Search results
Results from the WOW.Com Content Network
For most spoken languages, the boundaries between lexical units are difficult to identify; phonotactics are one answer to this issue. One might expect that the inter-word spaces used by many written languages like English or Spanish would correspond to pauses in their spoken version, but that is true only in very slow speech, when the speaker deliberately inserts those pauses.
The standard 'vanilla' approach to locate the end of a sentence: [clarification needed] (a) If it is a period, it ends a sentence. (b) If the preceding token is in the hand-compiled list of abbreviations, then it does not end a sentence. (c) If the next token is capitalized, then it ends a sentence. This strategy gets about 95% of sentences ...
For example, one could define a dictionary having a string "toast" mapped to the integer 42 or vice versa. The keys in a dictionary must be of an immutable Python type, such as an integer or a string, because under the hood they are implemented via a hash function. This makes for much faster lookup times, but requires keys not change.
Word segmentation is the problem of dividing a string of written language into its component words. In English and many other languages using some form of the Latin alphabet, the space is a good approximation of a word divider (word delimiter), although this concept has limits because of the variability with which languages emically regard collocations and compounds.
Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. Word2vec was developed by Tomáš Mikolov and colleagues at Google and published in 2013. Word2vec represents a word as a high-dimension vector of numbers which capture relationships between words.
It disregards word order (and thus most of syntax or grammar) but captures multiplicity. The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a classifier. [1] It has also been used for computer vision. [2]
A string (or word [23] or expression [24]) over Σ is any finite sequence of symbols from Σ. [25] For example, if Σ = {0, 1}, then 01011 is a string over Σ. The length of a string s is the number of symbols in s (the length of the sequence) and can be any non-negative integer; it is often denoted as |s|.
(1 byte) True: \x08\x01 False: \x08\x00 (2 bytes) int32: 32-bit little-endian 2's complement or int64: 64-bit little-endian 2's complement: Double: little-endian binary64: UTF-8-encoded, preceded by int32-encoded string length in bytes BSON embedded document with numeric keys BSON embedded document Concise Binary Object Representation (CBOR ...