Document: example Maharashtra. C 06 E 811 C 6 B. htm. txt म हफल वचणय स गललय तरण वर ब बटय च हलल (attack of a leapord on a young man who has gone to collect flowers of Moha) इसल पर , त . २२ - च र ळ आण म हफल वचणय स ठ जगल त गललय एक आद व स तरण वर ब बटय न अच नक हलल कलय न त तरण गभ र जखम झ ल आह. ह घटन शकरव र (त . २० ) मळझर (त . क नवट ) य ग व चय जगल त घडल . . . . इसल पर वन पर कषतर क रय लय अतरगत यण ऱय मळझर यथ ल आद व स तरण मन हर. . .
Topic example 1 टवट -२० व शवचषक त ल भ रत च कर ड पटतव )India’s championship in tewnty-20 Worldcup) <desc>पह लय आयस स व शव टवट -२० सरव तकषट -व जत -सपरधत ल भ रत चय व जय च वतत दण र लख श ध . </desc> <narr>टवट -२० व शचचषक सपरधमध ल प क सत न व रदध भ रत च व जय , हय ऐत ह स क व जय न म तत खळ डन कलल व करम तय न म ळव लल बकष स व परसक र च रककम स मन व र च तसच </p>
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<div style="width: auto;" class="description columns twelve"><p><img class="imgdescription" title="Tools • Terrier – Open source IR system – Models • TF-IDF (Vector space" src="https://present5.com/presentation/fa5063884c8834c0c94e04a4ef978b57/image-8.jpg" alt="Tools • Terrier – Open source IR system – Models • TF-IDF (Vector space" />
Tools • Terrier – Open source IR system – Models • TF-IDF (Vector space model) • DFR-BM 25 (Probabilistic) – Both models available in Terrier • Evaluation against relevance judged document for 25 queries </p>
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<div style="width: auto;" class="description columns twelve"><p><img class="imgdescription" title="Lemmatizer Vs stemmer – भ रत ल bhaarataalaa – for Bharat – भ रत" src="https://present5.com/presentation/fa5063884c8834c0c94e04a4ef978b57/image-9.jpg" alt="Lemmatizer Vs stemmer – भ रत ल bhaarataalaa – for Bharat – भ रत" />
Lemmatizer Vs stemmer – भ रत ल bhaarataalaa – for Bharat – भ रत च bhaarataachaa - of Bharat – भ रत त bhaarataat – in Bharat – भ रत वर bhaarataavar – on Bharat • Lemmatizer finds Lemma – भ रत • Stemmer finds stem: Longest unchangeable word prefix – भ रत </p>
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<div style="width: auto;" class="description columns twelve"><p><img class="imgdescription" title="Marathi suffixes • Suffixes include case markers, postposition markers etc. • Suffixes may get" src="https://present5.com/presentation/fa5063884c8834c0c94e04a4ef978b57/image-10.jpg" alt="Marathi suffixes • Suffixes include case markers, postposition markers etc. • Suffixes may get" />
Marathi suffixes • Suffixes include case markers, postposition markers etc. • Suffixes may get attached after another suffix • Example: – घर सम रच दख ल – घर -सम र -च -दख ल – gharaa-samor-chaa-dekhil – house-front- of-also – Root word: घर (ghar) (house) </p>
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<div style="width: auto;" class="description columns twelve"><p><img class="imgdescription" title="Morphological analyzer • Use of Marathi morphology analyzer – Better matching words • र" src="https://present5.com/presentation/fa5063884c8834c0c94e04a4ef978b57/image-11.jpg" alt="Morphological analyzer • Use of Marathi morphology analyzer – Better matching words • र" />
Morphological analyzer • Use of Marathi morphology analyzer – Better matching words • र म versus र म • Gives all possible roots – Selects first root – most frequent • Used at indexing and query processing end </p>
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<div style="width: auto;" class="description columns twelve"><p><img class="imgdescription" title="Lemmatizer Results MAP Rprecision Precision at 5 10 Recall TF-IDF without lemmatizer 0. 3366" src="https://present5.com/presentation/fa5063884c8834c0c94e04a4ef978b57/image-12.jpg" alt="Lemmatizer Results MAP Rprecision Precision at 5 10 Recall TF-IDF without lemmatizer 0. 3366" />
Lemmatizer Results MAP Rprecision Precision at 5 10 Recall TF-IDF without lemmatizer 0. 3366 0. 2944 0. 3167 0. 2583 0. 8724 TF-IDF + lemmatizer 0. 4003 0. 3551 0. 3417 0. 2917 0. 9686 DFR+ without lemmatizer 0. 3455 0. 3209 0. 3500 0. 2667 0. 8744 DFR-BM 25 + lemmatizer 0. 4140 0. 3686 0. 3833 0. 3083 0. 9619 0. 3625 0. 3797 0. 4600 0. 3960 0. 9178 DFR-BM 25 + lemmatizer (Fire submission) </p>
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<div style="width: auto;" class="description columns twelve"><p><img class="imgdescription" title="Suffixes • Usually ignored • Indexing suffixes - not studied • Index selected suffixes" src="https://present5.com/presentation/fa5063884c8834c0c94e04a4ef978b57/image-13.jpg" alt="Suffixes • Usually ignored • Indexing suffixes - not studied • Index selected suffixes" />
Suffixes • Usually ignored • Indexing suffixes - not studied • Index selected suffixes – Suffixes of space and time • • वर – var - on सम र – samor - in front of मधय – madhye - in नतर -nanter – after • Created manually – 66 words list </p>
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<div style="width: auto;" class="description columns twelve"><p><img class="imgdescription" title="Stop-words • • Most frequently occurring words Little discriminatory value Occur in 80 %" src="https://present5.com/presentation/fa5063884c8834c0c94e04a4ef978b57/image-14.jpg" alt="Stop-words • • Most frequently occurring words Little discriminatory value Occur in 80 %" />
Stop-words • • Most frequently occurring words Little discriminatory value Occur in 80 % or more documents Selected stop-words – त , य , न , अस , आह , य , ह , कर , त </p>
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<div style="width: auto;" class="description columns twelve"><p><img class="imgdescription" title="Results suffix indexing and stop-words MAP DFR-BM 25 Rprecision Precision at 5 at 10" src="https://present5.com/presentation/fa5063884c8834c0c94e04a4ef978b57/image-15.jpg" alt="Results suffix indexing and stop-words MAP DFR-BM 25 Rprecision Precision at 5 at 10" />
Results suffix indexing and stop-words MAP DFR-BM 25 Rprecision Precision at 5 at 10 Recall 0. 4381 0. 3846 0. 3917 0. 3167 0. 97085 0. 4433 0. 3798 0. 4000 0. 3208 0. 9731 + lemmatization + suffix Indexing DFR-BM 25 + lemmatization + suffix Indexing + stop-words </p>
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<div style="width: auto;" class="description columns twelve"><p><img class="imgdescription" title="P-R graph • Precision-recall graph for all four cases is show below " src="https://present5.com/presentation/fa5063884c8834c0c94e04a4ef978b57/image-16.jpg" alt="P-R graph • Precision-recall graph for all four cases is show below " />
P-R graph • Precision-recall graph for all four cases is show below </p>
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<div style="width: auto;" class="description columns twelve"><p><img class="imgdescription" title="Future work • Morphological analyzer – Accuracy 94. 5 % • Needs to be" src="https://present5.com/presentation/fa5063884c8834c0c94e04a4ef978b57/image-17.jpg" alt="Future work • Morphological analyzer – Accuracy 94. 5 % • Needs to be" />
Future work • Morphological analyzer – Accuracy 94. 5 % • Needs to be improved • Heuristic suffix stripping: unknown words • Handle derivational morphology • Spelling variations, common spelling mistakes </p>
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<div style="width: auto;" class="description columns twelve"><p><img class="imgdescription" title="Acknowledgement • “Cross Lingual Information Access” Project • Maharashtra times: Times Media Group, –" src="https://present5.com/presentation/fa5063884c8834c0c94e04a4ef978b57/image-18.jpg" alt="Acknowledgement • “Cross Lingual Information Access” Project • Maharashtra times: Times Media Group, –" />
Acknowledgement • “Cross Lingual Information Access” Project • Maharashtra times: Times Media Group, – http: //in. indiatimes. com/aboutus. cms • Sakal: Sakal Media Group – http: //www. sakaal. in/ </p>
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<div style="width: auto;" class="description columns twelve"><p><img class="imgdescription" title="References • http: //ir. dcs. gla. ac. uk/terrier/ • Ricardo Baeza Yates and Berthier" src="https://present5.com/presentation/fa5063884c8834c0c94e04a4ef978b57/image-19.jpg" alt="References • http: //ir. dcs. gla. ac. uk/terrier/ • Ricardo Baeza Yates and Berthier" />
References • http: //ir. dcs. gla. ac. uk/terrier/ • Ricardo Baeza Yates and Berthier Ribeiro Neto, Modern Information Retrieval • Jacques Savoy, Searching strategies for the Bulgarian language • Morphological Analyzer, CFILT </p>
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<div style="width: auto;" class="description columns twelve"><p><img class="imgdescription" title="Thank you " src="https://present5.com/presentation/fa5063884c8834c0c94e04a4ef978b57/image-20.jpg" alt="Thank you " />
Thank you </p>
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<div style="width: auto;" class="description columns twelve"><p><img class="imgdescription" title="" src="" alt="" />
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Скачать презентацию Marathi Marathi Monolingual Information Retrieval Mr Ashish
Marathi – Marathi Monolingual Information Retrieval Mr. Ashish Almeida Prof. Pushpak Bhattacharyya 
Overview • • Morphological analyzer Suffix processing Stop-words Future work 
Present work Search “भ रत ” – bhaarat – Bharat Will not match pages which has terms such as भ रत च – bharataachaa - Of Bharat भ रत त – bharataat - In Bharat Lack of large size corpus Unavailability of tools 
Corpus Statistics- Marathi • 99, 275 Documents (510 MB) – Maharashtra times – Sakal News • April 2004 to September 2007 • UTF-8 encoding • XML tags – DOC - document – DOCNO – document identifier – TEXT - article 
Topics • 100 topics • Aligned with English topics • XML tags – num : query identifier – title: title of the query – desc: description – narr: Additional information about the query • Cover all issues –local, international