Abstract. This paper describes the process of formalizing an existing, industrial domain specific language (dsl) that is based on the taskresource paradigm. Initially, the semantics of this dsl is defined informally and implicitly through an interpreter. The formalization starts by projecting the existing concrete syntax onto a formal abstract syntax that defines the language operators and process terms. Next, we define the dynamic operational semantics at the level of individual syntactical notions, using structural operational semantics (sos) as a formal metalanguage. Here, the impact of the formalization process on the dsl is considered in terms of disambiguation of underlying (semantic) language design decisions.
This paper describes a study involving the active integration of mathematics and music applied to the process of senior secondary students' understanding of the function concept. The study has used the frame of the post-positivist paradigm, quasi-experimental research design, multiple research methods, experimentaland control-groups, pre-tests and post-tests. The study has considered nine author-developed analogies between mathematics and music as tools to improve students' understanding of the function concept. The findings suggest that this type of active integration of mathematics and music makes a statistically significant difference in students' understanding of function.
Identifying the lemma of a Named Entity is important for many Natural Language Processing applications like Information Retrieval. Here we introduce a novel approach for Named Entity lemmatisation which utilises the occurrence frequencies of each possible lemma. We constructed four corpora in English and Hungarian and trained machine learning methods using them to obtain simple decision rules based on the web frequencies of the lemmas. In experiments our web-based heuristic achieved an average accuracy of nearly 91%.
Our paper discusses the potential use of Web Content Mining techniques for gathering scientific social information from the homepages of researchers. We will introduce our system which seeks [affiliation, position, start year, end year] information tuples on these homepages along with preliminary experimental results. We believe that the lessons learnt from these experiments may be useful for further scientific social web mining.
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