Investigating a Generic Paraphrase-Based Approach for Relation Extraction

By Lorenza Romano, Milen Kouylekov, Idan Szpektor, Ido Dagan, Alberto Lavelli. 2006

120 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

How Can We Know What Language Models Know? (2019, methodology): “Although many methods could be used for paraphrasing (Romano et al., 2006; Bhagat and Ravichandran, 2008), we follow the simple”
Stress Test Evaluation for Natural Language Inference (2018, methodology): “The NLI task attracted significant interest before datasets became large enough for the application of neural methods (Glickman et al., 2005; Harabagiu and Hickl, 2006; Romano et al., 2006; Dagan et al., 2006; Giampiccolo et al., 2007; Dagan et al., 2010; MacCartney, 2009; Zanzotto et al., 2006; Mal”
Semantic Parsing via Paraphrasing (2014, background): “While it has been shown that paraphrasing meth-ods are useful for question answering (Harabagiu and Hickl, 2006) and relation extraction (Romano et al., 2006), this is, to the best of our knowledge, the ﬁrst paper to perform semantic parsing through paraphrasing.”
EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference (2019, background): “…test for natural language understanding (Cooper et al., 1996; Fyodorov; Glickman et al., 2005; Haghighi et al., 2005; Harabagiu and Hickl, 2006; Romano et al., 2006; Dagan et al., 2006; Giampiccolo et al., 2007; Zanzotto et al., 2006; Malakasiotis and Androutsopoulos, 2007; MacCartney, 2009;…”
Modeling Semantic Containment and Exclusion in Natural Language Inference (2008, background): “lexical or semantic overlap (Jijkoun and de Rijke, 2005), pattern-based relation extraction (Romano et al., 2006), or approximate matching of predicate-argument structure (Hickl et al.”
Generating Phrasal and Sentential Paraphrases: A Survey of Data-Driven Methods (2010, methodology): “Finally, paraphrases have also been used to improve the task of relation extraction (Romano et al. 2006).”
Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance? (2020, methodology): “ct number. PT is model trained on data partitioned by linguistic property. DS is models trained on downsampled data from MultiNLI to match the number of instances in PT. 2005;Harabagiu and Hickl,2006;Romano et al., 2006;Dagan et al.,2006;Giampiccolo et al.,2007; Zanzotto et al.,2006;Malakasiotis and”
Leveraging Linguistic Structure For Open Domain Information Extraction (2015, background): “Our approach of ﬁnding short entailments from a longer utterance is similar in spirit to work on textual entailment for information extraction (Romano et al., 2006).”
Design and realization of a modular architecture for textual entailment (2013, background): “In information extraction, instance candidates can be compared against templates (Romano et al. 2006).”
Natural Logic for Textual Inference (2007, methodology): “Up to now, the most successful approaches have used fairly impoverished semantic representations, relying on measures of lexical or semantic overlap (Jijkoun and de Rijke, 2005), pattern-based relation extraction (Romano et al., 2006), or approximate matching of predicate-argument structure (Hickl e”
Learning Entailment Rules for Unary Templates (2008, methodology): “Entail-ment rules capture linguistic and world-knowledge inferences and are used as an important building block within different applications, e.g. (Romano et al., 2006).”
Large Scale Acquisition of Paraphrases for Learning Surface Patterns (2008, methodology): “Romano et al. (2006) and Sekine (2006) used syntactic paraphrases to obtain patterns for extracting relations.”