This paper proposes a method of combining two n-gram language models, one constructed from a very small corpus of the right domain of interest, the other constructed from a large but less adequate corpus, resulting in a significantly enhanced language model. This method is based on the observation that a small corpus from the right domain has high quality n-grams but has serious sparseness problem, while a large corpus from a different domain has more n-gram statistics but inadequately biased. Two n-gram models are combined by extending the idea of Katz's backoff. We ran experiments with 3-gram language models constructed from newspaper corpora of several million to tens of million words together with models from smaller broadcast news corpora. The target domain was broadcast news. We obtained significant improvement (30%) by incorporating a small corpus around one thirtieth size of the newspaper corpus. © Springer-Verlag 2004.
CITATION STYLE
Cho, S., Kim, S. H., Park, J., & Lee, Y. J. (2004). Overcoming the sparseness problem of spoken language corpora using other large corpora of distinct characteristics. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2945, 407–411. https://doi.org/10.1007/978-3-540-24630-5_48
Mendeley helps you to discover research relevant for your work.