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Found 4 papers, 1 papers with code
Learning Rich Representation of Keyphrases from Text
In the discriminative setting, we introduce a new pre-training objective - Keyphrase Boundary Infilling with Replacement (KBIR), showing large gains in performance (upto 8. 16 points in F1) over SOTA, when the LM pre-trained using KBIR is fine-tuned for the task of keyphrase extraction.
On the Use of Context for Predicting Citation Worthiness of Sentences in Scholarly Articles
In this paper, we study the importance of context in predicting the citation worthiness of sentences in scholarly articles.
Identifying Named Entities as they are Typed
However, the typical experimental setup for evaluating Named Entity Recognition (NER) systems is not directly applicable to systems that process text in real time as the text is being typed.
A Semi-Markov Structured Support Vector Machine Model for High-Precision Named Entity Recognition
Named entity recognition (NER) is the backbone of many NLP solutions.
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