Learning Semantic Textual Similarity with Structural Representations

By Aliaksei Severyn, Massimo Nicosia, Alessandro Moschitti. 2013

39 citations recorded by the source corpus.

Subfields: Natural Language Processing, Representation & Self-Supervised Learning

How researchers see this paper

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

In Natural Language Processing

1. José Camacho-Collados
2. Mohammad Taher Pilehvar
3. Roberto Navigli
4. Nigel Collier
5. Aliaksei Severyn
6. Iryna Gurevych
7. Lucie Flekova
8. Hinrich Schütze

In Representation & Self-Supervised Learning

1. José Camacho-Collados
2. Mohammad Taher Pilehvar
3. Nigel Collier
4. Roberto Navigli
5. Iryna Gurevych
6. Lucie Flekova
7. Aliaksei Severyn
8. Jonas Pfeiffer

Citations in the corpus

• 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).”
• Modelling input texts: from Tree Kernels to Deep Learning (2015, methodology): “92 5.1.1 Semantic Textual Similarity . . . . . . . . . . . . . . . . . . . . .”
• 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).”
• 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).”
• Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization (2016, background): “…et al., 2011), non-factoid question answering (Surdeanu et al., 2011), question generation (Heilman, 2011), semantic role labeling (Laparra and Rigau, 2013), personality profiling (Flekova and Gurevych, 2015), semantic similarity (Severyn et al., 2013) and metaphor detection (Tsvetkov et al., 2013).”