Most research related to unithood were conducted as part of a larger effort for the determination of termhood. Consequently, novelties are rare in this small sub-field of term extraction. In addition, existing work were mostly empirically motivated and derived. We propose a new probabilistically-derived measure, independent of any influences of termhood, that provides dedicated measures to gather linguistic evidence from parsed text and statistical evidence from Google search engine for the measurement of unithood. Our comparative study using 1,825 test cases against an existing empirically-derived function revealed an improvement in terms of precision, recall and accuracy.
@cite_3 proposed a measure known as for extracting complex terms. The measure is based upon the claim that a substring of a term candidate is a candidate itself given that it demonstrates adequate independence from the longer version it appears in. For example, , and are acceptable as valid complex term candidates. However, is not. Therefore, some measures are required to gauge the strength of word combinations to decide whether two word sequences should be merged or not. Given a word sequence @math to be examined for unithood, the is defined as: where @math is the number of words in @math , @math is the set of longer term candidates that contain @math , @math is the longest n-gram considered, @math is the frequency of occurrence of @math , and @math . While certain researchers @cite_0 consider as a termhood measure, others @cite_5 accept it as a measure for unithood. One can observe that longer candidates tend to gain higher weights due to the inclusion of @math in Equation . In addition, the weights computed using Equation are purely dependent on the frequency of @math .
@cite_0 @cite_5 @cite_3
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In this paper, we propose a new idea for the automatic recognition of domain specific terms. Our idea is based on the statistics between a compound noun and its component single-nouns. More precisely, we focus basically on how many nouns adjoin the noun in question to form compound nouns. We propose several scoring methods based on this idea and experimentally evaluate them on the NTCIRI TMREC test collection. The results are very promising especially in the low recall area. The information used for the extraction of terms can be considered as rather 'internal', i.e. coming from the candidate string itself. This paper presents the incorporation of 'external' information derived from the context of the candidate string. It is embedded to the C-value approach for automatic term recognition (ATR), in the form of weights constructed from statistical characteristics of the context words of the candidate string.