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A lazy copy is an implementation of a deep copy. When initially copying an object, a (fast) shallow copy is used. A counter is also used to track how many objects share the data. When the program wants to modify an object, it can determine if the data is shared (by examining the counter) and can do a deep copy if needed.
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 ...
A common alternative to using dictionaries is the hashing trick, where words are mapped directly to indices with a hashing function. [5] Thus, no memory is required to store a dictionary. Hash collisions are typically dealt via freed-up memory to increase the number of hash buckets [clarification needed]. In practice, hashing simplifies the ...
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]
Gensim is implemented in Python and Cython for performance. Gensim is designed to handle large text collections using data streaming and incremental online algorithms, which differentiates it from most other machine learning software packages that target only in-memory processing.
dictc (DICT Client) [11] client for Windows written in Delphi. dict.org's own client (part of the dictd [7] package) dictem, [12] for the Emacs text editor; Dictionary, an application included with Mac OS X. Online dictionaries can be accessed by setting it as the helper for 'dict://' URI schemes. Fantasdic; GNOME Dictionary, comes with GNOME
Sparse dictionary learning (also known as sparse coding or SDL) is a representation learning method which aims to find a sparse representation of the input data in the form of a linear combination of basic elements as well as those basic elements themselves. These elements are called atoms, and they compose a dictionary.
Each dictionary entry is of the form dictionary[...] = {index, token}, where index is the index to a dictionary entry representing a previously seen sequence, and token is the next token from the input that makes this entry unique in the dictionary. Note how the algorithm is greedy, and so nothing is added to the table until a unique making ...