Building up phrase synonymy in English:the scope of 21st c.Roget Thesaurus
|Тема||Building up phrase synonymy in English:the scope of 21st c.Roget Thesaurus|
1. THEORETICAL NOTIONS OF THE ROGET’S THESAURUS 5
1.1 Roget’s Thesaurus 5
1.2 Structure of Roget’s Thesaurus 6
2. SYNONYMY IN THE ROGET THESAURUS 8
LIST OF REFERNCE SOURCES 17
Building up phrase synonymy in English:the scope of 21st c.Roget Thesaurus - Курсовая, Литература
Many vocabulary learning systems, such as the electronic dictionary and corpus-based software, have been developed for learning English vocabulary. With the electronic dictionary, learners can quickly look up an English word. The electronic dictionary not only reduces the learner’s querying time but also provides the same information as non-electronic dictionaries. Beyond the electronic dictionary, corpora can be consulted as they provide more linguistic information than the electronic dictionary, such as how frequently words occur, which words tend to co-occur, and how the language is structured. The research of Cobb and Lee and Liou showed that a corpus-based approach can be used to scaffold the learning of students with low vocabulary skills and improve the results of vocabulary acquisition.
The explicit organizational structure of the thesaurus is, at the local level, sets of synonyms; and at the global level, a hierarchy of concepts. In contrast, the implicit organization at the local level has the characteristics of dictionary sense definitions (genus and differentiae), and at the global level has the characteristics of a small-world social network. The concept of genus and differentiae provides a model that can be seen to account for the distribution of polysemy within senses and across the Thesaurus.
The small-world network model can be seen to account for the incidence of semantic hubs and authorities among cross-references, and conceptual and semantic switching centers among senses and words in the Thesaurus. Previous work on Roget’s Thesaurus calculated chains and equivalence relations algorithmically from senses and words. In that research it was found that there is an inner semantic core of most-densely-connected words and senses. This study expanded on that research identifying the semantic structure of the inner core and relating it to the top most polysemous words in Roget’s.
While the largest thesaurus Categories relate to concrete objects such as plants, animals, food, clothing and technology, the most-connected words (in terms of numbers of senses and synonyms) were found to relate to abstract concepts such as motion, agitation and what appear to be concepts related to survival. This observation was supported by frequency counts, and global cross-reference and word connectivity patterns.
It is important to note that this method of investigation is not thoroughly flawless. First, the study design does not record the differences in student performance with respect to the time he or she has spent with the learning system. It is not known how many learning effects were caused by the proposed system. Secondly, considering the clarification of context sentences retrieved from WordNet has a limit of length and quantity the matching exercises only provide two matching items at a time. Any extra resources, such as corpus, could be used to enrich the context sentences of WordNet. Finally, a larger number and variety of participants is needed to make further generalizations of results. Further research, that includes these and other issues, needs to be conducted to draw more rigorous claims
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