A statistical significance testing approach for measuring term burstiness with applications to domain-specific terminology extraction

Domain-specific terminology extraction is an important task in text analysis. A term in a corpus is said to be "bursty" when its occurrences are concentrated in few out of many documents. Being content rich, bursty terms are highly suited for subject matter characterization, and serve as natural candidates for identifying with technical terminology. Multiple measures of term burstiness have been proposed in the literature. However, the statistical significance testing paradigm has remained underexplored in text analysis, including in relation to term burstiness. To test these waters, we propose as our main contribution a multinomial language model-based exact test of statistical significance for term burstiness. Due to its prohibitive computational cost, we advance a heuristic formula designed to serve as a proxy for test P-values. As a complementary theoretical contribution, we derive a previously unreported relationship connecting the inverse document frequency and inverse collection frequency (two foundational quantities in text analysis) under the multinomial language model. The relation is used in the evaluation of our heuristic. Using the GENIA Term corpus benchmark, we compare our approach against established methods, demonstrating our heuristic's potential in identifying domain-specific technical terms. We hope this demonstration of statistical significance testing in text analysis serves as a springboard for future research.