Amila Silva


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On Learning Word Embeddings From Linguistically Augmented Text Corpora
Amila Silva | Chathurika Amarathunga
Proceedings of the 13th International Conference on Computational Semantics - Short Papers

Word embedding is a technique in Natural Language Processing (NLP) to map words into vector space representations. Since it has boosted the performance of many NLP downstream tasks, the task of learning word embeddings has been addressing significantly. Nevertheless, most of the underlying word embedding methods such as word2vec and GloVe fail to produce high-quality embeddings if the text corpus is small and sparse. This paper proposes a method to generate effective word embeddings from limited data. Through experiments, we show that our proposed model outperforms existing works for the classical word similarity task and for a domain-specific application.


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SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using Natural Language Processing (SecureNLP)
Peter Phandi | Amila Silva | Wei Lu
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper describes the SemEval 2018 shared task on semantic extraction from cybersecurity reports, which is introduced for the first time as a shared task on SemEval. This task comprises four SubTasks done incrementally to predict the characteristics of a specific malware using cybersecurity reports. To the best of our knowledge, we introduce the world’s largest publicly available dataset of annotated malware reports in this task. This task received in total 18 submissions from 9 participating teams.