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Nodes generally correspond to lexical and grammatical meanings as these are directly expressed by items in the lexicon or by inflectional means, but the theory allows the option of decomposing meanings into more fine-grained representation via processes of semantic paraphrasing, [3] which are also key to dealing with synonymy and translation ...
The lexical route is the process whereby skilled readers can recognize known words by sight alone, through a "dictionary" lookup procedure. [ 1 ] [ 4 ] According to this model, every word a reader has learned is represented in a mental database of words and their pronunciations that resembles a dictionary, or internal lexicon.
Lexical semantics (also known as lexicosemantics), as a subfield of linguistic semantics, is the study of word meanings. [ 1 ] [ 2 ] It includes the study of how words structure their meaning, how they act in grammar and compositionality , [ 1 ] and the relationships between the distinct senses and uses of a word.
This can be explained by models that do not assume a distinct level between the semantic and the phonological stages (and so lack a lemma representation). [3] During the process of language activation, lemma retrieval is the first step in lexical access. In this step, meaning and the syntactic elements of a lexical item are realized as the lemma.
In the spectrum theory, at one end "each phonological form is connected to one complex semantic representation", at the opposite end, homonyms and polysemes have their "own semantic representation[s]". [14] The middle of the spectrum contains the theories that "suggest that related senses share a general or core semantic representation". [14]
The cohort model relies on several concepts in the theory of lexical retrieval. The lexicon is the store of words in a person's mind; [3] it contains a person's vocabulary and is similar to a mental dictionary. A lexical entry is all the information about a word and the lexical storage is the way the items are stored for peak retrieval.
While distributional semantics typically has been applied to lexical items—words and multi-word terms—with considerable success, not least due to its applicability as an input layer for neurally inspired deep learning models, lexical semantics, i.e. the meaning of words, will only carry part of the semantics of an entire utterance.
In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]