{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T22:31:03Z","timestamp":1761863463895,"version":"3.41.0"},"reference-count":40,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2015,1,30]],"date-time":"2015-01-30T00:00:00Z","timestamp":1422576000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Cross-Lingual Information Access Project"},{"name":"D.I.T. Government of India"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2015,1,30]]},"abstract":"<jats:p>Temporal annotation of plain text is considered a useful component of modern information retrieval tasks. In this work, different approaches for identification and classification of temporal expressions in Hindi are developed and analyzed. First, a rule-based approach is developed, which takes plain text as input and based on a set of hand-crafted rules, produces a tagged output with identified temporal expressions. This approach performs with a strict F1-measure of 0.83. In another approach, a CRF-based classifier is trained with human tagged data and is then tested on a test dataset. The trained classifier identifies the time expressions from plain text and further classifies them to various classes. This approach performs with a strict F1-measure of 0.78. Next, the CRF is replaced by an SVM-based classifier and the same experiment is performed with the same features. This approach is shown to be comparable to the CRF and performs with a strict F1-measure of 0.77. Using the rule base information as an additional feature enhances the performances to 0.86 and 0.84 for the CRF and SVM respectively. With three different comparable systems performing the extraction task, merging them to take advantage of their positives is the next step. As the first merge experiment, rule-based tagged data is fed to the CRF and SVM classifiers as additional training data. Evaluation results report an increase in F1-measure of the CRF from 0.78 to 0.8. Second, a voting-based approach is implemented, which chooses the best class for each token from the outputs of the three approaches. This approach results in the best performance for this task with a strict F1-measure of 0.88. In this process a reusable gold standard dataset for temporal tagging in Hindi is also developed. Named the ILTIMEX2012 corpus, it consists of 300 manually tagged Hindi news documents.<\/jats:p>","DOI":"10.1145\/2629574","type":"journal-article","created":{"date-parts":[[2015,6,18]],"date-time":"2015-06-18T18:14:05Z","timestamp":1434651245000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Approaches to Temporal Expression Recognition in Hindi"],"prefix":"10.1145","volume":"14","author":[{"given":"Nitin","family":"Ramrakhiyani","sequence":"first","affiliation":[{"name":"TRDDC Pune"}]},{"given":"Prasenjit","family":"Majumder","sequence":"additional","affiliation":[{"name":"DAIICT Gandhinagar"}]}],"member":"320","published-online":{"date-parts":[[2015,1,30]]},"reference":[{"volume-title":"Proceedings of HLT-NAACL. 420--427","author":"Ahn D.","key":"e_1_2_2_1_1"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/383952.383954"},{"volume-title":"Proceedings of the International Temporal Web Analytics Workshop (TWAW\u201911)","author":"Alonso O.","key":"e_1_2_2_3_1"},{"key":"e_1_2_2_4_1","unstructured":"Bharati A. Chaitanya V. Sangal R. and Ramakrishnamacharyulu K. V. 1995. Natural Language Processing: A Paninian Perspective. Prentice-Hall of India New Delhi.  Bharati A. Chaitanya V. Sangal R. and Ramakrishnamacharyulu K. V. 1995. Natural Language Processing: A Paninian Perspective . Prentice-Hall of India New Delhi."},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1014348124664"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1967293.1967296"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-30586-6_59"},{"volume-title":"Proceedings of LREC.","year":"2010","author":"Jha G. N.","key":"e_1_2_2_8_1"},{"key":"e_1_2_2_9_1","unstructured":"Kodu T. 2005a. CRF++: Yet another CRF toolkit. http:\/\/crfpp.googlecode.com\/svn\/trunk\/doc\/index.html.  Kodu T. 2005a. CRF++: Yet another CRF toolkit. http:\/\/crfpp.googlecode.com\/svn\/trunk\/doc\/index.html."},{"key":"e_1_2_2_10_1","unstructured":"Kodu T. 2005b. YamCha: Yet another multipurpose CHunk annotator. http:\/\/www.chasen.org\/~taku\/software\/yamcha\/.  Kodu T. 2005b. YamCha: Yet another multipurpose CHunk annotator. http:\/\/www.chasen.org\/~taku\/software\/yamcha\/."},{"key":"e_1_2_2_11_1","unstructured":"Mani I. and Schiffman B. 2005. Temporally anchoring and ordering events in news. In Time and Event Recognition in Natural Language John Benjamins.  Mani I. and Schiffman B. 2005. Temporally anchoring and ordering events in news. In Time and Event Recognition in Natural Language John Benjamins."},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.3115\/1118238.1118249"},{"key":"e_1_2_2_13_1","unstructured":"Mazur P. 2008. TIMEX Portal. http:\/\/www.timexportal.info\/.  Mazur P. 2008. TIMEX Portal. http:\/\/www.timexportal.info\/."},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.3115\/1225403.1225412"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-04235-5_21"},{"key":"e_1_2_2_16_1","unstructured":"MITRE-Corporation. 2001. TIDES Temporal Annotation Guide. The MITRE Corporation.  MITRE-Corporation. 2001. TIDES Temporal Annotation Guide . The MITRE Corporation."},{"key":"e_1_2_2_17_1","unstructured":"MITRE-Corporation. 2005. Standard for the Annotation of Temporal Expressions. The MITRE Corporation.  MITRE-Corporation. 2005. Standard for the Annotation of Temporal Expressions . The MITRE Corporation."},{"volume-title":"Proceedings of the 7th Message Understanding Conference. DARPA.","year":"1998","key":"e_1_2_2_18_1"},{"key":"e_1_2_2_19_1","unstructured":"M. Negri and L. Marseglia. 2004. Recognition and normalization of time expressions: ITC-irst at TERN. Rapport Interne ITC-irst Trento.  M. Negri and L. Marseglia. 2004. Recognition and normalization of time expressions: ITC-irst at TERN. Rapport Interne ITC-irst Trento."},{"key":"e_1_2_2_20_1","unstructured":"NIST. 2004a. Automatic content extraction 2004. http:\/\/www.itl.nist.gov\/iad\/mig\/tests\/ace\/2004\/index.html.  NIST. 2004a. Automatic content extraction 2004. http:\/\/www.itl.nist.gov\/iad\/mig\/tests\/ace\/2004\/index.html."},{"volume-title":"The ACE 2004 evaluation plan. http:\/\/www.itl.nist.gov\/iad\/mig\/tests\/ace\/2004\/doc\/ace04-evalplan-v7.pdf.","year":"2004","author":"NIST.","key":"e_1_2_2_21_1"},{"volume-title":"Lessons: Regular expressions","year":"2012","author":"Oracle Corporation","key":"e_1_2_2_22_1"},{"volume-title":"Overview of FIRE","year":"2011","author":"Palchowdhury S.","key":"e_1_2_2_23_1"},{"volume-title":"Proceedings of the ARDA Workshop.","year":"2002","author":"Pustejovsky J.","key":"e_1_2_2_24_1"},{"key":"e_1_2_2_25_1","unstructured":"Pustejovsky J. Castano J. Ingria R. Sauri R. Gaizauskas R. Setzer A. 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