Reviewing and Evaluating Automatic Term Recognition Techniques Export

@incollection{citeulike:4056835, abstract = {Automatic Term Recognition (ATR) is defined as the task of identifying domain specific terms from technical corpora. Termhood-based approaches measure the degree that a candidate term refers to a domain specific concept. Unithood-based approaches measure the attachment strength of a candidate term constituents. These methods have been evaluated using different, often incompatible evaluation schemes and datasets. This paper provides an overview and a thorough evaluation of state-of-the-art ATR methods, under a common evaluation framework, i.e. corpora and evaluation method. Our contributions are two-fold: (1) We compare a number of different ATR methods, showing that termhood-based methods achieve in general superior performance. (2) We show that the number of independent occurrences of a candidate term is the most effective source for estimating term nestedness, improving ATR performance.}, author = {Korkontzelos, Ioannis and Klapaftis, Ioannis and Manandhar, Suresh}, citeulike-article-id = {4056835}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-540-85287-2_24}, citeulike-linkout-1 = {http://www.springerlink.com/content/l55471g5259560k9}, doi = {10.1007/978-3-540-85287-2_24}, journal = {Advances in Natural Language Processing}, keywords = {term\_recognition}, pages = {248--259}, posted-at = {2009-10-30 07:26:19}, priority = {2}, title = {Reviewing and Evaluating Automatic Term Recognition Techniques}, url = {http://dx.doi.org/10.1007/978-3-540-85287-2_24}, year = {2008} }

TY - CHAP ID - citeulike:4056835 L3 - citeulike-article-id:4056835 N2 - Automatic Term Recognition (ATR) is defined as the task of identifying domain specific terms from technical corpora. Termhood-based approaches measure the degree that a candidate term refers to a domain specific concept. Unithood-based approaches measure the attachment strength of a candidate term constituents. These methods have been evaluated using different, often incompatible evaluation schemes and datasets. This paper provides an overview and a thorough evaluation of state-of-the-art ATR methods, under a common evaluation framework, i.e. corpora and evaluation method. Our contributions are two-fold: (1) We compare a number of different ATR methods, showing that termhood-based methods achieve in general superior performance. (2) We show that the number of independent occurrences of a candidate term is the most effective source for estimating term nestedness, improving ATR performance. JF - Advances in Natural Language Processing EP - 259 TI - Reviewing and Evaluating Automatic Term Recognition Techniques SP - 248 KW - term_recognition AU - Korkontzelos, Ioannis AU - Klapaftis, Ioannis AU - Manandhar, Suresh PY - 2008/// UR - http://dx.doi.org/10.1007/978-3-540-85287-2_24 ER -