No abstract

Named entity recognition (NER) is a fundamental part of other natural language processing tasks such as information retrieval, question answering systems and machine translation. Progress and success have already been achieved in research on the English NER systems. However, the Urdu NER system is still in its infancy due to the complexity and morphological richness of the Urdu language. Existing Urdu NER systems are highly dependent on manual feature engineering and word embedding to capture similarity. Their performance lags if the words are previously unknown or infrequent. The feature-based models suffer from complicated feature engineering and are often highly reliant on external resources. To overcome these limitations in this study, we present several deep neural approaches that automatically learn features from the data and eliminate manual feature engineering. Our extension involved convolutional neural network to extract character-level features and combine them with word embedding to handle out-of-vocabulary words. The study also presents a tweets dataset in Urdu, annotated manually for five named entity classes. The effectiveness of the deep learning approaches is demonstrated on four benchmarks datasets. The proposed method demonstrates notable progress upon current state-of-the-art NER approaches in Urdu. The results show an improvement of 6.26% in the F1 score.

Relation classification is to recognize semantic relation between two given entities mentioned in the given text in Knowledge Graph. Existing models have performed well on the inverse relation classification with large-scale datasets, but their performance drops significantly for few-shot learning. In this paper, we propose a novel method, function words adaptively enhanced attention framework (FAEA+), to capture class-related function words by the designed hybrid attention for fewshot inverse relation classification. Then, an instance-aware prototype network is present to adaptively capture relation information associated with query instances and eliminate intra-class redundancy due to function words introduced. We theoretically prove that the introduction of function words will increase intra-class differences, and the designed instance-aware prototype network is competent for reducing redundancy. Experimental results show that FAEA+ significantly improved over strong baselines on two datasets. Moreover, our model Article Title has a distinct advantage in solving inverse relations, which outperforms state-of-the-art results by 16.82% under a 1-shot setting in FewRel1.0.

No abstract