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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 ‘vital spirit’ 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.
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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 (πνεῦμα), 'breath, motile air, spirit,' and psykhē (ψυχή), 'soul'[2] (even though the latter term, ψῡχή = psykhē/psūkhē, 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 psūkhein, 'to breathe', whence psūkhē, '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 (نفس) opposite rūḥ (روح); Hebrew neshama (נְשָׁמָה nəšâmâh) or nephesh (נֶ֫פֶשׁ nép̄eš) (in Hebrew neshama comes from the root NŠM or 'breath') opposite ruach (רוּחַ 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 נֶ֫פֶשׁ (root נפשׁ) and רוּחַ (root רוח), 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:
- Christian theology can use the term 'Spirit' to describe the Holy Spirit.
- Christian Science uses 'Spirit' as one of seven synonyms for God, as in: 'Principle; Mind; Soul; Spirit; Life; Truth; Love'[11]
- Latter Day Saint prophet Joseph Smith Jr. taught that the concept of spirit as incorporeal or without substance was incorrect: 'There is no such thing as immaterial matter. All spirit is matter, but it is more fine or pure, and can only be discerned by purer eyes.'[12] Regarding the soul, Joseph Smith wrote 'And the Gods formed man from the dust of the ground, and took his spirit (that is, the man’s spirit), and put it into him; and breathed into his nostrils the breath of life, and man became a living soul.'[13] Thus, the soul is the combination of a spirit with a body (although most members use 'soul' and 'spirit' interchangeably). In Mormon scripture, spirits are sometimes referred to as 'intelligences'[14]. But other Mormon scriptures teach that God organized the spirits out of a pre-existing substance called 'intelligence' or 'the light of truth'[15]. While this may seem confusing, it can be compared to how a programmer writes an algorithm by organizing lines of logical code. The logic always existed, independent of the programmer, but it is the creator who organizes it into a living spirit / intelligence / soul.
- Various forms of animism, such as Japan's Shinto and African traditional religion, focus on invisible beings that represent or connect with plants, animals, or landforms (kami)[citation needed]: translators usually employ the English word 'spirit' when trying to express the idea of such entities.
- According to C. G. Jung (in a lecture delivered to the literary Society of Augsburg, 20 October 1926, on the theme of “Nature and Spirit”):
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 ‘spirit’ no less clearly than πνεύμα (pneuma, Greek) and spiritus (Latin).[16]
- Psychical research, 'In all the publications of the Society for Psychical Research the term ‘spirit’ stands for the personal stream of consciousness whatever else it may ultimately be proved to imply or require' (James H. Hyslop, 1919).[17]
Related concepts[edit]
Navicat premium essentials 12 1 1970. Similar concepts in other languages include Greek pneuma, Chinese Ling and hun (靈魂) 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 (רוח; wind) as 'the spirit', whose essence is divine.[18] Integrity pro 8 1 15.
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 (气 qi), use the words for breath to express concepts similar to 'the spirit'.[2]
See also[edit]
- Great Spirit or Wakan Tanka is a term for the Supreme Being.
References[edit]
- ^Michels, John (January 18, 1884). Science: Volume 3. Highwire Press, Jestor: American Association for the Advancement of Science. p. 75. Retrieved 10 October 2019.
- ^ abcdFrançois 2009, p.187-197.
- ^Chodkiewicz, M., “Rūḥāniyya”, in: Encyclopaedia of Islam, Second Edition, Edited by: P. Bearman, Th. Bianquis, C.E. Bosworth, E. van Donzel, W.P. Heinrichs. Consulted online on 18 November 2019 <http://dx.doi.org/10.1163/1573-3912_islam_SIM_6323> First published online: 2010
- ^Burtt, Edwin A. (2003). Metaphysical Foundations of Modern Physical Science. Mineola, New York: Dover Publications, Inc. p. 275.
- ^OED 'spirit 2.a.: The soul of a person, as commended to God, or passing out of the body, in the moment of death.'
- ^anə-, from *ə2enə1-. Watkins, Calvert. 2000. The American Heritage Dictionary of Indo-European Roots, second edition. Boston: Houghton-Mifflin Co., p.4. Also available online. (NB: Watkins uses ə1, ə2, ə3 as fully equivalent variants for h1, h2, h3, respectively, for the notation of Proto-Indo-European laryngeal segments.)
- ^bhes-2. Watkins, Calvert. 2000. The American Heritage Dictionary of Indo-European Roots, second edition. Boston: Houghton-Mifflin Co., 2000, p.11. Also available online
- ^Koehler, L., Baumgartner, W., Richardson, M. E. J., & Stamm, J. J. (1999). The Hebrew and Aramaic lexicon of the Old Testament (electronic ed.) (711). Leiden; New York: E.J. Brill.
- ^Brown, F., Driver, S. R., & Briggs, C. A. (2000). Enhanced Brown-Driver-Briggs Hebrew and English Lexicon (electronic ed.) (659). Oak Harbor, WA: Logos Research Systems. (N.B. Corresponds closely to printed editions.)
- ^Brown, F., Driver, S. R., & Briggs, C. A. (2000). Enhanced Brown-Driver-Briggs Hebrew and English Lexicon (electronic ed.) (924ff.). Oak Harbor, WA: Logos Research Systems. (N.B. Corresponds closely to printed editions.)
- ^Eddy, Mary Baker (1875). 'Glossary'. Science and Health With Key to the Scriptures(txt)
|format=
requires|url=
(help). p. 587. Retrieved 2009-03-11.GOD — The great I AM; the all-knowing, all-seeing, all-acting, all-wise, all-loving, and eternal; Principle; Mind; Soul; Spirit; Life; Truth; Love; all substance; intelligence.
- ^
- ^'Abraham 5:7'. www.churchofjesuschrist.org. Retrieved 2020-07-14.
- ^'Abraham 3:22'. www.churchofjesuschrist.org. Retrieved 2020-07-14.
- ^'Topical Guide: Intelligence, Intelligences'. www.churchofjesuschrist.org. Retrieved 2020-07-14.
- ^Jung, C. G. (1960). 'Spirit and Life'. In Hull, R. F. C. (ed.). The Collected Works of C. G. Jung. XX. 8. New York, NY: Pantheon Books for Bollinger. pp. 319–320.
- ^Hyslop, James Hervey (1919). Contact with the Other World (First ed.). New York, NY: The Century Co. p. 11.
- ^'Ruach: Spirit or Wind or ???'. BiblicalHeritage.org. Archived from the original on 6 October 2015.
Further reading[edit]
- François, Alexandre (2008), 'Semantic maps and the typology of colexification: Intertwining polysemous networks across languages', in Vanhove, Martine (ed.), From Polysemy to Semantic change: Towards a Typology of Lexical Semantic Associations, Studies in Language Companion Series, 106, Amsterdam, New York: Benjamins, pp. 163–215
- Baba, Meher (1967). Discourses. San Francisco: Sufism Reoriented. ISBN1-880619-09-1.
External links[edit]
- The dictionary definition of spirit at Wiktionary
- Quotations related to Spirit at Wikiquote
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. Cisdem pdf converter 7 1 0 percent. 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).
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:
Danqi Chen and Christopher D Manning. 2014. A Fast and Accurate Dependency Parser using Neural Networks. Proceedings of EMNLP 2014
For the Compositional Vector Grammar parser (starting at version 3.2):
Richard Socher, John Bauer, Christopher D. Manning and Andrew Y. Ng. 2013.Parsing With Compositional Vector Grammars. Proceedings of ACL 2013
For the Shift-Reduce Constituency parser (starting at version 3.2):
This parser was written by John Bauer. You can thank him and cite the web page describing it: https://nlp.stanford.edu/software/srparser.html. You can also cite the original research papers of others mentioned on that page.
For the PCFG parser (which also does POS tagging):
Dan Klein and Christopher D. Manning. 2003. Accurate Unlexicalized Parsing. Proceedings of the 41st Meeting of the Association for Computational Linguistics, pp. 423-430.
For the factored parser (which also does POS tagging):
Dan Klein and Christopher D. Manning. 2003. Fast Exact Inference with a Factored Model for Natural Language Parsing. In Advancesin Neural Information Processing Systems 15 (NIPS 2002), Cambridge, MA: MIT Press, pp. 3-10.
For the Universal Dependencies representation:
Joakim Nivre, Marie-Catherine de Marneffe, Filip Ginter, Yoav Goldberg, Jan Hajič,Christopher D. Manning, Ryan McDonald, Slav Petrov, Sampo Pyysalo, Natalia Silveira,Reut Tsarfaty, and Daniel Zeman. 2016. Universal Dependencies v1: A Multilingual Treebank Collection. In LREC 2016.
For the English Universal Dependencies converter and the enhanced English Universal Dependencies representation:
Sebastian Schuster and Christopher D. Manning. 2016. Enhanced English Universal Dependencies: An Improved Representation for Natural Language Understanding Tasks.In LREC 2016.
For the (English) Stanford Dependencies representation:
Marie-Catherine de Marneffe, Bill MacCartney and Christopher D. Manning. 2006. GeneratingTyped Dependency Parses from Phrase Structure Parses. In LREC 2006.
For the German parser:
Anna Rafferty and Christopher D. Manning. 2008.Parsing Three German Treebanks: Lexicalized and Unlexicalized Baselines.In ACL Workshop on Parsing German.
For the Chinese Parser:
Roger Levy and Christopher D. Manning.2003.Is it harder to parse Chinese, or the Chinese Treebank?ACL 2003, pp. 439-446.
For the Chinese Stanford Dependencies:
Pi-Chuan Chang, Huihsin Tseng, Dan Jurafsky, and Christopher D. Manning.2009.Discriminative Reordering with Chinese Grammatical Relations Features.In Proceedings of the Third Workshop on Syntax and Structure in Statistical Translation.
For the Arabic parser:
Spence Green and Christopher D. Manning.2010.Better Arabic Parsing: Baselines, Evaluations, and Analysis.In COLING 2010.
For the French parser:
Spence Green, Marie-Catherine de Marneffe, John Bauer, and Christopher D. Manning.2010.Multiword Expression Identification with Tree Substitution Grammars: A Parsing tour de force with French.In EMNLP 2011.
For the Spanish parser:
Most of the work on Spanish was by Jon Gauthier. There is no published paper, but you can thank him and/or citethis webpage:https://nlp.stanford.edu/software/spanish-faq.html
Questions about the parser?
- If you're new to parsing, you can start by running the GUI to tryout the parser. Scripts are included for linux (lexparser-gui.sh) andWindows (lexparser-gui.bat).
- Take a look at the Javadoc
lexparser
packagedocumentation andLexicalizedParser
class documentation.(Point your web browser at theindex.html
file in the includedjavadoc
directory and navigate to those items.) - Look at the parser FAQ for answers to common questions.
- If none of that helps, please see our emailguidelines for instructions on how to reach us for further assistance.
Download
The 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
- tydevi Typed DependencyViewer that makes a picture of the Stanford Dependencies analysis of a sentence. By Bernard Bou.
- DependenSee A Dependency Parse Visualisation Tool that makespictures of Stanford Dependency output. By Awais Athar. (GitHub)
- GATEplug-in. By the GATE Team (esp. Adam Funk).
- GrammarScopegrammatical relation browser. GUI, especially focusing on grammatical relations (typed dependencies), including an editor. ByBernard Bou.
PHP
- PHP-Stanford-NLP. Supports POS Tagger, NER, Parser. By Anthony Gentile (agentile).
Python/Jython
- Pythoninterface built using JPype by Stefanie Tellex.
- Jython interface.by Viktor Pekar.
Ruby
- Ruby wrapper to theStanford Natural Language Parser. By Bill McNeill. An extended andbetter packaged version of this by John Wilkinson is available at github.
Mate: Universal Tab Translator 6 1 4 X 4
.NET / F# / C#
- Sergey Tihon has ported the Stanford Parser to F# (or any .NET language, including C#), using IKVM. See his blog post, his Github site, or the listing on NuGet.
OS X
- If you use Homebrew, you can install the Stanford Parser with:brew install stanford-parser
Release history
Mate: Universal Tab Translator 6 1 48
Version 4.0.0 | 2020-05-22 | Model tokenization updated to UDv2.0 | |
Version 3.9.2 | 2018-10-17 | Updated for compatibility | |
Version 3.9.1 | 2018-02-27 | new French and Spanish UD models, misc. UD enhancements, bug fixes | |
Version 3.8.0 | 2017-06-09 | Updated for compatibility | |
Version 3.7.0 | 2016-10-31 | new UD models | |
Version 3.6.0 | 2015-12-09 | Updated for compatibility | |
Version 3.5.2 | 2015-04-20 | Switch to universal dependencies | shift reduce parser models |
Version 3.5.1 | 2015-01-29 | Dependency parser fixes and model improvements | shift reduce parser models |
Version 3.5.0 | 2014-10-31 | Upgrade to Java 8; add neural-network dependency parser | shift reduce parser models |
Version 3.4.1 | 2014-08-27 | Add Spanish models | shift reduce parser models |
Version 3.4 | 2014-06-16 | Shift-reduce parser, dependency improvements, French parser uses CC tagset | shift reduce parser models |
Version 3.3.1 | 2014-01-04 | English dependency 'infmod' and 'partmod' combined into 'vmod', other minor dependency improvements | |
Version 3.3.0 | 2013-11-12 | English dependency 'attr' removed, other dependency improvements, imperative training data added | |
Version 3.2.0 | 2013-06-20 | New CVG based English model with higher accuracy | |
Version 2.0.5 | 2013-04-05 | Dependency improvements, -nthreads option, ctb7 model | |
Version 2.0.4 | 2012-11-12 | Improved dependency code extraction efficiency, other dependency changes | |
Version 2.0.3 | 2012-07-09 | Minor bug fixes | |
Version 2.0.2 | 2012-05-22 | Some models now support training with extra tagged, non-tree data | |
Version 2.0.1 | 2012-03-09 | Caseless English model included, bugfix for enforced tags | |
Version 2.0 | 2012-02-03 | Threadsafe! | |
Version 1.6.9 | 2011-09-14 | Improved recognition of imperatives, dependencies now explicitely include a root, parser knows osprey is a noun | |
Version 1.6.8 | 2011-06-19 | New French model, improved foreign language models, bug fixes | |
Version 1.6.7 | 2011-05-18 | Minor bug fixes. | |
Version 1.6.6 | 2011-04-20 | Internal code and API changes (ArrayLists rather than Sentence; use of CoreLabel objects) to match tagger and CoreNLP. | |
Version 1.6.5 | 2010-11-30 | Further improvements to English Stanford Dependencies and other minor changes | |
Version 1.6.4 | 2010-08-20 | More minor bug fixes and improvements to English Stanford Dependencies and question parsing | |
Version 1.6.3 | 2010-07-09 | Improvements to English Stanford Dependencies and question parsing, minor bug fixes | |
Version 1.6.2 | 2010-02-26 | Improvements to Arabic parser models, and to English and Chinese Stanford Dependencies | |
Version 1.6.1 | 2008-10-26 | Slightly improved Arabic and German parsing, and Stanford Dependencies | |
Version 1.6 | 2007-08-19 | Added Arabic, k-best PCCFG parsing; improved English grammatical relations | |
Version 1.5.1 | 2006-06-11 | Improved English and Chinese grammatical relations; fixed UTF-8 handling | |
Version 1.5 | 2005-07-21 | Added grammatical relations output; fixed bugs introduced in 1.4 | |
Version 1.4 | 2004-03-24 | Made PCFG faster again (by FSA minimization); added German support | |
Version 1.3 | 2003-09-06 | Made parser over twice as fast; added tokenization options | |
Version 1.2 | 2003-07-20 | Halved PCFG memory usage; added support for Chinese | |
Version 1.1 | 2003-03-25 | Improved parsing speed; included GUI, improved PCFG grammar | |
Version 1.0 | 2002-12-05 | Initial release |
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 strongest rain ever recorded in India shut down the financialhub of Mumbai, snapped communication lines, closed airports and forcedthousands of people to sleep in their offices or walk home during thenight, officials said today.
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.
Mate: Universal Tab Translator 6 1 4 0
This output was generated with the command:
Mate: Universal Tab Translator 6 1 49
java -mx200m edu.stanford.nlp.parser.lexparser.LexicalizedParser-retainTMPSubcategories -outputFormat'wordsAndTags,penn,typedDependencies' englishPCFG.ser.gz mumbai.txt