کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
536062 870444 2010 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A composite kernel for named entity recognition
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
A composite kernel for named entity recognition
چکیده انگلیسی

In this paper, we propose a novel kernel function for support vector machines (SVM) that can be used for sequential labeling tasks like named entity recognition (NER). Machine learning methods like support vector machines, maximum entropy, hidden Markov model and conditional random fields are the most widely used methods for implementing NER systems. The features used in machine learning algorithms for NER are mostly string based features. The proposed kernel is based on calculating a novel distance function between the string based features. In tasks like NER, the similarity between the contexts as well as the semantic similarity between the words play an important role. The goal is to capture the context and semantic information in NER like tasks. The proposed distance function makes use of certain statistics primarily derived from the training data and hierarchical clustering information. The kernel function is applied to the Hindi and biomedical NER tasks and the results are quite promising.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 31, Issue 12, 1 September 2010, Pages 1591–1597
نویسندگان
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