Collective Entity Disambiguation with Structured Gradient Tree Boosting

By Yi Yang, Ozan Irsoy, Kazi Shefaet Rahman. 2018

41 citations recorded by the source corpus.

We present a gradient-tree-boosting-based structured learning model for jointly disambiguating named entities in a document. Gradient tree boosting is a widely used machine learning algorithm that underlies many top-performing natural language processing systems. Surprisingly, most works limit the use of gradient tree boosting as a tool for regular classification or regression problems, despite the structured nature of language. To the best of our knowledge, our work is the first one that employs the structured gradient tree boosting (SGTB) algorithm for collective entity disambiguation. By defining global features over previous disambiguation decisions and jointly modeling them with local features, our system is able to produce globally optimized entity assignments for mentions in a document. Exact inference is prohibitively expensive for our globally normalized model. To solve this problem, we propose Bidirectional Beam Search with Gold path (BiBSG), an approximate inference algorithm that is a variant of the standard beam search algorithm. BiBSG makes use of global information from both past and future to perform better local search. Experiments on standard benchmark datasets show that SGTB significantly improves upon published results. Specifically, SGTB outperforms the previous state-of-the-art neural system by near 1% absolute accuracy on the popular AIDA-CoNLL dataset.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing, Information Extraction

How researchers see this paper

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

In Artificial Intelligence

1. André F. T. Martins
2. Xiang Ren
3. Octavian-Eugen Ganea
4. Pedro Henrique Martins
5. Zita Marinho
6. Nikolaos Kolitsas
7. Yueting Zhuang
8. Thomas Hofmann

In Information Systems

1. Xiang Ren
2. Nikolaos Kolitsas
3. Thomas Hofmann
4. Fei Wu
5. Octavian-Eugen Ganea
6. Guoping Hu
7. Michael R. Lyu
8. Hao Ma

In Natural Language Processing

1. André F. T. Martins
2. Pedro Henrique Martins
3. Zita Marinho
4. Nikolaos Kolitsas
5. Octavian-Eugen Ganea
6. Thomas Hofmann
7. Xiang Ren
8. Zhigang Chen

In Information Extraction

1. Nikolaos Kolitsas
2. Thomas Hofmann
3. André F. T. Martins
4. Octavian-Eugen Ganea
5. Thorsten Joachims

Citations in the corpus

• Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models (2020, unknown): “(2018) [14] 93.”
• Joint Learning of Named Entity Recognition and Entity Linking (2019, background): “…interest in attempting to improve EL performance by leveraging knowledge base information (Radhakrishnan et al., 2018) or by allying local and global features, using information about the neighbouring mentions and respective entities (Le and Titov, 2018; Cao et al., 2018; Yang et al., 2018).”
• End-to-End Neural Entity Linking (2018, methodology): “In the context of ED, recent neural methods (He et al., 2013; Sun et al., 2015; Yamada et al., 2016; Ganea and Hofmann, 2017; Le and Titov, 2018; Yang et al., 2018; Radhakrishnan et al., 2018) have established state-of-the-art results, outperforming engineered features based models.”
• Learning Dynamic Context Augmentation for Global Entity Linking (2019, methodology): “The key idea is to maximize a global coherence/similarity score between all linked entities (Hoffart et al., 2011; Ratinov et al., 2011; Cheng and Roth, 2013; Nguyen et al., 2014; Alhelbawy and Gaizauskas, 2014; Pershina et al., 2015; Guo and Barbosa, 2016; Globerson et al., 2016; Ganea and Hofmann,”