Sense, Structure, and Sentence Comprehension

Hare, Mary; Tabaczynski, Tracy; Masamune, Ken; Elman, Jeffrey L.

doi:10.1037/e527342012-123

Search citation statements

Order By: Relevance

Paper Sections

Select...

Noun (Agent) -Noun (Location)1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2015

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nouns also activate expectations about other nouns occurring as co-arguments in the same sentence that denotes the same event (key -door) (Hare et al, 2009). Pado and Lapata (2007) conducted an experiment by collecting human judgments of similarity for noun-noun combinations using a rating scale.…”

Section: Noun (Agent) -Noun (Location)mentioning

confidence: 99%

Korean Semantic Similarity Measures for the Vector Space Models

Lee¹,

Young-In²,

Hyun-jung³

et al. 2015

Phonetics and Speech Sciences

View full text Add to dashboard Cite

As well known in the literature (Budanitsky & Hirst, 2006), semantic similarity has attracted a great deal of interest in natural language processing in recent years. A number of approaches have been implemented, involving word sense disambiguation, text structure, and automatic corrections of word errors in text use measures of relatedness and distance. One of the most widely used machine learning approaches is the semantic vector space model. Based on a huge size of corpora, the models represent the degree of semantic similarity or relatedness of two target words by using distance in the vector space as a measure for semantic similarity to evaluate. For

show abstract

Section: Noun (Agent) -Noun (Location)mentioning

confidence: 99%