caa a22 4500 467884080 CHVBK 20180406152729.0 cr unu---uuuuu 170328e20060401xx s 000 0 eng 10.1007/s10032-005-0146-7 doi (NATIONALLICENCE)springer-10.1007/s10032-005-0146-7 Stable methods for recognizing acronym-expansion pairs: from rule sets to hidden Markov models [Elektronische Daten] [Eduardo Schumann, Klaus Schulz] The replacement of textual units by synonymous canonical forms is an important prerequisite for many variants of automated text analysis. In scientific texts, one common normalization step is the consistent replacement of acronyms by their definitions. For many acronyms, the definition is found at a certain position of the text where the acronym is introduced and "expanded” to a synonymous sequence of full words. A recent approach to detecting acronym-expansion pairs by Park and Byrd [19] describes possible graphical correspondences between acronyms and expansions by means of fine-grained rules. Here we show how rule sets as used in [19] can be translated into hidden Markov models that abstract from details of the graphical correspondence and improve recall in a significant way. Stability in terms of precision is ensured by exploiting simple properties of the expansion with an optional reinforcement of linguistic knowledge. With this extension of the original formalism, the introduction of large rule sets can be avoided and a fixed model can be applied to a large variety of texts without retraining, with good values both for recall and precision. Springer-Verlag, 2005 Acronym recognition nationallicence Biomedical texts nationallicence Automated text analysis nationallicence Terminological expressions nationallicence Hidden Markov models nationallicence Schumann Eduardo CIS, University of Munich, Oettingenstr 67, D-80538, München, Germany aut Schulz Klaus CIS, University of Munich, Oettingenstr 67, D-80538, München, Germany aut International Journal of Document Analysis and Recognition (IJDAR) Springer-Verlag 8/1(2006-04-01), 1-99999 1433-2833 8:1<1 2006 8 10032 https://doi.org/10.1007/s10032-005-0146-7 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10032-005-0146-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Schumann Eduardo CIS, University of Munich, Oettingenstr 67, D-80538, München, Germany aut NATIONALLICENCE 700 1- Schulz Klaus CIS, University of Munich, Oettingenstr 67, D-80538, München, Germany aut NATIONALLICENCE 773 0- International Journal of Document Analysis and Recognition (IJDAR) Springer-Verlag 8/1(2006-04-01), 1-99999 1433-2833 8:1<1 2006 8 10032 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer