About: Semantic space

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Semantic spaces in the natural language domain aim to create representations of natural language that are capable of capturing meaning. The original motivation for semantic spaces stems from two core challenges of natural language: Vocabulary mismatch (the fact that the same meaning can be expressed in many ways) and ambiguity of natural language (the fact that the same term can have several meanings).

Attributes	Values
rdfs:label	Semantic space (en)
rdfs:comment	Semantic spaces in the natural language domain aim to create representations of natural language that are capable of capturing meaning. The original motivation for semantic spaces stems from two core challenges of natural language: Vocabulary mismatch (the fact that the same meaning can be expressed in many ways) and ambiguity of natural language (the fact that the same term can have several meanings). (en)
dct:subject	Semantic relations Semantics Natural language processing
Wikipage page ID	54449666 (xsd:integer)
Wikipage revision ID	1037888802 (xsd:integer)
Link from a Wikipage to another Wikipage	Natural language processing Semantic relations GloVe (machine learning) Google Vocabulary mismatch Machine learning Stanford University Keyword research Semantics Wikipedia Distributional–relational database Latent semantic analysis Ambiguity Facebook FastText Hyperspace Analogue to Language Rule-based system Tensor Natural language processing Accuracy and precision Dimension Explicit semantic analysis Semantic folding Word embedding Neural networks Word2vec
sameAs	Semantic space Semantic space
dbp:wikiPageUsesTemplate	dbt:Reflist
has abstract	Semantic spaces in the natural language domain aim to create representations of natural language that are capable of capturing meaning. The original motivation for semantic spaces stems from two core challenges of natural language: Vocabulary mismatch (the fact that the same meaning can be expressed in many ways) and ambiguity of natural language (the fact that the same term can have several meanings). The application of semantic spaces in natural language processing (NLP) aims at overcoming limitations of rule-based or model-based approaches operating on the keyword level. The main drawback with these approaches is their brittleness, and the large manual effort required to create either rule-based NLP systems or training corpora for model learning. Rule-based and machine learning based models are fixed on the keyword level and break down if the vocabulary differs from that defined in the rules or from the training material used for the statistical models. Research in semantic spaces dates back more than 20 years. In 1996, two papers were published that raised a lot of attention around the general idea of creating semantic spaces: latent semantic analysis and Hyperspace Analogue to Language. However, their adoption was limited by the large computational effort required to construct and use those semantic spaces. A breakthrough with regard to the accuracy of modelling associative relations between words (e.g. "spider-web", "lighter-cigarette", as opposed to synonymous relations such as "whale-dolphin", "astronaut-driver") was achieved by explicit semantic analysis (ESA) in 2007. ESA was a novel (non-machine learning) based approach that represented words in the form of vectors with 100,000 dimensions (where each dimension represents an Article in Wikipedia). However practical applications of the approach are limited due to the large number of required dimensions in the vectors. More recently, advances in neural network techniques in combination with other new approaches (tensors) led to a host of new recent developments: Word2vec from Google, GloVe from Stanford University, and fastText from Facebook AI Research (FAIR) labs. (en)
prov:wasDerivedFrom	wikipedia-en:Semantic_space?oldid=1037888802&ns=0
page length (characters) of wiki page	5247 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Semantic_space
is Link from a Wikipage to another Wikipage of	Moral identity Conceptual space Distributional–relational database Mirella Lapata Sentiment analysis Semantic folding
is academic discipline of	Mirella Lapata
is foaf:primaryTopic of	wikipedia-en:Semantic_space

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