Scaling Web-based Acquisition of Entailment Relations

By Idan Szpektor, Hristo Tanev, Ido Dagan, Bonaventura Coppola. 2004

211 citations recorded by the source corpus.

Subfields: 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 Natural Language Processing

In Information Extraction

Citations in the corpus

Knowledge-Based Textual Inference via Parse-Tree Transformations (2015, unknown): “…comparable corpora (Barzilay & McKeown , 2001; Barzilay & Lee, 2003; Pang, Knight, & Marcu, 2003), by querying the Web (Ravichandran & Hovy, 2002; Szpektor et al., 2004), or from a local corpus (Lin & Pantel, 2001; Glickman & Dagan, 2003; Bhagat & Ravichandran, 2008; Szpektor & Dagan, 2008;…”
A Survey of Paraphrasing and Textual Entailment Methods (2009, methodology): “Although TEASE can also be used as a generator, if particular input templates are provided, we discuss it further in Section 4.2, along with other bootstrapping extraction methods, since in its full form it requires no initial templates (nor seed slot values).”
Espresso: Leveraging Generic Patterns for Automatically Harvesting Semantic Relations (2006, background): “Many recent efforts have also focused on extracting semantic relations between entities, such as entailments (Szpektor et al. 2004), is-a (Ravi-chandran and Hovy 2002), part-of (Girju et al. 2006), and other relations.”
Efficient Search for Transformation-based Inference (2012, unknown): “Typical examples for knowledge resources of such rules are DIRT (Lin and Pantel, 2001), and TEASE (Szpek-tor et al., 2004), as well as syntactic transformations constructed manually.”
The PASCAL Recognising Textual Entailment Challenge (2005, methodology): “htm – The output of the TEASE system for extracting entailment relations and paraphrases ([14]).”
Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations (2016, background): “, 2014), question answering (Ravichandran and Hovy, 2002) and textual entailment (Szpektor et al., 2004).”
A Probabilistic Classification Approach for Lexical Textual Entailment (2005, background): “Recent works on paraphrase and entailment rule acquisition (Barzilay and McKeown 2001; Lin and Pantel 2001; Szpektor et al. 2004) learn lexical syntactic paraphrase patterns.”
Learning Entailment Relations by Global Graph Structure Optimization (2012, methodology): “However, some distributional similarity algorithms measure similarity between binary templates directly (Lin and Pantel 2001; Szpektor et al. 2004; Bhagat, Pantel, and Hovy 2007; Yates and Etzioni 2009), whereas others decompose binary templates into two unary templates, estimate similarity between…”
Learning Entailment Rules for Unary Templates (2008, background): “This sparked intensive research on unsupervised acquisition of entailment rules (and similarly paraphrases) e.g. (Lin and Pantel, 2001; Szpektor et al., 2004; Sekine, 2005).”
The Third PASCAL Recognizing Textual Entailment Challenge (2007, methodology): “htm – The output of the TEASE system for extracting entailment relations and paraphrases ([13]).”
Relation Extraction with Matrix Factorization and Universal Schemas (2013, background): “This is similar in spirit to work on learning entailment rules (Szpektor et al., 2004; Zanzotto et al., 2006; Szpektor and Dagan, 2008).”