Meta-Learning Orthographic and Contextual Models for Language Independent Named Entity Recognition
نویسندگان
چکیده
This paper presents a named entity classification system that utilises both orthographic and contextual information. The random subspace method was employed to generate and refine attribute models. Supervised and unsupervised learning techniques used in the recombination of models to produce the final results.
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 کاملVoted NER System using Appropriate Unlabeled Data
This paper reports a voted Named Entity Recognition (NER) system with the use of appropriate unlabeled data. The proposed method is based on the classifiers such as Maximum Entropy (ME), Conditional Random Field (CRF) and Support Vector Machine (SVM) and has been tested for Bengali. The system makes use of the language independent features in the form of different contextual and orthographic wo...
متن کاملNamed Entity Recognition for Manipuri Using Support Vector Machine
This paper reports about the development of a Manipuri NER system, a less computerized Indian language. Two different models, one using an active learning technique based on the context patterns generated from an unlabeled news corpus and the other based on the well known Support Vector Machine (SVM), have been developed. The active learning technique has been considered as the baseline system....
متن کاملLanguage Independent NER using a Unified Model of Internal and Contextual Evidence
This paper investigates the use of a language independent model for named entity recognition based on iterative learning in a co-training fashion, using word-internal and contextual information as independent evidence sources. Its bootstrapping process begins with only seed entities and seed contexts extracted from the provided annotated corpus. F-measure exceeds 77 in Spanish and 72 in Dutch.
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 کامل