Boosting for Chinese Named Entity Recognition

Chinese 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...

Chinese 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...

Boosting for Named Entity Recognition

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...

بهبود 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 ...