Symbolic Machine Learning: a Different Answer to the Problem of the Acquisition of Lexical Knowledge from Corpora

One relevant way to structure the domain of lexical knowledge (complex terms, or relations between lexical units) acquisition from corpora is to oppose numerical versus symbolic techniques. Numerical approaches of acquisition exploit the frequential aspect of data, and use statistical techniques, while symbolic approaches exploit the structural aspect of data, and use structural or symbolic information. Methods from this former approach have been widely used and produce portable, robust, and fully automatic systems. They provide however poor explanations of their results, and may have difficulties to grasp very specific relations. The symbolic approach groups two strategies. The first one is the symbolic linguistic approach, in which operational definitions of the elements to acquire are manually established by linguists ―usually in the form of morpholexical patterns that carry the studied terms or relations―, or by a list of linguistic clues. However, when such patterns or clues are unknown, but examples of elements respecting the target terms or relation are known, techniques from the second strategy of this symbolic approach can be used, i.e. symbolic machine learning (ML) methods. This facet of this approach, far less known and employed, is just beginning to appear and widen in the natural language processing community. The aim of this paper is to point out the interest of such techniques, and to show how they can be used to infer efficient and expressive extraction patterns of complex terms or lexical relations from examples of elements that verify the target relations or the form of the terms. However, these techniques are often supervised, i.e. require to be (manually) fed by examples. We also explain that one method from each of the numerical and symbolic ML approaches can be combined in order to keep advantages from both: meaningful patterns, efficient extraction and portability.