  It is beneficial to automate the process of deriving concept hierarchies from
corpora since a manual construction of concept hierarchies is typically a
time-consuming and resource-intensive process. As such, the overall process of
learning concept hierarchies from corpora encompasses a set of steps: parsing
the text into sentences, splitting the sentences and then tokenising it. After
the lemmatisation step, the pairs are extracted using FCA. However, there might
be some uninteresting and erroneous pairs in the formal context. Generating
formal context may lead to a time-consuming process, so formal context size
reduction is required to remove uninterested and erroneous pairs, taking less
time to extract the concept lattice and concept hierarchies accordingly. In
this premise, this study aims to propose two frameworks: (1) A framework to
review the current process of deriving concept hierarchies from corpus
utilising FCA; (2) A framework to decrease the formal contexts ambiguity of the
first framework using an adaptive version of ECA*. Experiments are conducted by
applying 385 sample corpora from Wikipedia on the two frameworks to examine the
reducing size of formal context, which leads to yield concept lattice and
concept hierarchy. The resulting lattice of formal context is evaluated to the
standard one using concept lattice-invariants. Accordingly, the homomorphic
between the two lattices preserves the quality of resulting concept hierarchies
by 89% in contrast to the basic ones, and the reduced concept lattice inherits
the structural relation of the standard one. The adaptive ECA* is examined
against its four counterpart baseline algorithms to measure the execution time
on random datasets with different densities (fill ratios). The results show
that adaptive ECA* performs concept lattice faster than other mentioned
competitive techniques in different fill ratios.
