Latent semantic indexing: a probabilistic analysis
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Distribution of content words and phrases in text and language modelling
Natural Language Engineering
Characterizing Genres of Web Pages: Genre Hybridism and Individualization
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
Dependency-Based Construction of Semantic Space Models
Computational Linguistics
A Bayesian mixture model for term re-occurrence and burstiness
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Counting lumps in word space: density as a measure of corpus homogeneity
SPIRE'05 Proceedings of the 12th international conference on String Processing and Information Retrieval
Inferring gender of movie reviewers: exploiting writing style, content and metadata
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Usefulness of sentiment analysis
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Discovery and analysis of evolving topical social discussions on unstructured microblogs
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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The highly variable and dynamic word usage in social media presents serious challenges for both research and those commercial applications that are geared towards blogs or other user-generated non-editorial texts. This paper discusses and exemplifies a terminology mining approach for dealing with the productive character of the textual environment in social media. We explore the challenges of practically acquiring new terminology, and of modeling similarity and relatedness of terms from observing realistic amounts of data. We also discuss semantic evolution and density, and investigate novel measures for characterizing the preconditions for terminology mining.