Terminals TAB TER SDL2.5 WATERPR LOOSE PIECE Enlarge Mfr. Part # 1-2321928-1. Mouser Part # 571-1-2321928-1. TE Connectivity / AMP: Terminals TAB TER. By tamkimbaoboi v6.2.1 Vietnam 21 days ago. Mate: Universal Tab Translator. Twopeople Software. Translate Share. Monitor and reply to app reviews. Google's free service instantly translates words, phrases, and web pages between English and over 100 other languages. Get the world's largest catalog of guitar, bass & ukulele chords, tabs, and lyrics to learn your favorite songs! Search for any song by type, difficulty, tuning, and rating. Focus on guitar techniques or discover songs for particular moments with collections from professional guitarists. Play 15,000+ popular songs in their original sound with Tonebridge Guitar Effects. Features:. Explore.
In folk belief, spirit is the vital principle or animating force within all living things. As far back as 1628 and 1633 respectively, both William Harvey and René Descartes speculated that somewhere within the body, in a special locality, there was a âvital spiritâ or 'vital force', which animated the whole bodily frame, such as the engine in a factory moves the machinery in it. [1] Spirit has frequently been conceived of as a supernatural being, or non-physical entity; for example, a demon, ghost, fairy, or angel.[2] In ancient Islamic terminology however, a spirit (rūḥ), applies only to pure spirits, but not to other invisible creatures, such as jinn, demons and angels.[3]
Historically, spirit has been used to refer to a 'subtle' as opposed to 'gross' material substance, as put forth in the notable last paragraph of Sir Isaac Newton's Principia Mathematica.[4] In English Bibles, 'the Spirit' (with a capital 'S'), specifically denotes the Holy Spirit.
The concepts of spirit and soul often overlap, and both are believed to survive bodily death in some religions,[5] and 'spirit' can also have the sense of ghost, i.e. a manifestation of the spirit of a deceased person. Spirit is also often used to refer to the consciousness or personality.
Etymology[edit]
The modern English word 'spirit' comes from the Latinspiritus, but also 'spirit, soul, courage, vigor', ultimately from a Proto-Indo-European*(s)peis. It is distinguished from Latin anima, 'soul' (which nonetheless also derives from an Indo-European root meaning 'to breathe', earliest form *h2enh1-).[6] In Greek, this distinction exists between pneuma (Ïνεῦμα), 'breath, motile air, spirit,' and psykhÄ (ÏÏ
Ïή), 'soul'[2] (even though the latter term, Ïá¿¡Ïή = psykhÄ/psÅ«khÄ, is also from an Indo-European root meaning 'to breathe': *bhes-, zero grade*bhs-devoicing in proto-Greek to *phs-, resulting in historical-period Greek ps- in psÅ«khein, 'to breathe', whence psÅ«khÄ, 'spirit', 'soul').[7]
The word 'spirit' came into Middle English via Old French. The distinction between soul and spirit also developed in the Abrahamic religions: Arabic nafs (ÙÙس) opposite rūḥ (رÙØ); Hebrew neshama (× Ö°×©Ö¸××Ö¸×â nÉšâmâh) or nephesh (× Ö¶Ö«×¤Ö¶×©×â népÌeÅ¡) (in Hebrew neshama comes from the root NÅ M or 'breath') opposite ruach (ר×Ö¼×Ö·â rúaħ). (Note, however, that in Semitic just as in Indo-European, this dichotomy has not always been as neat historically as it has come to be taken over a long period of development: Both × Ö¶Ö«×¤Ö¶×©×â (root × ×¤×©×â) and ר×Ö¼×Ö·â (root ר××â), as well as cognate words in various Semitic languages, including Arabic, also preserve meanings involving miscellaneous air phenomena: 'breath', 'wind', and even 'odour'.[8][9][10])
Usage[edit]![]()
'Spirit' has acquired a number of meanings:
The connection between spirit and life is one of those problems involving factors of such complexity that we have to be on our guard lest we ourselves get caught in the net of words in which we seek to ensnare these great enigmas. For how can we bring into the orbit of our thought those limitless complexities of life which we call 'Spirit' or 'Life' unless we clothe them in verbal concepts, themselves mere counters of the intellect? The mistrust of verbal concepts, inconvenient as it is, nevertheless seems to me to be very much in place in speaking of fundamentals. 'Spirit' and 'Life' are familiar enough words to us, very old acquaintances in fact, pawns that for thousands of years have been pushed back and forth on the thinker's chessboard. The problem must have begun in the grey dawn of time, when someone made the bewildering discovery that the living breath which left the body of the dying man in the last death-rattle meant more than just air in motion. It can scarcely be an accident onomatopoeic words like ruach (Hebrew), ruch (Arabic), roho (Swahili) mean âspiritâ no less clearly than ÏνεÏμα (pneuma, Greek) and spiritus (Latin).[16]
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Similar concepts in other languages include Greek pneuma, Chinese Ling and hun (éé) and Sanskrit akasha / atman[2] (see also prana). Some languages use a word for spirit often closely related (if not synonymous) to mind.[citation needed] Examples include the German Geist (related to the English word ghost) or the French l'esprit. English versions of the Bible most commonly translate the Hebrew word ruach (ר××; wind) as 'the spirit', whose essence is divine.[18]
Alternatively, Hebrew texts commonly use the word nephesh. Kabbalists regard nephesh as one of the five parts of the Jewish soul, where nephesh (animal) refers to the physical being and its animal instincts. Similarly, Scandinavian, Baltic, and Slavic languages, as well as Chinese (æ° qi), use the words for breath to express concepts similar to 'the spirit'.[2]
See also[edit]
References[edit]
Further reading[edit]
External links[edit]
Retrieved from 'https://en.wikipedia.org/w/index.php?title=Spirit&oldid=983012991'
About | Citing |Questions |Download |Included Tools |Extensions |Release history |Sample output |Online |FAQ
About
A natural language parser is a program that works out the grammaticalstructure of sentences, for instance, which groups of words go together(as 'phrases') and which words are the subject or object of averb. Probabilistic parsers use knowledge of language gained fromhand-parsed sentences to try to produce the most likely analysis of newsentences. These statistical parsers still make some mistakes, butcommonly work rather well. Their development was one of the biggest breakthroughs innatural language processing in the 1990s. You can try out our parseronline.
Package contents
This package is a Java implementation of probabilistic natural languageparsers, both highly optimized PCFG and lexicalized dependency parsers, and alexicalized PCFG parser. The original version of this parser was mainly written by Dan Klein,with support code and linguistic grammar development by Christopher Manning. Extensive additional work (internationalization and language-specificmodeling, flexible input/output, grammar compaction, lattice parsing,k-best parsing,typed dependencies output,user support, etc.) has been done by Roger Levy, Christopher Manning,Teg Grenager, Galen Andrew, Marie-Catherine de Marneffe, BillMacCartney, Anna Rafferty, Spence Green, Huihsin Tseng, Pi-Chuan Chang, WolfgangMaier, and Jenny Finkel.
The lexicalized probabilistic parser implements a factored product model, with separate PCFG phrase structure and lexical dependency experts, whose preferences are combined by efficient exact inference, using an A* algorithm.Or the software can be used simply as an accurate unlexicalized stochasticcontext-free grammar parser.Either of these yields a good performance statistical parsing system.A GUI is provided for viewing the phrase structure tree output of the parser.
As well as providing an English parser, the parser can beand has been adapted to work with other languages.A Chinese parser based on the Chinese Treebank, a Germanparser based on the Negra corpus and Arabic parsers based on the Penn Arabic Treebank are also included.The parser has also been used for other languages, such as Italian,Bulgarian, and Portuguese.
The parser provides Universal Dependencies (v1) and Stanford Dependencies output as well as phrase structure trees. Typed dependencies areotherwise known grammatical relations. This style of output is available only for English and Chinese.For more details, please refer to the Stanford Dependencies webpage and the Universal Dependencies v1 documentation. (See also the current Universal Dependencies documentation, but we are yet to update to it.).
Shift-reduce constituency parser
As of version 3.4 in 2014, the parser includes the code necessary to run a shift reduce parser, a much faster constituent parser with competitive accuracy. Models for this parser are linked below.
Neural-network dependency parser
In version 3.5.0 (October 2014) we released a high-performance dependency parser powered by a neural network. The parser outputs typed dependency parses for English and Chinese. The models for this parser are included in the general Stanford Parser models package.
Dependency scoring
The package includes a tool for scoring of generic dependency parses, in a class edu.stanford.nlp.trees.DependencyScoring. This tool measures scores for dependency trees, doing F1 and labeled attachment scoring. The included usage message gives a detailed description of how to use the tool.
Usage notes
The current version of the parser requires Java 8 or later.(You can also download an old version of the parser, version 1.4,which runs under JDK 1.4, version 2.0 which runs under JDK 1.5, version 3.4.1which runs under JDK 1.6, but those distributions are no longer supported.)The parser also requires a reasonable amount of memory (at least 100MB to run as a PCFG parser on sentences up to 40 words in length; typically around 500MB of memory to be able to parse similarly long typical-of-newswire sentences using the factored model).
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The parser is available for download,licensed under the GNUGeneral Public License (v2 or later). Source is included. The packageincludes components for command-line invocation, a Java parsingGUI, and a Java API.
The download is a 261 MB zipped file (mainly consisting of included grammar data files). If you unpack the zip file, you should have everything needed. Simple scripts are included to invoke the parser on a Unix or Windows system. For another system, you merely need to similarly configure the classpath.
Licensing
The parser code is dual licensed (in a similar manner to MySQL, etc.). Open source licensing is under the full GPL,which allows many free uses.For distributors of proprietarysoftware, commercial licensing is available.(Fine print: The traditional (dynamic programmed) Stanford Parser does part-of-speech tagging as it works, but the newer constituency and neural network dependency shift-reduce parsers require pre-tagged input. For convenience, we include the part-of-speech tagger code, but not models with the parser download. However, if you want to use these parsers under a commercial license, then you need a license to both the Stanford Parser and the Stanford POS tagger. Or you can get the whole bundle of Stanford CoreNLP.)If you don't need a commercial license, but would like to supportmaintenance of these tools, we welcome gift funding: use this form and write 'Stanford NLP Group open source software' in the Special Instructions.
Citing the Stanford Parser![]()
The main technical ideas behind how these parsers work appear in thesepapers. Feel free to cite one or more of the following papers or people depending on what youare using. Since the parser is regularly updated, we appreciate it ifpapers with numerical results reflecting parser performance mention theversion of the parser being used!
For the neural-network dependency parser: For the Compositional Vector Grammar parser (starting at version 3.2): For the Shift-Reduce Constituency parser (starting at version 3.2): For the PCFG parser (which also does POS tagging): For the factored parser (which also does POS tagging): For the Universal Dependencies representation: For the English Universal Dependencies converter and the enhanced English Universal Dependencies representation: For the (English) Stanford Dependencies representation: For the German parser: For the Chinese Parser: For the Chinese Stanford Dependencies: For the Arabic parser: For the French parser: For the Spanish parser: Questions about the parser?
DownloadThe standard download includes models for Arabic, Chinese, English, French, German, and Spanish. Thereare additional models we do not release with the standalone parser, including shift-reduce models, thatcan be found in the models jars for each language. Below are links to those jars. Arabic Models Chinese Models English Models French Models German Models Spanish Models Extensions: Packages by others using the parser
Java
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PHP
Python/Jython
Ruby
.NET / F# / C#
OS X
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Sample input and output
The parser can read various forms of plain text input and can outputvarious analysis formats, including part-of-speech tagged text, phrasestructure trees, and a grammatical relations (typed dependency) format.For example, consider the text:
The following output showspart-of-speech tagged text, then a context-free phrase structure grammarrepresentation, and finally a typed dependency representation. All ofthese are different views of the output of the parser.
This output was generated with the command:
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