Exploiting Bi-LSTMs for Named Entity Recognition in Indian Culinary Science

G. Mahalakshmi, Makesh Narsimhan Sreedhar, Ravi Kiran Selvam, S. Sendhilkumar
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Abstract

This paper discusses the use of Bidirectional LSTMs for recognition of Named Entities over the Indian Recipe Blogs. Recipe posts from popular blogs including Hebbar's Kitchen are harvested and trained for recognizing NEs. Both the word embeddings and character embeddings are utilized as feature vectors for training the Bi-LSTM. CRF model is used for joint decoding of the labels. The system shows a development data F1 score of 92.87% and test data F1 score of 94.66%. The dataset used and meta-results obtained are released freely for research use.
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利用Bi-LSTMs在印度烹饪科学中进行命名实体识别
本文讨论了在印度食谱博客上使用双向lstm来识别命名实体。包括Hebbar's Kitchen在内的热门博客上的食谱帖子被收集起来,并经过培训以识别新食品。利用词嵌入和字符嵌入作为特征向量来训练Bi-LSTM。采用CRF模型对标签进行联合解码。系统开发数据F1得分为92.87%,测试数据F1得分为94.66%。使用的数据集和获得的元结果免费发布,供研究使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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