A Transitive Model for Extracting Translation Equivalents of Web Queries through Anchor Text Mining

Transcription

1 A Trasitive Model for Extractig Traslatio Equivalets of Web Queries through Achor Text Miig We-Hsiag Lu Istitute of Iformatio Sciece Academia Siica; Dept. of Computer Sciece ad Iformatio Egieerig Natioal Chiao Tug Uiversity Hsichu 300, Taiwa, ROC Lee-Feg Chie Istitute of Iformatio Sciece, Academia Siica Nagag 5, Taiwa, ROC Hsi-Jia Lee Dept. of Computer Sciece ad Iformatio Egieerig Natioal Chiao Tug Uiversity Hsichu 300, Taiwa, ROC Abstract Oe of the existig difficulties of cross-laguage iformatio retrieval (CLIR) ad Web search is the lack of appropriate traslatios of ew termiology ad proper ames. Differet from covetioal approache i our previous research we developed a approach for exploitig Web achor texts as live biligual corpora ad reducig the existig difficulties of query term traslatio. Although Web achor text udoubtedly, are very valuable multiligual ad wide-scoped hypertext resource ot every particular pair of laguages cotais sufficiet achor texts i the Web to extract correspodig traslatios i the laguage pair. For more geeralized applicatio i this paper we exted our previous approach by addig a phase of trasitive (idirect) traslatio via a itermediate (third) laguage, ad propose a trasitive model to further exploit achor-text miig i term traslatio extractio applicatios. Prelimiary experimetal results show that may query traslatios which caot be obtaied usig the previous approach ca be extracted with the improved approach.. Itroductio Cross-laguage iformatio retrieval (CLIR), addressig the special eed where users ca query i oe laguage ad retrieve relevat documets writte or idexed i aother laguage, has become a importat issue i the research of iformatio retrieval (Dumais et al., 996; Davis et al., 997; Ballesteros & Croft, 998; Nie et al., 999). However, its applicatio to practical Web search services has ot lived up to expectatio sice they suffer a major bottleeck that lacks up-to-date biligual lexicos cotaiig the traslatio of popular query terms such as proper ous (Kwok, 200). To eable capability of CLIR, existig IR systems mostly rely o biligual dictioaries for cross-ligual retrieval. I these system queries submitted i a source laguage ormally have to be traslated ito a target laguage by meas of simple dictioary lookup. These dictioary-based techiques are limited i real-world applicatio sice the queries give by users ofte cotai proper ous. Aother kid of popular approaches to dealig with query traslatio based o corpus-based techiques uses a parallel corpus cotaiig aliged seteces whose traslatio pairs are correspodig to each other (Brow et al., 993; Daga et al., 993; Smadja et al., 996). Although more reliable traslatio equivalets ca be extracted by these techique the uavailability of large eough parallel corpora for various subject domais ad multiple laguages is still i a thory situatio. O the other had, the alterative approach usig comparable or urelated text corpora were studied by Rapp (999) ad Fug et al. (998). This task is more difficult due to lack of parallel correlatio betwee documet or setece pairs. I our collected query log most of user queries cotai oly oe or two word so we use query term, query or term iterchageably i this paper.

2 I our previous research we have developed a approach to extractig traslatios of Web queries through miig of Web achor texts ad lik structures (Lu, et al., 200). This approach exploits Web achor texts as live biligual corpora to reduce the existig difficulties of query traslatio. Achor text set which are composed of a umber of achor texts likig to the same page may cotai similar descriptio texts i multiple laguage thus it is more likely that user s queries ad their correspodig traslatios frequetly appear together i the same achor text sets. The achor-text miig approach has bee foud effective particularly for proper ame such as iteratioal compay ame ames of foreig movie star worldwide evet e.g., Yahoo, Athrax, Harry Potter, etc. Discoverig useful kowledge from the potetial resource of Web achor texts is still ot fully explored. Accordig to our previous experimet the extracted traslatio equivalets might ot be reliable eough whe a query term whose correspodig traslatios either appear ifrequetly i the same achor text sets or eve do ot appear together. Especially, the traslatio process will be uavailable if there is a lack of sufficiet achor texts for a particular laguage pair. Although Web achor text udoubtedly, are live multiligual resource ot every particular pair of laguages cotais sufficiet achor texts. To deal with the problem this paper exteds the previous achor-text-based approach by addig a phase of idirect traslatio via a itermediate laguage. For a query term which is uable to be traslated, our idea is to traslate it ito a set of traslatio cadidates i a itermediate laguage, ad the seek for the most likely traslatio from the cadidate which are traslated from the itermediate laguage ito the target laguage (Gollis et al., 200; Bori, 2000). We therefore propose a trasitive traslatio model to further exploit achor text miig for traslatig Web queries. A series of experimets has bee coducted to realize the performace of the proposed approach. Prelimiary experimetal results show that may query traslatios which caot be obtaied usig the previous approach ca be extracted with the improved approach. 2 The Previous Approach For query traslatio, the achor-text-based approach is a ew techique compared with the biligual-dictioary- ad parallel-corpus-based approaches. I this sectio we will itroduce the basic cocept of the achor-text-based approach. For more details please refer to our iitial work (Lu, et al., 200). 2. Achor-Text Set A achor text is the descriptive part of a out-lik of a Web page. It represets a brief descriptio of the liked Web page. For a Web page (or URL) u i, its achor-text set is defied as all of the achor texts of the lik i.e., u i 's i-lik poitig to u i. I geeral, the achor-text set records u i 's alterative cocepts ad textual expressios such as titles ad headig which are cited by other Web pages. With differet preferece covetios ad laguage competece, the achor-text set could be composed of multiligual phrase short text acroym or eve u i 's URL. For a query term appearig i the achor-text set, it is likely that its correspodig traslatios also appear together. The achor-text sets ca be cosidered as a comparable corpus of traslated text from the viewpoit of traslatio extractio. 2.2 The Probabilistic Iferece Model To determie the most probable target traslatio t for source query term we developed a probabilistic iferece model (Wog et al., 995). This model is adopted for estimatig probability value betwee source query ad each traslatio cadidate that co-occur i the same achor-text sets. The estimatio assumes that the achor texts likig to the same pages may cotai similar terms with aalogous cocepts. Therefore, a cadidate traslatio has a higher chace to be a effective traslatio if it is writte i the target laguage ad frequetly co-occurs with the source query term i the same achor-text sets. I the field of Web research, it has bee prove that the use of lik structures is effective for estimatig the 2

3 authority of Web pages (Kleiberg, 998; Chakrabarti et al., 998). The model further assumes that the traslatio cadidates i the achor-text sets of the pages with higher authority may have more reliability i cofidece. The similarity estimatio fuctio based o the probabilistic iferece model is defied below: s t) s t) = = s t) = s t ui) ( s t) ui) s t ui) ui). s t ui) ui) () The above measure is adopted to estimate the degree of similarity betwee source term s ad target traslatio t. The measure is estimated based o their co-occurrece i the achor text sets of the cocered Web pages U = {u, u 2,... u }, i which u i is a page of cocer ad u i ) is the probability value used to measure the authority of page u i. By cosiderig the lik structures ad cocept space of Web page u i ) is estimated with the probability of u i beig liked, ad its estimatio is defied as follows: u i )= L(u i )/Σ j=, L(u j ), where L(u j )= the umber of i-liks of page u j. Such estimatio is simplified from HITS algorithm (Kleiberg, 998). I additio, we assume that s ad t are idepedet give u i, the the joit probability s t u i ) is equal to the product of s u i ) ad t u i ), ad the similarity measure becomes: s t) = s u)t i u)p i ( ui).(2) [ s ui) + t ui) s u)t i u) i ] ui) The values of s u i ) ad t u i ) are defied to be estimated by calculatig the fractios of the umbers of u i s i-liks cotaiig s ad t over L(u i ), respectively. Therefore, a cadidate traslatio has a higher cofidece value to be a effective traslatio if it frequetly co-occurs with the source term i the achor-text sets of the pages with higher authority. 2.3 The Estimatio Process For each source term, the probabilistic iferece model extracts the most probable traslatio that maximizes the estimatio. The estimatio process based o the model was developed to extract term traslatios through miig of real-world achor-text sets. The process cotais three major computatioal modules: achor-text extractio, term extractio ad term traslatio extractio. The achor-text extractio module was costructed to collect pages from the Web ad build up a corpus of achor-text sets. O the other had, for each give source term the term extractio module extracts key terms as the traslatio cadidate set from the achor-text sets of the pages cotaiig s. At last, the term traslatio module extracts the traslatio that maximizes the similarity estimatio. For more details about the estimatio proces please refer to our previous work (Lu et al., 200). To make a differece from the traslatio process via a itermediate laguage, the above process is called direct traslatio, ad the adopted model called direct traslatio model hereafter. Meawhile, we will use fuctio Pdirect i Equatio (3) for the estimatio of the direct traslatio. Pdirect( t) = log s t). (3) 3 The Improved Approach 3. The Idirect Traslatio Model As metioed above, for those query terms whose correspodig traslatios either appear ifrequetly i the same achor text sets or do ot appear together, the estimatio with equatio (2) is basically ureliable. To icrease the possibility of traslatio extractio especially for the source terms whose correspodig traslatios do ot co-occur, we add a phase of idirect traslatio through a itermediate laguage. For example, as show i Fig., our idea is to obtai the correspodig target 3

4 traslatio i simplified Chiese by traslatig the source term i traditioal Chiese ito a itermediate term Soy i Eglish, ad the seek for traslatig Soy ito a target term i simplified Chiese. For both the source query ad the target traslatio, we assume that their traslatios i the itermediate laguage are the same ad ca be foud. s (Traditioal Chiese) The above assumptio is ot urealistic. For example, it is possible to fid the Chiese traslatio of a Japaese movie star through submittig his/her Eglish ame to a search egie ad browsig the retrieved Chiese pages cotaiig the Eglish ame. The Web cotais large amouts of multiligual page ad Eglish is the most likely itermediate laguage betwee other laguages. Based o this assumptio, we exted the probabilistic iferece model ad propose a idirect traslatio model as the followig formula: Pidirect( t) = log s m, m t) m Soy (Eglish) t (Simplified Chiese) s : source term t : target traslatio m : itermediate traslatio Fig.. A abstract diagram showig the cocepts of direct traslatio ad idirect traslatio. log[ s m) m t)] = log s m) + log m t). (4), where m is the trasitive traslatio of s ad t i the itermediate laguage, s m) ad m t) are the probability values obtaied with the direct traslatio model which ca be calculated by Equatio (2). 3.2 The Trasitive Traslatio Model The trasitive model is developed to combie both the direct ad idirect traslatio models ad improve the traslatio accuracy. By combiig Equatio (3) ad (4), the trasitive traslatio model is defied as follows: Ptras( t) = Pdirect( t), if Pdirect(t) > θ Pidirect( t), otherwise., where θ is a predefied threshold value. (5) 4 Experimetal Results 4. Aalysis of Achor-Text Sets ad Query Logs I the iitial experimet we took traditioal Chiese ad simplified Chiese as the source ad target laguage respectively, ad used Eglish as the itermediate laguage. We have collected,980,86 traditioal Chiese Web pages i Taiwa. Amog these page 09,46 pages whose achor-text sets cotaied both traditioal Chiese ad Eglish terms were take as the achor-text set corpus. We also collected 2,79,7 simplified Chiese Web pages i Chia ad extracted 57,786 pages whose achor-text sets cotaied both simplified Chiese ad Eglish terms. I additio, through mergig the two Web page collectios ito a larger oe, we extracted 4,56 Web pages cotaiig both traditioal ad simplified Chiese terms. The three comparable corpora provide a potetial resource of traslatio pairs for some Web queries. I order to realize the feasibility i traslatig query terms via trasitive traslatio, we aim at fidig out the correspodig simplified Chiese traslatios of traditioal Chiese query terms via Eglish as the itermediate laguage. 4

5 We also collected popular query terms with the logs from two real-world Chiese search egies i Taiwa, i.e., Dreamer ad GAIS 2. The Dreamer log cotaied 228,566 uique query terms from a period of over 3 moths i 998, ad the GAIS log cotaied 4,82 uique query terms from a period of two weeks i 999. There were 9,709 most popular query terms whose frequecies were above 0 i both of the logs ad,230 of them were Eglish terms. After filterig out the terms which were used locally, we obtaied 258 terms. These query terms were take as the major test set i the term traslatio extractio aalysis. The traditioal Chiese traslatios of the test query terms were determied maually ad take as the source query set i the followig experimets. Accordig to our previous work (Lu et al., 200), there were three methods for term extractio, which is a ecessary process step i extractig traslatios from achor-text corpus. Sice we have ot yet collected a query log i simplified Chiese, i the followig experimets we adopted the PAT-tree-based keyword extractio method, which is a efficiet statistics-based approach that ca extract loger terms without usig a dictioary (Chie, 997). To evaluate the achieved performace of query traslatio, we used the average top- iclusio rate as a metric. For a set of test query term its top- iclusio rate is defied as the percetage of the query terms whose effective traslatio(s) ca be foud i the top extracted traslatios. 4.2 Performace with the Direct Traslatio Model I order to realize the feasibility of the trasitive traslatio model, we carried out some experimets based o the direct traslatio models ad the three differet achor-text set corpora i the first step. Table shows the results of the obtaied top-5 iclusio rate 2 These two search egies are secod-tier portals i Taiwa, whose logs have certai represetatives i the Chiese commuitie ad whose URL s are as follows: ad where terms TC, SC ad ENG represet traditioal Chiese, simplified Chiese ad Eglish terms respectively. The performace of traslatig TC ito SC is worse tha that of the other two sice the size of the achor-text set corpus cotaiig both TC ad SC is relatively small i compariso with the others. This is why we are pursuig i this paper to itegrate the direct traslatio with the idirect traslatio via a third laguage. However, the performace of the direct traslatio from TC to SC is used as a referece i compariso with our proposed models i the followig experimets. Table. Top- iclusio rates obtaied with the direct traslatio model ad the three specific laguage pairs corpora. Type Top Top2 Top3 Top4 Top5 TC=>SC 35.7% 43.0% 46.9% 49.6% 5.2% TC=>ENG 68.6% 82.2% 85.7% 88.0% 88.8% ENG=>SC 45.3% 55.8% 59.3% 6.6% 64.0% 4.3 Performace with the Idirect ad Trasitive Traslatio Models To realize the improvemet usig the trasitive traslatio model, some further experimets were coducted. As show i Table 2, the idirect ad trasitive traslatio models outperform tha the direct traslatio model. As metioed above, the size of the achor-text corpus that cotais both TC ad SC is small. The idirect traslatio model i therefore, helpful to fid out the correspodig traslatios for some terms with low-frequecy values i the corpora. For example, the traditioal Chiese term ±ñˆ was foud ca obtai its correspodig traslatio equivalet ±Áˆ i simplified Chiese via the itermediate traslatio Siemes, which caot be foud oly usig the direct traslatio. By examiig the top- traslatios obtaied with the three differet model it was foud that the iclusio rates ca be from 44.2% usig the idirect traslatio to 49.2% usig the trasitive traslatio model. Table 3 illustrates some of the traslatios extracted usig the trasitive traslatio model. 5

6 Table 2. Top- iclusio rates obtaied with differet models. Model Top Top2 Top3 Top4 Top5 Direct 35.7% 43.0% 46.9% 49.6% 5.2% Traslatio Idirect 44.2% 55.% 58.0% 59.7% 60.5% Traslatio Trasitive 49.2% 58.% 60.9% 6.6% 62.0% Traslatio Combiatio 55.8% 60.8% 64.0% 65.9% 67.8% of Trasitive Traslatio ad Lexico 4.4 Performace with a Itegratio of Lexico Lookup A additioal experimet was also made to compare with the use of a traslatio lexico for query traslatio. The lexico cotaied more tha 23,948 word/phrase etries i both traditioal Chiese ad simplified Chiese. It was foud the top- iclusio rate that usig the lexico lookup was 2.4% which is obviously lower tha the 49.2% that usig the proposed trasitive traslatio model. I additio, the top- iclusio rate ca reach to 55.8% (see the last row of Table 2) if both of the approaches are combied. With the combied approach, the traslatio(s) of a query term is picked up from the lexico if such a traslatio is already i the lexico, otherwise it is obtaied based o the trasitive traslatio model. 5 Cocludig Remarks Achor-text set corpus is a valuable resource for extractig traslatios of Web queries. How to exploit such kid of corpora i query traslatio is a challegig ad potetial research task. I this paper, we exted our previous approach by proposig a trasitive traslatio model ad achieve some improvemets o traslatig those queries whose traslatios caot be extracted usig the previous approach. The improved approach has bee prove particularly useful for the specific laguage pairs whose achor texts are isufficiet. However, there are still some problems eed to be further ivestigated i the future. Refereces Ballestero L. ad Croft, W. B. (997) Phrasal Traslatio ad Query Expasio Techiques for Cross-Laguage Iformatio Retrieval, Proceedigs of ACM-SIGIR 97, pp Bori, L. (2000) You ll Take the High Road ad I ll Take the Low Road: Usig a Third Laguage to Improve Biligual Word Aligmet, Proceedigs of the 8th COLING, pp Brow, P., Pietra, S. A. D., Pietra, V. D. J., Mercer, R. L. (993) The Mathematics of Machie Traslatio, Computatioal Liguistic 9(2), pp Chakrabarti, S., Dom, B., Gibso, D., Kleiberg, J., Raghava, P., Rajagopala, S. (998) Automatic Resource List Compilatio by Aalysig Hyperlik Structure ad Associated Text, Proceedigs of the seveth World Wide Web Coferece. Chie, L. F. (997) PAT-Tree-Based Keyword Extractio for Chiese Iformatio Retrieval, Proceedigs of ACM-SIGIR 97, pp Daga, I., Church, K. W., Gale, W. A (993) Robust Biligual Word Aligmet for Machie Aided Traslatio. Proceedigs of the Workshop o Very Large Corpora, pp. -8. Davi M. ad Ogde, W. C. (997) Quilt: Implemetig a large-scale cross-laguage text retrieval system, Proceedigs of ACM-SIGIR 97 Coferece, pp Dumai S. T., Ladauer, T. K., Littma, M. L. (996) Automatic Cross-liguistic Iformatio Retrieval Usig Latet Sematic Idexig, SIGIR 96 Workshop o Cross-Liguistic Iformatio Retrieval, pp Fug, P. ad Yee, L. Y. (998) A IR Approach for Traslatig New Words from Noparallel, Comparable Text Proceedigs of The 36th Aual Coferece of the Associatio for Computatioal Liguistic pp Golli T., Saderso, M. (200) Improvig Cross laguage Iformatio with Triagulated Traslatio, Proceedigs of ACM-SIGIR200 Coferece, pp Kleiberg, J. (998) Authoritative Sources i a Hyperliked Eviromet, Proceedigs of 9th ACM-SIAM Symposium o Discrete Algorithms. Kwok, K. L. (200) NTCIR-2 Chiese, Cross Laguage Retrieval Experimets Usig PIRCS, Proceedigs of NTCIR workshop meetig. Lu, W. H., Chie, L. F., Lee, H. J. (200) Achor Text Miig for Traslatio of Web Querie 6

7 Proceedigs of The 200 IEEE Iteratioal Coferece o Data Miig. Nie, J. Y., Isabelle, P., Simard, M., ad Durad, R. (999) Cross-laguage Iformatio Retrieval Based o Parallel Texts ad Automatic Miig of Parallel Texts from the Web, Proceedigs of ACM-SIGIR 99 Coferece. Rapp, R. (999) Automatic Idetificatio of Word Traslatios from Urelated Eglish ad Germa Corpora, Proceedigs of The 37th Aual Coferece of the Associatio for Computatioal Liguistics. Smadja, F., McKeow, K., Hatzivassiloglou, V. (996) Traslatig Collocatios for Biligual Lexicos: A Statistical Approach, Computatioal Liguistic 22(), pp Wog, S. K. M., Yao Y. Y. (995) O Modelig Iformatio Retrieval with Probabilistic Iferece, ACM trasactios o Iformatio System Vol.3, pp Table 3. Some examples of extracted target traslatios with the three differet models. (the asterisk idicates the correct traslatio) Source terms i traditioal Chiese Top-5 extracted target traslatios i simplified Chiese Direct Traslatio Idirect Traslatio Trasitive Traslatio Model Model Model ±Áˆ* (Siemes) (Compay) Š (Chia) (website) ³(cooperatio) ±ñˆ(siemes) Not available ±Áˆ* (Siemes) (Compay) Š (Chia) (website) ³(cooperatio) (Compaq) Not available * (Compaq) ÆÌ (computer compay) (compay) (America) (Soy) ÆÌ(computer) š* (Soy) š* (Soy) œ (our compay) * (Soy) * (Soy) Æ (movie site) (record) ¹(etertaimet) Š é(chiese versio) (record compay) * (Compaq) ÆÌ (computer compay) (compay) (America) ÆÌ(computer) š* (Soy) * (Soy) Æ (movie site) ¹(etertaimet) (record compay) 7