6533b7dcfe1ef96bd12728a3
RESEARCH PRODUCT
Arabic Named Entity Recognition: A Feature-Driven Study
Yassine BenajibaPaolo RossoMona Diabsubject
Conditional random fieldAcoustics and UltrasonicsComputer sciencebusiness.industryPrinciple of maximum entropycomputer.software_genreMachine learningLinear discriminant analysisCable televisionSupport vector machineDiscriminative modelNamed-entity recognitionEntropy (information theory)Artificial intelligenceElectrical and Electronic EngineeringbusinesscomputerNatural language processingdescription
The named entity recognition task aims at identifying and classifying named entities within an open-domain text. This task has been garnering significant attention recently as it has been shown to help improve the performance of many natural language processing applications. In this paper, we investigate the impact of using different sets of features in three discriminative machine learning frameworks, namely, support vector machines, maximum entropy and conditional random fields for the task of named entity recognition. Our language of interest is Arabic. We explore lexical, contextual and morphological features and nine data-sets of different genres and annotations. We measure the impact of the different features in isolation and incrementally combine them in order to evaluate the robustness to noise of each approach. We achieve the highest performance using a combination of 15 features in conditional random fields using broadcast news data (Fbeta = 1=83.34).
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2009-07-01 | IEEE Transactions on Audio, Speech, and Language Processing |