 5 shows f C1Alph. We observe in this figure that the degree of truth of the token occurrence count for C1Alph is equal to 1 at positions 3, 4 and 5 because these positions are the nearest to position 5 where the token has been observed in all the three examples of the learning set. Therefore, its occurrence degree of truth is equal to 3 / 3 = 1. The other positions are near position 1 and 0 where the token is observed twice 8

9 A Fuzzy Approach for ertinent Information Extraction from Web Resources in the three examples. Therefore, its occurrence degree of truth is equal to 2/3= 0.66 (see first column in Table 1). Degree of truth of occurence osition Fig. 5. Function specifying the degree of truth of occurrence of token C1Alph The cost of a token and the cost of a detector are defined as follows: Cost (distance) = f (distance). f (distance). (3) L Costdetector = Cost ( i) (4) i= 1 where i is the token at position i. The cost of a token estimates the probability that the token is at the expected position with a good occurrence count. The position of a token is declared a good position when the token is observed at that position during the learning process. The token position varies from zero to the average length of the tuple. The token occurrence count varies between zero and the number of learned examples. A token occurrence is qualified as good if the token is observed in all learned examples. Then, while parsing a Web page of the learning set, we associate with each zone detector the minimum cost of the learned detectors C min, the maximum costc max, and the average cost C moy, C C + C 2 i min max moy =. (5) These cost metrics will serve during the following extraction stage to construct membership functions. The third module consists of extracting the different tuples contained in a Web page based on the extraction rule obtained from the learning module. To extract the different tuples in a Web page, we proceed in three steps. First, we extract the global zone of the page. Next, the various records contained in the global zone are extracted. Fi- 9

10 International Journal of Computational Science nally, for each record, we extract the different attributes it contains. This way, all tuples of the page are extracted. The extraction of a zone is done via the determination of the two separators of the beginning and end of the zone. The determination of a separator is achieved by means of its two detectors. We calculate the error made by a detector by determining the deviation from the average learned cost C moy. Then, we determine the separator error from errors made by its DetectorL and DetectorR. Indeed, the separator we seek is the one whose two detectors commit minimal errors in comparison with the costs of the detectors learned during the learning stage. ErrorDetector = CostDetector Cmoy (6) ErrorSeparator = ErrorDetectorL + ErrorDetectorR (7) To estimate the error that a separator commits from the errors committed by its detectors DetectorL and DetectorR, we use a fuzzy engine. The fuzzification process is done using three membership functions corresponding to linguistic variables Errorleft, ErrorRight and ErrorTot. They describe respectively error committed by DetectorL, DetectorR and Seprator. To each linguistic variable we associate five linguistic values, Negative, NegativeSmall, Zero, ositivesmall, and ositive. Each such linguistic value defines a fuzzy subset whose membership function is illustrated in Figure 6. The value zero of the error means that the cost of DetectorL is equal to the average learned cost C Moy. The minimum error value that can be reached by a detector is -C Moy and the maximum error value is L-C Moy. Therefore, we have chosen as limit for the error C Moy. ErrorLeft Membership function Negative NegativeSmall Zero ositivesmall ositive error Fig. 6. Membership function of DetectorL error Similar membership functions are used with other linguistic variables. The inference process is achieved by the following rule base. (R1) if ErrorLeft is ositivesmall or ( ErrorRight is ositivesmall) then ErrorTot is ositivesmall. (R2) if ErrorLeft is ositive or ( ErrorRight is ositive) then ErrorTot is ositive. 10

11 A Fuzzy Approach for ertinent Information Extraction from Web Resources (R3) if ErrorRight is Zero and ( ErrortLeft is Zero) then ErrorTot is Zero. (R4) if ErrorLeft is NegativeSmall or ( ErrorRight is NegativeSmall) then ErrorTot is NegativeSmall. (R5) if ErrorLeft is Negative or (ErrorRight is Negative) then ErrorTot is Negative. Defuzzification is done using the Centroid method as follows, e = U y. μ U ET ET ( y). dy μ ( y). dy Where e is the estimated total error of the separator and μ ET (y) is the output obtained from the rule base for a particular value y of the error. In this work we used the max operator to aggregate the output of the five rules. We obtain after defuzzification a real value e representing the error committed by a separator compared with the learned one. nce the separator error is determined, we compare this error with a threshold β specified by the user. If the separator error is lower than β then the separator is a good one and it indicates the beginning (respectively the end) of a zone. (8) 4.3 An Illustrative Example Let s consider Web pages listing country names and codes (Figure 5). Each tuple consists of a country name and its corresponding telephone code. To label a Web page, we use an interface that allows a user to specify for each zone the starting and ending characters of the zone. 11

12 International Journal of Computational Science Global Zone First Record Zone Attribute Name Zone Attribute Code Zone Global Zone First Record Zone Attribute Name Zone <HTML><TITLE>Some Country Codes</TITLE> <BDY><B>Some Country Attribute Codes</B><> Code Zone <B>Congo</B> <I>242</I><BR> <B>Egypt</B> <I>20</I><BR> <B>Belize</B> <I>501</I><BR> <B>Spain</B> <I>34</I><BR> <HR><B>End</B></BDY> </HTML> (a) Web page with 4 tuples (b) Corresponding source code Global Zone First Record Attribute «Name» Attribute «Code» (c) Label of the first tuple Congo 242 Fig. 7. Labelling of a Web page The learning module accepts as input a sequence of couples (Web age, Label). The user may use as a page label one or many of the tuple labels. In this example, we used three examples to train the system. Then, the information extraction system builds for each zone detector a frequency matrix. This matrix indicates the occurrence count of a given token at a given position. For example, FrequencyL(5,1) = 3 in Table 1 means that the token C1Alph has been observed in three examples at position 5 in DetectorL. Then we compute for each detector the corresponding learned costs, C min, C max and C moy. These costs are used to construct membership functions. The cost of a detector is equal to the sum of the costs of its tokens. For example, suppose that DetectorL has the following structure: C1Alph Num /Spc /HTML HTML Distance :

13 A Fuzzy Approach for ertinent Information Extraction from Web Resources To compute the cost of the token C1Alph at position 4, we must determine the degree of truth of C1Alph at this position and the degree of truth of its occurrence at this position. The degree of truth function corresponding to the position of the token C1Alph is given in Figure 4 and the degree of truth function corresponding to its occurrence is given in Figure 5. Then the costs corresponding to the different tokens of the detector are, C f f C f f C f f C1Alph C1Alph C1Alph (4) = (4)* (4) = 0.75*1 = 0.75 Num Num Num (3) = (3) * (3) = 1*0.33 = 0.33 /Spc /Spc /Spc (2) = (2)* (2) = 0.5* 0.33 = 0.16 /HTML /HTML /HTML = = = HTML HTML HTML (0) = (3)* (3) =1*0.33 = 0.33 C (1) f (3) * f (3) 0.33* C f f and the cost of the detector is, Cdetector = CC1Alph (4) + CNum (3) + C/Spc (2) + C/HTML (1) + CHTML (0) = = Experimental Results We compared the performance of our approach with that of SoftMealy 2. We considered five collections of Web pages that present different types of anomalies and attempted to extract from each page the different tuples it contains. The results are summarised in Table 2 below. The comparison is performed with respect to the Recall Coefficient and recision performance metrics, which are defined as follows: Recall number of extracted tuples =. (8) total number of tuples in the Web page number of extracted pertinent tuples recision = (9) total number of tuples in the Web page 2 The comparison is limited to SoftMealy since only the code of SoftMealy was available to us. 13

14 International Journal of Computational Science Table 2. Number of tuples pertinent or otherwise extracted by SoftMealy and our approach for each set of the test pages SoftMealy our approach Set of Web pages Results Set_1 (5 pages) Set_2 (11 pages) Set_3 (17 pages) Set_4 (23 pages) Set_5 (33 pages) Total number of tuples Number of extracted tuples Number of pertinent tuples extracted Number of extracted tuples Number of pertinent tuples extracted The histogram of Figure 8 summarizes results shown in Table 2. For each page set, the two bars to the left give the number of retrieved tuples and the number of pertinent retrieved tuples for SoftMealy and those to the right are the numbers produced by our approach. We notice that the number of tuples retrieved by our approach is superior to those retrieved by SoftMealy. Furthermore, we notice that the efficiency of our approach increases with the increasing cardinality of the learning set of Web pages. Histogram of retrieved tuples Tuples retrieved E1 E2 E3 E4 E5 Sets of web pages SoftMealy SoftMealy ur approach ur approach Fig. 8. Histogram of obtained results Figures 9 and 10 plot the Recall Coefficient and recision performance measures, obtained by the SoftMealy wrapper and by our approach, for each of the Sets. 14

15 A Fuzzy Approach for ertinent Information Extraction from Web Resources 1,2 1 Recall 0,8 0,6 0,4 SoftMealy ur approach 0,2 0 S1 S2 S3 S4 S5 Sets of pages Fig. 9. Comparison between SoftMealy and our approach with respect to the Recall Coefficient metric The reader can clearly see that, with respect to the recall coefficient, the two curves corresponding to the two approaches follow the same pace. This phenomenon can be explained by the fact that training processes use the same Web page structure. However, Figures 9 and 10 clearly illustrate that the recall coefficient of our approach is always superior to the recall coefficient obtained by SoftMealy and the gap between the two approaches increases when the number of test pages increases. 0,7 0,6 0,5 precision 0,4 0,3 0,2 SoftMealy ur approach 0,1 0 S1 S2 S3 S4 S5 Sets of web pages Fig. 10. Comparison between SoftMealy and our approach with respect to recision metric In addition, we notice that our approach achieves noticeably better precision than SoftMealy on the used test set. SoftMealy achieves slightly better precision only when the learning set is too small. 15

16 International Journal of Computational Science 6 Conclusion In this paper, we presented a new approach to information extraction from multi-attribute semistructured Web pages. ur approach is flexible in the sense that it tolerates the existence of variant anomalies in the pages such as, missing attributes, permutation of attributes, etc. The flexibility is achieved by following a fuzzy inductive learning approach. The user can intervene at any moment to improve the rules learned by adding new training examples. Both the information extraction and learning algorithms are independent of the lexical analyser. Experimental results obtained on several test Web pages show a superior performance of our approach compared to that of Soft- Mealy with respect to Recall Coefficient and recision metrics. The basis on which we worked is characterized by different exceptions. SoftMealy, for example, is very dependent on the training set. Indeed, transitions of the automaton are very dependent on what is observed during the training. The function of generalization used in SoftMealy does not tolerate mistakes in tokens positions. It doesn't record the token occurrence counts during the training. So, a transition is rigid in the sense that it does not allow any variation in the token positions. Another fundamental difference between SoftMealy and our approach is in the detection of the beginning and end of a zone. In our case, this decision is based on an estimation of a cost error metric while SotMealy relies on the detectors it has seen in training set. References 1. Levy, A., Knoblock, C., Minton, S., and Cohen W.: Trends and controversies: Information integration. IEEE Intelligent Systems 13(5) (1998) 2. Hsu, C., and Dung M.: Generating finite-state transducers for semi structured data extraction from the web. J. Information Systems 23(8) (1998) 3. Hsu, C-N.: Initial Results on Wrapping Semi structured Web ages with Finite-State Transducers and Contextual Rules. Available at resented at AAAI 98 Workshop on AI and Information Integration (1998) 4. Defense Advanced Research roects Agency: roc. Sixth Message Understanding Conference (MUC-6). Morgan Kaufmann ublisher, Inc., (1995) 5. Freitag, D.: Information Extraction from HTML: Application of a general machine learning approach, in roc. AAAI- 98, Madison, WI, (1998) 6. Freitag, D.: Machine learning for information extraction in informal domains. Machine Learning 39(2/3) (2000) 7. Freitag, D., Kushmerick, N.: Boosted Wrapper Induction. roceedings of the Seventeenth National Conference on Artificail Intelligence (2000) Freitag, D. and McCallum, A.: Information extraction using HMMs and shrinkage. In roc. AAAI- 99Workshop on Machine Learning for Information Extraction, AAAI Technical Report WS (1999) 16

17 A Fuzzy Approach for ertinent Information Extraction from Web Resources 9. Freitag, D.: Multistrategy learning for information extraction. roceedings of the Fifteenth International Machine Learning Conference (1998) Bikel, D., Miller, S., Schwartz, R., and Weischedel, R.: Nymble: a high-performance learning namefinder. In roc. ANL-97 (1997) Wiederhold, G.: Intelligent Information Integration. Kluwer (1996) 12. Muslea, I., Minton, S., and Knoblock, C.: Hierachical wrapper induction for semi structured information sources. J. Autonomous Agents and Multi-Agent Systems (2000) 13. Muslea, I.: Extraction patterns for Information Extraction Tasks: A Survey, presented at the AAAI-99 Workshop on Machine Learning for Information Extraction (1999) 14. Muslea, I., Minton, S., and Knoblock, C.: A Hierarchical Approach to Wrapper Induction, presented at 3 rd conference on Autonomous Agents (1999) 15. Muslea, I., Minton, S., Knoblock, C.A.: STALKER: Learning extraction rules for semi structured Webbased information sources. In proceedings of AAAI-98 Workshop on AI and Information Integration, Technical Report WS-98-01, AAAI ress, Menlo ark, CA (1998) 16. Kim, J.-T. and Moldovan, D.: Acquisition of linguistic patterns for knowledge-based information extraction. IEEE Trans. on Knowledge and Data Engineering 7(5) (1995) Eikvil, L.: Information Extraction from World Wide Web. A survey. (1999) 18. Liu, L., u, C., Ilan, W.: XWRA: An XML-Enabled Wrapper Construction System for Web Information Sources. In roceedings of the International Conference on Data Engineering (2000) 19. Califf, M.-E.: Relational Learning Techniques for Natural Language Information Extraction. hd thesis, University of Texas at Austin (1998) 20. Kushmerick, N.: Wrapper Induction for Information Extraction, h.d. Thesis, University of Washington, Seattle, WA (1997) 21. Kushmerick, N., Weld, D., Doorenbos,R.: Wrapper Induction for Information Extraction, in roc. IJCAI-97, Nagoya, Japan (1997) Kushmerick, N.: Wrapper induction: Efficiency and expressiveness. Artificial Intelligence Journal 118(1-2) (2000) Ashish, N., Knob lock, C.: Semi-automatic wrapper generation for Internet information sources, in roc. Cooperative Information Systems (1997) 24. Soderland, S.: Learning Text Analysis Rules for Domain specific Natural Language rocessing. hd thesis, University of Massachusetts, CS Tech. Report (1996) 25. Soderland, S.: Learning information extraction rules for semi-structured and free text. Machine Learning 34(1/3) (1999) Huffman, S.: Learning information extraction patterns from examples. In Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language rocessing, volume 1040 of Lecture Notes in Artificial Intelligence, Springer-Verlag, Berlin (1996) Leek, T.: Information extraction using hidden Markov models. Master s thesis, UC San Diego (1997) 28. Ashish, N., Knoblock,C.: Semi-automatic wrapper generation for Internet information sources, in roc. Cooperative Information Systems (1997) 17

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