Piggyback: Using Search Engines for Robust Cross-Domain Named Entity Recognition

40 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations (2018, methodology): “This coarsening of the WordNet sense inventory has been shown particularly useful in downstream applications (R¨ud et al., 2011; Severyn et al., 2013; Flekova and Gurevych, 2016; Pilehvar et al., 2017).”
Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization (2016, background): “Usage of supersense labels has been shown to improve dependency parsing (Agirre et al., 2011), named entity recognition (Marrero et al., 2009; R¨ud et al., 2011), non-factoid question answering (Surdeanu et al., 2011), question generation (Heilman, 2011), semantic role labeling (Laparra and Rigau,…”
WiC: 10, 000 Example Pairs for Evaluating Context-Sensitive Representations (2018, background): “been shown particularly useful in downstream applications (Rüd et al., 2011; Severyn et al., 2013; Flekova and Gurevych, 2016; Pilehvar et al., 2017).”
Towards a Seamless Integration of Word Senses into Downstream NLP Applications (2017, background): “Various applications have been shown to improve upon moving from senses to supersenses (Rüd et al., 2011; Severyn et al., 2013; Flekova and Gurevych, 2016).”
Fine-grained location extraction from tweets with temporal awareness (2014, background): “Lexical features are widely used in NER tasks and proven to be effective [22, 24, 31].”