Question: What baseline models are NCEL compared to?
Evidence: We compare NCEL with the following state-of-the-art EL methods including three local models and three types of global models:
Local models: He BIBREF29 and Chisholm BIBREF6 beat many global models by using auto-encoders and web links, respectively, and NTEE BIBREF16 achieves the best performance based on joint embeddings of words and entities.
Iterative model: AIDA BIBREF22 links entities by iteratively finding a dense subgraph.
Loopy Belief Propagation: Globerson BIBREF18 and PBoH BIBREF30 introduce LBP BIBREF31 techniques for collective inference, and Ganea BIBREF24 solves the global training problem via truncated fitting LBP.
PageRank/Random Walk: Boosting BIBREF32 , AGDISTISG BIBREF33 , Babelfy BIBREF34 , WAT BIBREF35 , xLisa BIBREF36 and WNED BIBREF19 performs PageRank BIBREF37 or random walk BIBREF38 on the mention-entity graph and use the convergence score for disambiguation.
For fairly comparison, we report the original scores of the baselines in the papers. Following these methods, we evaluate NCEL on the following five datasets: (1) CoNLL-YAGO BIBREF22 : the CoNLL 2003 shared task including testa of 4791 mentions in 216 documents, and testb of 4485 mentions in 213 documents. (2) TAC2010 BIBREF39 : constructed for the Text Analysis Conference that comprises 676 mentions in 352 documents for testing. (3) ACE2004 BIBREF23 : a subset of ACE2004 co-reference documents including 248 mentions in 35 documents, which is annotated by Amazon Mechanical Turk. (4) AQUAINT BIBREF40 : 50 news articles including 699 mentions from three different news agencies. (5) WW BIBREF19 : a new benchmark with balanced prior distributions of mentions, leading to a hard case of disambiguation. It has 6374 mentions in 310 documents automatically extracted from Wikipedia.
We compare NCEL with the following state-of-the-art EL methods including three local models and three types of global models: 1.Local models: He (He et al., 2013a) and Chisholm (Chisholm and Hachey, 2015) beat many global models by using auto-encoders and web links, respectively, and NTEE (Yamada et al., 2017) achieves the best performance based onjoint embeddings of words and entities. 2.Iterative model: AIDA (Hoffart et al., 2011) links entities by iteratively finding a dense subgraph. 3.Loopy Belief Propagation: Globerson (Globerson et al., 2016) and PBoH (Ganea et al., 2016) introduce LBP (Murphy et al., 1999) techniques for collective inference, and Ganea (Ganea and Hofmann, 2017) solves the global training problem via truncated fitting LBP. 4.PageRank/Random Walk: Boosting (Kulkarni et al., 2009), AGDISTISG (Usbeck et al., 2014), Ba- belfy (Moro et al., 2014), WAT (Piccinno and Ferragina, 2014), xLisa (Zhang and Rettinger, 2014) and WNED (Guo and Barbosa, 2017) performs PageRank (Page et al., 1999) or random walk (Tong et al., 2006) on the mention-entity graph and use the convergence score for disambiguation.
Answer: He (He et al., 2013a) and Chisholm (Chisholm and Hachey, 2015) , AIDA (Hoffart et al., 2011), Globerson (Globerson et al., 2016) and PBoH (Ganea et al., 2016), (Murphy et al., 1999), and Ganea (Ganea and Hofmann, 2017) ,.Boosting (Kulkarni et al., 2009), AGDISTISG (Usbeck et al., 2014), Ba- belfy (Moro et al., 2014), WAT (Piccinno and Ferragina, 2014), xLisa (Zhang and Rettinger, 2014) and WNED (Guo and Barbosa, 2017)