Open Information Extraction Using Wikipedia

By Fei Wu, Daniel S. Weld. 2010

703 citations recorded by the source corpus.

Subfields: Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

Learning Word Vectors for 157 Languages (2018, methodology): “It has been applied to many different tasks, such as information extraction (Wu and Weld, 2010), or word sense disambiguation (Mihalcea, 2007).”
Compositional Semantic Parsing on Semi-Structured Tables (2015, background): “In parallel, open information extraction (Wu and Weld, 2010; Masaum et al., 2012) and knowledge base population (Ji and Grishman, 2011) extract information from web pages and compile them into structured data.”
Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction (2013, methodology): “It was also used to train open extractors based on Wikipedia infoboxes and corresponding sentences (Wu and Weld, 2007; Wu and Weld, 2010).”
Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths (2015, background): “, information extraction (Wu and Weld, 2010), question answering (Yao and Van Durme, 2014), medical informatics (Wang and Fan, 2014), ontology learning (Xu et al.”
Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning (2016, methodology): “Open Information Extraction Existing work in open IE has used external sources from the web to improve extraction accuracy and coverage (Agichtein and Gravano, 2000; Etzioni et al., 2011; Fader et al., 2011; Wu and Weld, 2010).”
A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages (2020, methodology): “This is why Wikipedia has been extensively used in various NLP applications (Wu and Weld, 2010; Mihalcea, 2007; Al-Rfou et al., 2013; Bojanowski et al., 2017).”
Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification (2016, background): “Relation classification is the task of finding semantic relations between pairs of nominals, which is useful for many NLP applications, such as information extraction (Wu and Weld, 2010), question answering (Yao and Van Durme, 2014).”
Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing (2012, methodology): “Automatic information extraction is the topic of many models and demos (Snow et al., 2006; Yates et al., 2007; Wu and Weld, 2010; Suchanek et al., 2008) but none of them relies on a joint embedding model.”
Weakly Supervised Training of Semantic Parsers (2012, background): “Recent research has produced several weakly supervised relation extractors (Craven and Kumlien, 1999; Mintz et al., 2009; Wu and Weld, 2010; Riedel et al., 2010; Hoffmann et al., 2011).”
Continuous and Interactive Factual Knowledge Learning in Veriﬁcation Dialogues (2020, methodology): “The main existing approach to solving OKBC is to extract facts from documents and add them to the KB [35, 21, 3, 54, 14, 45, 44].”
Open Language Learning for Information Extraction (2012, background): “, 2011), which uses shallow syntactic processing to identify relation phrases that begin with a verb and occur between the argument phrases;2 (2) WOEparse (Wu and Weld, 2010), which uses bootstrapping from entries in Wikipedia info-boxes to learn extraction patterns in dependency parses.”
Open Information Extraction: The Second Generation (2011, methodology): “Pre-emptive Information Extraction and Open Information Extraction are the first paradigms that relax the restriction of a given vocabulary of relations and scale to all relation phrases expressed in text [Shinyama and Sekine, 2006; Banko et al., 2007; Banko and Etzioni, 2008; Wu and Weld, 2010].”
Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference (2012, background): “In StatSnowball, Zhu et al. (2009) demonstrate high quality results of an MLN-based approach.”