Entity Disambiguation with Web Links

By Andrew Chisholm, Ben Hachey. 2015

84 citations recorded by the source corpus.

Entity disambiguation with Wikipedia relies on structured information from redirect pages, article text, inter-article links, and categories. We explore whether web links can replace a curated encyclopaedia, obtaining entity prior, name, context, and coherence models from a corpus of web pages with links to Wikipedia. Experiments compare web link models to Wikipedia models on well-known conll and tac data sets. Results show that using 34 million web links approaches Wikipedia performance. Combining web link and Wikipedia models produces the best-known disambiguation accuracy of 88.7 on standard newswire test data.

Subfields: Artificial Intelligence, Information Extraction, Knowledge Graphs & Representation

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Citations in the corpus

Deep Joint Entity Disambiguation with Local Neural Attention (2017, neutral): “…et al., 2016) 87.9 (Yamada et al., 2016) 87.2 our (local, K=100, R=50) 88.8 Global models (Huang et al., 2015) 86.6 (Ganea et al., 2016) 87.6 (Chisholm and Hachey, 2015) 88.7 (Guo and Barbosa, 2016) 89.0 (Globerson et al., 2016) 91.0 (Yamada et al., 2016) 91.5 our (global) 92.22 ± 0.14 ﬁrst…”
Joint Entity Linking with Deep Reinforcement Learning (2019, result): “revious Work Baselines. We compare RLEL with a series of EL systems which report state-of-the-art results on the test datasets. „ere are various methods including classi•cation model [25], rank model [6, 34] and probability graph model [14, 16, 20, 21, 37]. Except that, Cheng et al:[5] formulate the”
Boosting Entity Linking Performance by Leveraging Unlabeled Documents (2019, methodology): “We also compare to recent state-of-the-art systems trained supervisedly on Wikipedia and extra supervision or on AIDA CoNLL: Chisholm and Hachey (2015), Guo and Barbosa (2016), Glober-son et al. (2016), Yamada et al. (2016), Ganea and Hofmann (2017), Le and Titov (2018).”
Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation (2016, methodology): “Following past work (He et al., 2013; Chisholm and Hachey, 2015), we used mentions only with a valid entry in the KB, and reported the micro-accuracy score of the top-ranked candidate entities.”
Improving Entity Linking by Modeling Latent Entity Type Information (2020, background): “7.2 Ganea and Hofmann (2017) 88.8 Ganea and Hofmann (2017) (reproduce) 88:75 0:30 BERT-Entity-Sim (local) 90:06 0:22 Local & Global models Huang, Heck, and Ji (2015) 86.6 Ganea et al. (2016) 87.6 Chisholm and Hachey (2015) 88.7 Guo and Barbosa (2016) 89.0 Globerson et al. (2016) 91.0 Yamada et a”
Learning Text Representations for 500K Classification Tasks on Named Entity Disambiguation (2018, methodology): “Among global models, (Chisholm and Hachey, 2015) use a learning to rank algorithm which combines local and global features, trained on in-domain corpora ( Aidatrain and Tac2010 train , respectively).”
Collective Entity Resolution with Multi-Focal Attention (2016, methodology): “The attention model provides a substantial gain over the baseline, and outperforms Chisholm and Hachey (2015) by 2.3% in absolute accuracy.”
Named Entity Disambiguation for Noisy Text (2017, background): “Noisy Data Chisholm and Hachey (2015) showed that despite the noisy nature of web data, augmenting Wikipedia-derived data with web-links from the Wikilinks corpus (Singh et al., 2012) can improve performance on standard datasets.”
Learning Distributed Representations of Texts and Entities from Knowledge Base (2017, methodology): “• Chisholm (Chisholm and Hachey, 2015) used a support vector machine (SVM) with various features derived from KB and a Wikilinks dataset (Singh et al.”
Collective Entity Disambiguation with Structured Gradient Tree Boosting (2018, unknown): “PPRforNED In-KB acc. et al. (2015) 86.4 Huang et al. (2015) 86.6 Chisholm and Hachey (2015) 88.7 Ganea et al. (2016) 87.6 Guo and Barbosa (2016) 89.0 Globerson et al. (2016) 91.0 Yamada et al. (2016) 91.5 Ganea and Hofmann (2017) 92.2”
Design Challenges for Entity Linking (2015, methodology): “We leave a more sophisticated context model for future work (Chisholm and Hachey, 2015; Singh et al., 2012).”
Improving Entity Linking by Modeling Latent Relations between Mentions (2018, background): “The coherence score function, in the simplest form, is a sum over all pairwise scores Φ( e i , e j , D ) (Ratinov et al., 2011; Huang et al., 2015; Chisholm and Hachey, 2015; Ganea et al., 2016; Guo and Barbosa, 2016; Globerson et al., 2016; Yamada et al., 2016), resulting in: A disadvantage of…”
Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation (2019, unknown): “8 (Chisholm and Hachey, 2015) 88.”