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The most vexing parse is a counterintuitive form of syntactic ambiguity resolution in the C++ programming language. In certain situations, the C++ grammar cannot distinguish between the creation of an object parameter and specification of a function's type. In those situations, the compiler is required to interpret the line as a function type ...
Parse tree generated with NLTK. The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language. It supports classification, tokenization, stemming, tagging, parsing, and semantic reasoning ...
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 SEO terminology, stop words are the most common words that many search engines used to avoid for the purposes of saving space and time in processing of large data during crawling or indexing. For some search engines , these are some of the most common, short function words , such as the , is , at , which , and on .
The erase–remove idiom cannot be used for containers that return const_iterator (e.g.: set) [6] std::remove and/or std::remove_if do not maintain elements that are removed (unlike std::partition, std::stable_partition). Thus, erase–remove can only be used with containers holding elements with full value semantics without incurring resource ...
Instead, a typically smaller list of "rules" is stored which provides a path for the algorithm, given an input word form, to find its root form. Some examples of the rules include: if the word ends in 'ed', remove the 'ed' if the word ends in 'ing', remove the 'ing' if the word ends in 'ly', remove the 'ly'
For example, word2vec has been used to map a vector space of words in one language to a vector space constructed from another language. Relationships between translated words in both spaces can be used to assist with machine translation of new words.
For example, Pfold is used in secondary structure prediction from a group of related RNA sequences, [20] covariance models are used in searching databases for homologous sequences and RNA annotation and classification, [11] [24] RNApromo, CMFinder and TEISER are used in finding stable structural motifs in RNAs.