BIOADI: a machine learning approach to identifying abbreviations and definitions in biological literature

BACKGROUND: To automatically process large quantities of biological literature for knowledge discovery and information curation, text mining tools are becoming essential. Abbreviation recognition is related to NER and can be considered as a pair recognition task of a terminology and its corresponding abbreviation from free text. The successful identification of abbreviation and its corresponding definition is not only a prerequisite to index terms of text databases to produce articles of related interests, but also a building block to improve existing gene mention tagging and gene normalization tools. RESULTS: Our approach to abbreviation recognition (AR) is based on machine-learning, which exploits a novel set of rich features to learn rules from training data. Tested on the AB3P corpus, our system demonstrated a F-score of 89.90% with 95.86% precision at 84.64% recall, higher than the result achieved by the existing best AR performance system. We also annotated a new corpus of 1200 PubMed abstracts which was derived from BioCreative II gene normalization corpus. On our annotated corpus, our system achieved a F-score of 86.20% with 93.52% precision at 79.95% recall, which also outperforms all tested systems. CONCLUSION: By applying our system to extract all short form-long form pairs from all available PubMed abstracts, we have constructed BIOADI. Mining BIOADI reveals many interesting trends of bio-medical research. Besides, we also provide an off-line AR software in the download section on http://bioagent.iis.sinica.edu.tw/BIOADI/

A Burst-by-Burst Adaptive Joint-Detection Based CDMA Speech Transceiver

A burst-by-burst adaptive speech transceiver is proposed, which can drop its source coding rate and speech quality under transceiver control in order to invoke a more error resilient modem mode amongst less favourable channel conditions. The novel, high-quality, Adaptive Multi-Rate (AMR) speech codec [5], operated at bit rates of 4.75 and 10.2 kbps and combined with sourcesensitivity-matched Redundant Residue Number Systems (RRNS) based channel codes. Burst-by-burst adaptive Joint-Detection based Code-Division Multiple Access (JDCDMA) is used for transmitting the dual-rate bitstream generated by the AMR speech codec