Personalized Page Rank for Named Entity Disambiguation

Abstract

The task of Named Entity Disambiguation is to map entity mentions in the document to their correct entries in some knowledge base. We present a novel graph-based disambiguation approach based on Personalized PageRank (PPR) that combines local and global evidence for disambiguation and effectively filters out noise introduced by incorrect candidates. Experiments show that our method outperforms state-of-the-art approaches by achieving 91.7% in micro-and 89.9% in macroaccuracy on a dataset of 27.8K named entity mentions.

Alleviating Poor Context with Background Knowledge for Named Entity Disambiguation

“…In principle, our proposal for enriching context should improve the results of their system. Pershina et al (2015) propose a system closely resembling (Alhelbawy and Gaizauskas, 2014). They report the best known results on CONNL 2003 so far, but unfortunately, their results are not directly comparable to the rest of the state-of-theart, as they artificially insert the gold standard entity in the candidate list.…”

“…Full weighted 88.32 83.46 83.61 80.69 (Lazic et al, 2015) 86.40 -(Alhelbawy & Gaizauskas,14) *87.60 - (Chisholm and Hachey, 2015) 88.70 - (Pershina et al, 2015) *91.77 -TAC14 best (Ji et al, 2014) -82.70 …”

Named Entity Disambiguation for Noisy Text

“…coreference and entity relatedness) that are unavailable to local methods, and have significantly outperformed the local approach on standard datasets (Guo and Barbosa, 2014;Pershina et al, 2015;Globerson et al, 2016). However, global approaches are difficult to apply in domains where only short and noisy text is available, as often occurs in social media, questions and answers, and other short web documents.…”

“…These datasets are based on news corpora and Wikipedia, which are naturally coherent, well-structured, and rich in context. Global disambiguation models (Guo and Barbosa, 2014;Pershina et al, 2015;Globerson et al, 2016) leverage this coherency by jointly disambiguating all the mentions in a single document. However, domains such as webpage fragments, social media, or search queries, are often short, noisy, and less coherent; such domains lack the necessary contextual information for global methods to pay off, and present a more challenging setting in general.…”

Trust, but Verify! Better Entity Linking through Automatic Verification

“…Global coherence has been successfully employed for EL in a number of seminal works (Kulkarni et al, 2009;Hoffart et al, 2011b;Han et al, 2011), and more recently by Moro et al (2014), Pershina et al (2015), andGloberson et al (2016), among others. These approaches maximize global coherence based on a general notion of semantic relatedness, while considering a fixed number of candidate entities for each mentions.…”