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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 ]
For example, 'palatalized voice' indicates palatalization of all segments of speech spanned by the braces. Several of these symbols may be profitably used as part of single speech sounds, in addition to indicating voice qualities across spans of speech. For example, [ↀ͡r̪͆ː] is blowing a raspberry.
IWE combines Word2vec with a semantic dictionary mapping technique to tackle the major challenges of information extraction from clinical texts, which include ambiguity of free text narrative style, lexical variations, use of ungrammatical and telegraphic phases, arbitrary ordering of words, and frequent appearance of abbreviations and acronyms ...
The study of communication disorders has a history that can be traced all the way back to the ancient Greeks.Modern clinical linguistics, however, largely has its roots in the twentieth century, with the term ‘clinical linguistics’ gaining wider currency in the 1970s, with it being used as the title of a book by prominent linguist David Crystal in 1981. [2]
Unlike previous models, BERT is a deeply bidirectional, unsupervised language representation, pre-trained using only a plain text corpus. Context-free models such as word2vec or GloVe generate a single word embedding representation for each word in the vocabulary, whereas BERT takes into account the context for each occurrence of a given word ...
A disfluence or nonfluence is a non-pathological hesitance when speaking, the use of fillers (“like” or “uh”), or the repetition of a word or phrase. This needs to be distinguished from a fluency disorder like stuttering with an interruption of fluency of speech, accompanied by "excessive tension, speaking avoidance, struggle behaviors, and secondary mannerism".