Personalizing PageRank for Word Sense Disambiguation

By Eneko Agirre, Aitor Soroa Etxabe. Conference of the European Chapter of the Association for Computational Linguistics, 2009

646 citations recorded by the source corpus.

In this paper we propose a new graph-based method that uses the knowledge in a LKB (based on WordNet) in order to perform unsupervised Word Sense Disambiguation. Our algorithm uses the full graph of the LKB efficiently, performing better than previous approaches in English all-words datasets. We also show that the algorithm can be easily ported to other languages with good results, with the only requirement of having a wordnet. In addition, we make an analysis of the performance of the algorithm, showing that it is efficient and that it could be tuned to be faster.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/534534e32f5e477eeb3301faa6265c23d221f684

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Citations in the corpus

Entity Linking meets Word Sense Disambiguation: a Unified Approach (2014, methodology): “Other approaches, instead, leverage the structural properties of the knowledge base by exploiting centrality and connectivity measures (Sinha and Mihalcea, 2007; Tsatsaronis et al., 2007; Agirre and Soroa, 2009; Navigli and Lapata, 2010).”
Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison (2017, background): “…an important branch of knowledge-based systems found their techniques on the structural properties of semantic graphs from lexical resources (Agirre and Soroa, 2009; Guo and Diab, 2010; Ponzetto and Navigli, 2010; Agirre et al., 2014; Moro et al., 2014; Weissenborn et al., 2015; Tripodi and…”
Embeddings for Word Sense Disambiguation: An Evaluation Study (2016, methodology): “We also report the results for UKB w2w (Agirre and Soroa, 2009), another knowledge-based WSD approach based on Personalized PageRank (Haveli-wala, 2002).”
FreeLing 3.0: Towards Wider Multilinguality (2012, methodology): “This module is a direct inclusion of the UKB project code (Agirre and Soroa, 2009).”
Graph-based term weighting for information retrieval (2012, methodology): “…dictionary graphs (of semantically-related senses), which are then used for automatic synonym extraction (Blondel et al. 2004; Ho and Fairon 2004; Muller et al. 2006), or word sense disambiguation (Agirre and Soroa 2009; Gaume 2008; Kozima 1993; Mihalcea and Tarau 2004; Véronis and Ide 1990).”
Incorporating Coreference Resolution into Word Sense Disambiguation (2011, background): “Personalizing PageRank approach [9] builds the context of at least 20 content words for each sentence to be disambiguated, taking the sentences immediately before and after it when the original sentence is too short.”
Making Sense of Word Embeddings (2016, methodology): “The La Sapienza system (Agirre and Soroa, 2009), relies on WordNet to get word senses and perform disambiguation.”
BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network (2012, neutral): “also [5] and [89]).”
A Study on Similarity and Relatedness Using Distributional and WordNet-based Approaches (2009, background): “Our choice was mainly motivated by the fact that MCR contains tightly aligned 1 http://http://ixa2.si.ehu.es/ukb/ 2 Available from http://http://wordnet.princeton.edu/ 3 http://wordnet.princeton.edu/glosstag wordnets of several languages (see below).”
Generating Coherent Event Schemas at Scale (2013, background): “This is a standard practice to handle large graphs (Agirre and Soroa, 2009; Mausam et al., 2010).”