| 查看: 228 | 回复: 0 | |||
| 当前主题已经存档。 | |||
[交流]
俄中日三国作者冠词用错的比较研究
|
|||
|
One of the most difficult challenges faced by non-native speakers of English is mastering the system of English articles. We trained a maximum entropy classifier to select among a/an, the, or zero article for noun phrases (NPs), based on a set of features extracted from the local context of each. When the classifier was trained on 6 million NPs, its performance on published text was about 83% correct. We then used the classifier to detect article errors in the TOEFL essays of native speakers of Chinese, Japanese, and Russian. These writers made such errors in about one out of every eight NPs, or almost once in every three sentences. The classifier's agreement with human annotators was 85% (kappa = 0.48) when it selected among a/an, the, or zero article. Agreement was 89% (kappa = 0.56) when it made a binary (yes/no) decision about whether the NP should have an article. Even with these levels of overall agreement, precision and recall in error detection were only 0.52 and 0.80, respectively. However, when the classifier was allowed to skip cases where its confidence was low, precision rose to 0.90, with 0.40 recall. Additional improvements in performance may require features that reflect general knowledge to handle phenomena such as indirect prior reference. In August 2005, the classifier was deployed as a component of Educational Testing Service's CriterionSM Online Writing Evaluation Service. Proportion of text units containing one or more article errors for three language groups [ Last edited by lby1258 on 2010-6-5 at 08:11 ] |
回复此楼» 猜你喜欢
今年的WR进展到哪一步了？
已经有8人回复
-
时间戳他又来了
已经有17人回复
-
系统今天又提示维护了，估计离放榜不远了
已经有9人回复
-
产物和副产物价值比较
已经有4人回复
-
这个自发加氧反应的机理是什么？
已经有7人回复
-
J-J-W不为人知的一面
已经有19人回复
-
没消息就是被刷了呗
已经有14人回复
-
微信指数没变化，科研之友没阅读
已经有8人回复
-
这种情况还有戏吗
已经有11人回复
-
同事接到电话了，我却没有
已经有4人回复