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.
 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.