Hybrid Models for Chinese Named Entity Recognition
نویسندگان
چکیده
This paper describes a hybrid model and the corresponding algorithm combining support vector machines (SVMs) with statistical methods to improve the performance of SVMs for the task of Chinese Named Entity Recognition (NER). In this algorithm, a threshold of the distance from the test sample to the hyperplane of SVMs in feature space is used to separate SVMs region and statistical method region. If the distance is greater than the given threshold, the test sample is classified using SVMs; otherwise, the statistical model is used. By integrating the advantages of two methods, the hybrid model achieves 93.18% F-measure for Chinese person names and 91.49% Fmeasure for Chinese location names.
منابع مشابه
A Novel Approach to Conditional Random Field-based Named Entity Recognition using Persian Specific Features
Named Entity Recognition is an information extraction technique that identifies name entities in a text. Three popular methods have been conventionally used namely: rule-based, machine-learning-based and hybrid of them to extract named entities from a text. Machine-learning-based methods have good performance in the Persian language if they are trained with good features. To get good performanc...
متن کاملبهبود شناسایی موجودیتهای نامدار فارسی با استفاده از کسره اضافه
Named entity recognition is a process in which the people’s names, name of places (cities, countries, seas, etc.) and organizations (public and private companies, international institutions, etc.), date, currency and percentages in a text are identified. Named entity recognition plays an important role in many NLP tasks such as semantic role labeling, question answering, summarization, machine ...
متن کاملChinese Named Entity Recognition with Cascaded Hybrid Model
We propose a high-performance cascaded hybrid model for Chinese NER. Firstly, we use Boosting, a standard and theoretically wellfounded machine learning method to combine a set of weak classifiers together into a base system. Secondly, we introduce various types of heuristic human knowledge into Markov Logic Networks (MLNs), an effective combination of first-order logic and probabilistic graphi...
متن کاملChinese Named Entity Recognition with Multiple Features
This paper proposes a hybrid Chinese named entity recognition model based on multiple features. It differentiates from most of the previous approaches mainly as follows. Firstly, the proposed Hybrid Model integrates coarse particle feature (POS Model) with fine particle feature (Word Model), so that it can overcome the disadvantages of each other. Secondly, in order to reduce the searching spac...
متن کاملChinese Named Entity Recognition Based on Hierarchical Hybrid Model
Chinese named entity recognition is a challenging, difficult, yet important task in natural language processing. This paper presents a novel approach based on a hierarchical hybrid model to recognize Chinese named entities. Three mutually dependent stages-boosting, Markov Logic Networks (MLNs) based recognition, and abbreviation detection are integrated in the model. AdaBoost algorithm is utili...
متن کامل