  Agriculture is a key component in any country's development. Domain-specific
knowledge resources serve to gain insight into the domain. Existing knowledge
resources such as AGROVOC and NAL Thesaurus are developed and maintained by the
domain experts. Population of terms into these knowledge resources can be
automated by using automatic term extraction tools for processing unstructured
agricultural text. Automatic term extraction is also a key component in many
semantic web applications, such as ontology creation, recommendation systems,
sentiment classification, query expansion among others. The primary goal of an
automatic term extraction system is to maximize the number of valid terms and
minimize the number of invalid terms extracted from the input set of documents.
Despite its importance in various applications, the availability of online
tools for the said purpose is rather limited. Moreover, the performance of the
most popular ones among them varies significantly. As a consequence, selection
of the right term extraction tool is perceived as a serious problem for
different knowledge-based applications. This paper presents an analysis of
three commonly used term extraction tools, viz. RAKE, TerMine, TermRaider and
compares their performance in terms of precision and recall, vis-a-vis RENT, a
more recent term extractor developed by these authors for agriculture domain.
