In the third shared task of the Computational Approaches to Linguistic Code-Switching
(CALCS) workshop, we focus on Named Entity Recognition (NER) on code-switched social …

Contextual string embeddings are a recent type of contextualized word embedding that were
shown to yield state-of-the-art results when utilized in a range of sequence labeling tasks …

Extracting named entities text forms the basis for many crucial tasks such as information
retrieval and extraction, machine translation, opinion mining, sentiment analysis and …

Named entity recognition (NER) is the basis for many natural language processing (NLP)
tasks such as information extraction and question answering. The accuracy of the NER …

Document types, where visual and textual information plays an important role in their
analysis and understanding, pose a new and attractive area for information extraction …

BiLSTM has been prevalently used as a core module for NER in a sequence-labeling setup.
State-of-the-art approaches use BiLSTM with additional resources such as gazetteers …

Neither traditional rule-based named entity recognition (NER) nor the latest language
models perform well in information extraction from noisy text—the text that contains linguistic …

Traffic accident detection is an important strategy governments can use to implement
policies intended to reduce accidents. They usually use techniques such as image …

While deep learning has shown promise in improving the automated diagnosis of disease
based on chest X-rays, deep networks may exhibit undesirable behavior related to short …

While keyphrase extraction has received considerable attention in recent years, relatively
few studies exist on extracting keyphrases from social media platforms such as Twitter, and …