| 英文摘要 | Scoring essays by human in a large-scale language test is often thought to be a headache. On one hand, it consumes a lot of manpower and material resources, on the other hand human often grade essays subjectively. But the Automatic Essay Scoring (AES) system doesn’t have these problems, it can assess essays rapidly, objectively and consistently, so it has brought more and more attention to the field of computer science and education. As a leader in the AES system research area, the American scientists developed several commercial systems in the 1990th, and some of them are used in the well-known GMAT and TOEFL test to score the essays. In recent years, Japan, Korea and Taiwan etc. also increased investment in this research area, and developed some practical or experimental AES systems. However, as so far, most of the available AES systems are designed to score the essays which are written by native speakers (although TOEFL is a test to assess foreign English learners, but the examinees’ English level is higher than normal people’s), and few of the systems are used to assess essays which are written by non-native learners. Some statistics have showed that while using an AES system which is originally designed to assess native speakers to assess non-native learners, it often gets low performance. This is because when a native speaker writes an essay, he/she can write the sentence fluently, but when a non-native learner writes an essay, he/she always makes some grammatical errors in sentences or uses wrong words, which makes the AES system can’t extract features correctly and score precisely. So, the key to solve the problem of automatically score non-native learners’ essays is to analyze these essays’ sentences in-depth. So this paper mainly studies how to analyze the quality of the sentences which are written by low level Chinese learners. As the learners of this level haven’t mastered large numbers of style of writing yet, it’s possible to construct a sentence-dictionary by clustering similar sentences into several groups, after that, experts can score every group of sentences to distinguish good sentences from bad ones. The main work of this paper is listed below: Cluster sentences into groups by comparing each pair of sentences’ semantic similarity, and implement four algorithms to calculate the semantic similarity of two sentences. It shows that using different similarity algorithm in the clustering algorithm, the final clustering ... |
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