Context-Sensitive Recognition for Emerging and Rare Entities
نویسندگان
چکیده
We present a novel named entity recognition (NER) system, and its participation in the Emerging and Rare Entity Recognition shared task, hosted at the 2017 EMNLP Workshop on Noisy User Generated Text (W-NUT). With a specialized evaluation highlighting performance on rare, and sparsely-occurring named entities, this task provided an excellent opportunity to build out a newly-developed statistical algorithm and benchmark it against the state-of-the-art. Powered by flexible context features of word forms, our system’s capacity for identifying neverbefore-seen entities made it well suited for the task. Since the system was only developed to recognize a limited number of named entity types, its performance was lower overall. However, performance was competitive on the categories trained, indicating potential for future development.
منابع مشابه
Named Entity Recognition in Persian Text using Deep Learning
Named entities recognition is a fundamental task in the field of natural language processing. It is also known as a subset of information extraction. The process of recognizing named entities aims at finding proper nouns in the text and classifying them into predetermined classes such as names of people, organizations, and places. In this paper, we propose a named entity recognizer which benefi...
متن کاملResults of the WNUT2017 Shared Task on Novel and Emerging Entity Recognition
This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization), but recall on them is a real problem in noisy text even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet “so...
متن کاملCAMAC: a context-aware mandatory access control model
Mandatory access control models have traditionally been employed as a robust security mechanism in multilevel security environments such as military domains. In traditional mandatory models, the security classes associated with entities are context-insensitive. However, context-sensitivity of security classes and flexibility of access control mechanisms may be required especially in pervasive c...
متن کاملImprovement of Chemical Named Entity Recognition through Sentence-based Random Under-sampling and Classifier Combination
Chemical Named Entity Recognition (NER) is the basic step for consequent information extraction tasks such as named entity resolution, drug-drug interaction discovery, extraction of the names of the molecules and their properties. Improvement in the performance of such systems may affects the quality of the subsequent tasks. Chemical text from which data for named entity recognition is extracte...
متن کاملA 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 کامل