Abbreviated words carry critical information in the literature of many special domains. This paper reports our research in recognizing dotted abbreviations with MaxEnt model. The key points in our work include: (1) allowing the model to optimize with as many features as possible to capture the text characteristics of context words, and (2) utilizing simple lexical information such as sentence-i...

Boulder, Colorado, June 2009. c ©2009 Association for Computational Linguistics Automatic Chinese Abbreviation Generation Using Conditional Random Field Dong Yang, Yi-cheng Pan, and Sadaoki Furui Department of Computer Science Tokyo Institute of Technology Tokyo 152-8552 Japan {raymond,thomas,furui}@furui.cs.titech.ac.jp Abstract This paper presents a new method for automatically generating abb...

Data of interest to biomedical researchers associated with the Human Genome Project (HGP) is stored all over the world in a number of di erent electronic data formats and accessible through a variety of interfaces and retrieval languages. These data sources include conventional relational databases with SQL interfaces, formatted text les on top of which indexing is provided for e cient retrieva...

We propose a new Chinese abbreviation prediction method which can incorporate rich local information while generating the abbreviation globally. Different to previous character tagging methods, we introduce the minimum semantic unit, which is more fine-grained than character but more coarse-grained than word, to capture word level information in the sequence labeling framework. To solve the “ch...

The Simple and Robust Abbreviation Dictionary (S-RAD) provides an easy to implement, high performance tool for the construction of a biomedical symbol dictionary. We describe and evaluate the algorithms of the system and apply it to the MEDLINE document set. The resulting dictionary represents a useful tool to researchers and provides programmers with a mechanism to disambiguate abbreviation sy...