Using machine learning to perform automatic term recognition

TL;DR: A novel method for labeling large amounts of high-quality training data for ATA in an unsupervised fashion and shows that the method of automatically generating training data is eective and the two ATA methods successfully generalize, considerably increasing recall while preserving high precision relative to a state-of-the-art baseline.

TL;DR: The results show a lot of variation between different systems and illustrate how some methodologies reach higher precision or recall, how different systems extract different types of terms, how some are exceptionally good at finding rare terms, or are less impacted by term length.

TL;DR: An attempt is made to understand more fully the role that language plays in the development of a person's identity and how language contributes to an individual’s well-being.

TL;DR: It is argued that the automatic term extraction procedure is an inherently multifactor process and the term extraction models needs to be based on multiple features including a specific type of a terminological resource under development.

TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.

TL;DR: This paper evaluates the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems, and proposes a number of modifications resulting in an algorithm RIPPERk that is very competitive with C4.5 and C 4.5rules with respect to error rates, but much more efficient on large samples.

TL;DR: By adding linguistic knowledge to the representation, rather than relying only on statistics, a better result is obtained as measured by keywords previously assigned by professional indexers, by extracting NP-chunks gives a better precision than n-grams.

TL;DR: In this paper, the problem of automatically extracting keyphrases from text is treated as a supervised learning task, where the learning algorithm must learn to classify as positive or negative examples of key phrases.