Coreference Resolution with World Knowledge

By Altaf Rahman, Vincent Ng. 2011

135 citations recorded by the source corpus.

Subfields: Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

Toward Gender-Inclusive Coreference Resolution (2019, background): “…which found substantial benefit by using mined mappings between names and professions to help resolve named entities like “Bill Clinton” to nominals like “president” (later examples include that of Rahman and Ng [2011] and Bansal and Klein [2012], who found less benefit from a similar approach).”
Easy Victories and Uphill Battles in Coreference Resolution (2013, methodology): “Acknowledging this complexity, coreference systems, either learning-based (Bengtson and Roth, 2008; Stoyanov et al., 2010; Haghighi and Klein, 2010; Rahman and Ng, 2011b) or rule-based (Haghighi and Klein, 2009; Lee et al., 2011), draw on diverse information sources and complex heuristics to…”
Solving Hard Coreference Problems (2019, background): “A lot of recent work has attempted to utilize similar types of resources to improve coreference resolution (Rahman and Ng, 2011a; Ratinov and Roth, 2012; Bansal and Klein, 2012; Rahman and Ng, 2012).”
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM (2018, unknown): “External knowledge base has been typically used as a source of features (Ratinov and Roth 2009; Rahman and Ng 2011; Nakashole and Mitchell 2015).”
A Joint Model for Entity Analysis: Coreference, Typing, and Linking (2014, methodology): “Entity linking has been employed for coreference resolution (Ponzetto and Strube, 2006; Rahman and Ng, 2011; Ratinov and Roth, 2012) and coreference for entity linking (Cheng and Roth, 2013) as part of pipelined systems.”
A Constrained Latent Variable Model for Coreference Resolution (2013, methodology): “Finally, some research (Ratinov and Roth, 2012; Bansal and Klein, 2012; Rahman and Ng, 2011a) has tried to integrate world knowledge from web-based statistics or knowledge bases into a coreference system.”
Scoring Coreference Partitions of Predicted Mentions: A Reference Implementation (2014, background): “2 In a latter paper, Rahman and Ng (2011) correctly state that “ CEAF can compare partitions with twinless mentions without any modiﬁ-cation.””
Resolving Complex Cases of Definite Pronouns: The Winograd Schema Challenge (2012, methodology): “To understand whether the somewhat unsatisfactory Stanford results can be attributed to its inability to exploit the training data, we employ as our third baseline a mention ranker that is trained in the same way as our system (see Section 3), except that it employs 39 commonlyused linguistic featur”
Coreference Semantics from Web Features (2012, background): “There is also work on end-to-end coreference resolution that uses large noun-similarity lists (Daum´e III and Marcu, 2005) or structured knowledge bases such as Wikipedia (Yang and Su, 2007; Haghighi and Klein, 2009; Kobdani et al., 2011) and YAGO (Rahman and Ng, 2011).”
Leveraging Knowledge Bases in LSTMs for Improving Machine Reading (2017, background): “proaches have exploited the use of these knowledge bases (KBs) in NLP tasks (Ratinov and Roth, 2009; Rahman and Ng, 2011; Nakashole and Mitchell, 2015), they require a lot of task-specific engineering to achieve good performance.”
Dynamic Knowledge-Base Alignment for Coreference Resolution (2013, methodology): “We also want to investigate a cluster ranking model, as used in (Rahman and Ng, 2011; Stoyanov and Eisner, 2012), to aggregate the features of all the coreferent mentions as inference progresses.”
Learning-based Multi-Sieve Co-reference Resolution with Knowledge (2012, methodology): “We also note that (Rahman and Ng, 2011) reports low utility of FrameNet-based features.”
Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle* (2021, background): “…which found substantial beneﬁt by using mined mappings between names and professions to help resolve named entities like “Bill Clinton” to nominals like “president” (later examples include that of Rahman and Ng (2011) and Bansal and Klein (2012), who found less beneﬁt from a similar approach).”