Multi-lingual Multi-media Information Retrieval System

Transcription

1 Mlti-lingal Mlti-media Information Retrieval System Shoji Mizobchi, Sankon Lee, Fmihiko Kawano, Tsyoshi Kobayashi, Takahiro Komats Gradate School of Engineering, University of Tokshima 2-1 Minamijosanjima, Tokshima, , Japan {shoji, Jn-ichi Aoe Department of Information Science and Intelligent Systems, University of Tokshima 2-1 Minamijosanjima, Tokshima, , Japan INTRODUCTION In the first NACSIS Test Collection for Information Retrieval (NTCIR) Workshop [3][4][5], we participated in the ad hoc Information Retrieval (IR) task. In this participation, we focs on evalating the Japanese IR engine of or prototype Mlti-Lingal Mlti-Media Information Retrieval (MLMMIR) system sing NTCIR, which is the first large-scale test collection in Japanese. A MLMMIR system is an integrating system of Mlti-Lingal Information Retrieval (MLIR) techniqes [2][9] and Mlti-Media Information Retrieval (MMIR) techniqes [1]. It is capable of mtally retrieving informative data that is represented by many langages and varios kinds of media by crossing bondaries among them. Aiming to realize sch a system, we bild the prototype MLMMIR system, called BOSS [7]. BOSS can retrieve a mixtre of varios kinds of sefl data by first translating the contents that are inclded in lingal and media data into Japanese keywords, and then providing them to the Japanese IR engine that we developed. The performance of the system depends heavily on the Japanese IR engine, and the overall system is now nder constrction. Therefore, we limit the range of evalation to not the overall system bt the Japanese IR engine of the system. We sbmitted one official ad hoc rn, JALAB, to NACSIS. However, it was excessively bad becase the collection of docments that was sed in atomatic term indexing is not the ones of NTCIR, bt the corps that we collected by orselves. Therefore, we report new five ad-hoc rns, JALAB1, JALAB2, JALAB3, JALAB4, and JALAB5, in addition to JALAB. They are the rns that were performed sing the index terms that were extracted from all docments of NTCIR. The remainder of this paper is organized as follows. The next section describes the overview of the system. Section 3 describes keyword extraction methods for mlti-lingal data and mltimedia data. Section 4 describes the Japanese IR engine of the system. Section 5 describes the evalation reslts for the ad hoc task. Finally, Section 6 describes conclsion. 2. OVERVIEW OF THE SYSTEM BOSS can retrieve the information that is relevant to a qery from a database and display the reslt in relevant order. The information that is inptted as a qery is lingal data sch as Japanese and English texts, or media data sch as images and sonds. In addition, the information that is stored in the database and is retrned in the reslt is a mixtre of these data. To break down the walls that stand among lingal and media data, BOSS retrieves relevant information throgh Japanese keywords. In the searching process, Japanese keywords are first extracted from any kinds of information that the system spports, and then they are provided for the conventional IR system. By doing in sch a way, it is possible to search for varios kinds of information mtally. Figre 1 shows the overview of BOSS. It consists of three main modles: the ser interface modle, the keyword extraction modle, and the IR engine. Each of them is easily explained below. User interface modle: The ser interface modle takes varios kinds of command inptted by a ser and displays the reslt of execting it. If data objects are inclded in the reslt, they are otptted sing a sitable method for each of their formats. Keyword extraction modle: The keyword extraction translates a data object that is sent from the ser interface modle into Japanese keywords sing the extraction method and the conversion dictionary that specialize in its format. Conversion dictionaries are sed to translate the featres that are extracted from a data object into Japanese keywords except that it is a Japanese text. The detail is described in Section 3. IR engine: The IR engine retrieves data objects that are relevant to Japanese keywords sing the inverted index. The relevance between the keywords and each of data objects that are stored in the database are compted sing a vectorspace method [6]. The inverted index is sed to compte these relevances fast. The detail is described in Section KEYWORD EXTRACTION The inpt data is translated into some Japanese keywords in the keyword extraction modle. This process is performed sing two different methods for lingal data and media data. 3.1 Method for Mlti-lingal Data In the method for lingal mlti-data, the keyword extraction that is specialized to the langage sed in a lingal data object is

2 Figre 1. Overview of BOSS performed. If Japanese is sed in the data object, Japanese keywords are gotten after this process. On the other hands, if other langage is sed, the keywords that are extracted in this process are then translated into Japanese keywords sing a bilingal dictionary. In the keyword extraction specialized to a langage, keyword candidates are recognized, modified, and redced only sing srface information sch as character type and freqency. This method can not extract the words that occr in the data object as keywords, bt it can extract keywords fast becase it does not reqire complex lingistic analysis. In the translation of keyword candidates, the bilingal dictionary inclding only nons of each langage is sed. In the case that there are some Japanese words eqivalent to a word in a certain langage, only one Japanese word with a general meaning is selected. 3.2 Method for Mlti-media Data In the method for media data, the signatre that identifies a certain data object is first made from the inptted data, and then the Japanese keywords that are associated with the signatre in advance are retrieved. A signatre is a list of bits of fixed length and consists of several attribtes of files sch as file names, file sizes, and bit information that is extracted from the data itself. The assignment of Japanese keywords to each media data is performed manally. 4. JAPANESE IR ENGINE The Japanese IR engine searches for data related to the keywords that are extracted from a qery, and otpts the reslt in descending order of its relevance to the keywords. The relevance between keywords and each stored data is compted sing the vector-space model. In this model, both of them are represented by vectors on a vector space. In the IR engine, index terms that correspond to each element of a vector are not keywords, bt strings are frther extracted from the keywords. They are selected in advance throgh learning process on test docments. 4.1 Vector Representation The contents of qeries and stored data objects are represented by vectors. Each element of a vector is associated with a particlar character bigram. If a character bigram appears in the extracted Japanese keywords, the corresponding element of a vector is set to 1. Otherwise, the element is set to 0. Vectors that are generated for stored data objects are retained in an inverted index to speed p the comptation of their relevance to a qery. The inverted index is a table identifying data objects corresponding to index terms. Each entry of the index incldes a particlar index term and the corresponding list of data object nmbers. The se of the inverted index enables s to search for relevant data objects qickly becase it loses the need of comptation of relevance for all vectors. Index terms are selected in advance throgh atomatic indexing process for data objects of a collection. In this process, keywords

3 are first extracted from the data objects. Then, character bigrams are frther extracted from the keywords. The freqency of each niqe bigram in the keywords are compted. Finally, the indicated nmber of bigrams with high freqencies is selected as index terms. 4.2 Searching in Relevance Order Let q = (q 1, q 2,..., q n ) and d = (d 1, d 2,..., d n ) be the vector for a qery and the vector for a stored data object, respectively. The relevance r between them is compted as follows. q d r = N ( q) + N ( d ) q d where q d, N(q), N(d) are compted as follows. q = N ( q) = N ( d ) = n i= 1 n i i= 1 n i= 1 q i d i q d i 5. EVALUATION 5.1 Demonstration of BOSS BOSS works on Windows NT and handles texts in English, Japanese, and Korean, images of JPEG format, and sonds of MIDI format. Figre 2 shows the reslt of retrieving information that is relevant to the English text abot a golf tornament. The pper left, bottom, and pper right windows in the figre are the content of the English text, the list of data objects that relevant to it, and the content of the most relevant Japanese text in the list, respectively. Figre 3 shows the reslt of retrieving information that is relevant to the jpeg image, the portrait of Mozart. The list at the bottom window in the figre incldes several kinds of data objects like texts, images, and sonds. The first relevant data object is a sond data. When clicking a mose on the play btton at the pper right of the window, msic gets started. Like this, BOSS can retrieve varios kinds of data objects for a particlar data object. 5.2 Reslts of the Ad Hoc Task In NTCIR-1, we participate the ad hoc IR task to evalate the Japanese IR engine of BOSS. In the task, docments for each of 53 topics are retrieved, and top 1,000 docments that are retrieved for each topic are jdged whether they are relevant to the topic or not. This task is performed sing the NTCIR. The NTCIR is presented by NACSIS to enhance research in Japanese text retrieval. It consists of docments, search topics, and relevance assessments for each search topic. The docments are 339,483 abstracts taken form NACSIS Academic Conference Papers Database. The search topics are formatted descriptions of ser s needs. They consist of 5 fields: Title, Description, Narrative, Concept and Field. The relevance assessments are lists of the docments that are jdged relevant to each of search topics. We sbmitted one official ad hoc rn, JALAB, to NACSIS. After this sbmission, we perform new five ad hoc rns, JALAB1, Figre 2. Reslt of retrieving information that is relevant to the English text abot a golf tornament

4 Figre 3. Reslt of retrieving information that is relevant to the jpeg image, the portrait of Mozart JALAB2, JALAB3, JALAB4, and JALAB5. The contents of the six rns are as follows. JALAB (sbmitted): The Aoe laboratory (AL) corps is sed in the selection of index terms. The AL corps is a collection of abot 15,000 docments that we collected from varios fields by itself. The total nmber of the index terms that are extracted from the docments in the corps is 56,000. In this rn, the description field is sed for each topic. JALAB1 (new): The collection of docments in the NTCIR is sed in the selection of index terms. The total nmber of index terms that are extracted from the docments in the collection is 366,886. In this rn, the title field is sed for each topic. JALAB2 (new): The sed docments in the selection of index terms and the total nmber of index terms are the same as JALAB1. In this rn, the description field is sed for each topic. JALAB3 (new): The sed docments in the selection of index terms and the total nmber of index terms are the same as JALAB1. In this rn, the narrative field is sed for each topic. JALAB4 (new): The sed docments in the selection of index terms and the total nmber of index terms are the same as JALAB1. In this rn, the title, description, and narrative fields are sed for each topic. JALAB5 (new): The sed docments in the selection of index terms and the total nmber of index terms are the same as JALAB1. In this rn, the description and concept fields are sed for each topic. The performance of the IR engine is measred in precision and recall. is the nmber of relevant data objects retrieved divided by the total nmber of data objects retrieved. Recall is the nmber of relevant data objects retrieved divided by the total nmber of relevant data objects. The reslts of the ad hoc task are as follows. Table 1 and Table 2 show the smmary of the reslts for the above 6 rns sing the relevance assessments, Relevant qrels (JE-1) and Partial Relevant qrels (JE-2), respectively. Figre 4 and Figre 5 show the recall precision crves for the reslts sing JE-1 and JE-2, respectively. 6. CONCLUSION In this paper, we have described or developing MLMMIR system, BOSS, and reported the evalation reslts of the Japanese IR engine of BOSS for the ad hoc task in NTCIR-1. With respect to BOSS, we have jst explained the overview of the system becase BOSS is not so complete as the overall evalation can be made for the system. Completing the system, many existing and new techniqes mst be introdced into it. With respect to the evalation, we have actally recognized that it is difficlt to retrieve relevant docments in the way of weighting index terms only by sing the information of whether they occr in docments or not. We need to introdce term weighting methods like TF-IDF[8].

5 Table 1. Reslt of the ad-hoc task for JE-1 Rn ID Rn desctiption Atomatic Atomatic Atomatic Atomatic Atomatic Atomatic Nmber of topics Used fields of topics Description Title Description Narrative Title Description Narrative Description Concept Collection sed in term indexing AL Corps NTCIR1 NTCIR1 NTCIR1 NTCIR1 NTCIR1 Nmber of index terms 56, , , , , ,886 Total nmber of docments over all topics Retreived: 52,051 53,000 53,000 53,000 53,000 53,000 Relevant: 1,910 1,910 1,910 1,910 1,910 1,910 Rel-ret: Recall Level Averages Recall Average precision over all relevant docs Non-interpolated Docment Level Averages Docment Level At 5 docs At 10 docs At 15 docs At 20 docs At 30 docs At 100 docs At 200 docs At 500 docs At 1,000 docs R- (precision after R docs retrieved (where R is the nmber of relevant docments) ) Exact

6 Table 2. Reslt of the ad-hoc task for JE-2 Rn ID Rn desctiption Atomatic Atomatic Atomatic Atomatic Atomatic Atomatic Nmber of topics Used fields of topics Description Title Description Narrative Title Description Narrative Description Concept Collection sed in term indexing AL Corps NTCIR1 NTCIR1 NTCIR1 NTCIR1 NTCIR1 Nmber of index terms 56, , , , , ,886 Total nmber of docments over all topics Retreived: 52,051 53,000 53,000 53,000 53,000 53,000 Relevant: 2,345 2,345 1,910 1,910 1,910 1,910 Rel-ret: ,052 Recall Level Averages Recall Average precision over all relevant docs Non-interpolated Docment Level Averages Docment Level At 5 docs At 10 docs At 15 docs At 20 docs At 30 docs At 100 docs At 200 docs At 500 docs At 1,000 docs R- (precision after R docs retrieved (where R is the nmber of relevant docments) ) Exact

7 JALAB JALAB1 JALAB2 JALAB3 JALAB4 JALAB Recall Figre 4 Recall-precision crves for JE JALAB JALAB1 JALAB2 JALAB3 JALAB4 JALAB Recall Figre 5 Recall-precision crves for JE-2

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