Dependency-Based Construction of Semantic Space Models

By Sebastian Padó, Mirella Lapata. 2007

715 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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

In Natural Language Processing

Citations in the corpus

Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank (2013, background): “Variants of this idea use more complex frequencies such as how often a word appears in a certain syntactic context (Pado and Lapata, 2007; Erk and Padó, 2008).”
From Frequency to Meaning: Vector Space Models of Semantics (2010, background): “…derived from the occurrences of the word in various contexts, such as windows of words (Lund & Burgess, 1996), grammatical dependencies (Lin, 1998; Padó & Lapata, 2007), and richer contexts consisting of dependency links and selectional preferences on the argument positions (Erk & Padó, 2008);…”
Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors (2014, methodology): “…of alternative count DSMs on standard benchmarks (Agirre et al., 2009; Baroni and Lenci, 2010; Bullinaria and Levy, 2007; Bullinaria and Levy, 2012; Sahlgren, 2006; Padó and Lapata, 2007), the existing literature contains very little in terms of direct comparison of count vs. predictive DSMs.”
Multimodal Distributional Semantics (2013, methodology): “…(such as documents or the occurrence of another word inside a fixed window from the target word) to more linguistically sophisticated ones (such as the occurrence of certain words connected to the target by special syntactic relations) (Padó & Lapata, 2007; Sahlgren, 2006; Turney & Pantel, 2010).”
Dependency-Based Word Embeddings (2014, background): “Specifically, 1code.google.com/p/word2vec/ 302 following work in sparse vector-space models (Lin, 1998; Padó and Lapata, 2007; Baroni and Lenci, 2010), we experiment with syntactic contexts that are derived from automatically produced dependency parse-trees.”
Linguistic Regularities in Sparse and Explicit Word Representations (2014, background): “Another popular choice of context is the syntactic relations the word participates in (Lin, 1998; Padó and Lapata, 2007; Levy and Goldberg, 2014).”
A Structured Vector Space Model for Word Meaning in Context (2008, background): “This corresponds to a beneficial effect of syntactic information found for other applications of semantic spaces (Lin, 1998; Padó and Lapata, 2007).”
Vector Space Models of Word Meaning and Phrase Meaning: A Survey (2012, background): “…learning (Hindle 1990; Lin 1998; Ravichandran et al. 2005; Gorman and Curran 2006; Snow et al. 2006), determining predominant word sense (McCarthy et al. 2004, Padó and Lapata 2007), predicting similarity of semantic relations (Turney 2006), and learning inference rules (Lin and Pantel 2001).”
Compositional Matrix-Space Models of Language (2010, background): “…VSMs proved to perform well in a number of tasks requiring computation of semantic relatedness between words, such as synonymy identiﬁcation (Landauer and Dumais, 1997), automatic thesaurus construction (Grefenstette, 1994), semantic priming, and word sense disambiguation (Padó and Lapata, 2007).”
Compositional Operators in Distributional Semantics (2014, methodology): “In fact, the comparative study of Blacoe and Lapata (2012) suggests something really surprising: that, for certain tasks, additive and multiplicative models can be almost as much effective as state-of-the-art deep learning models, which will be the subject of Section 4.4.”