{ "info": { "author": "Louis van Beurden", "author_email": "louis.vanbeurden@gmail.com", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", "Programming Language :: Python :: 3.5", "Topic :: Text Processing :: Indexing", "Topic :: Text Processing :: Linguistic" ], "description": "# IEML\n[![Build Status](https://travis-ci.org/IEMLdev/ieml.svg?branch=master)](https://travis-ci.org/IEMLdev/ieml)\n\nIEML is an artificial \"natural\" language having the following properties:\n * All the semantic relationships between IEML representations are computable.\n * Every meaning has only one IEML representation and every IEML representation has only one meaning.\n\nThe [ieml grammar](https://pierrelevyblog.files.wordpress.com/2014/01/00-grammaire-ieml1.pdf \"IEML grammar\") is constituted of a [dictionary](https://intlekt.io/?comments=I%3A \"IEML dictionary\") of elementary meanings units - words - and syntactic rules to combine them into topics, then facts and finally, theories.\n\nThis language have been made by the French philosopher [Pierre Levy](https://en.wikipedia.org/wiki/Pierre_L%C3%A9vy).\n\nThe language overcomes some inherent limitations of naturals languages for algorithmic manipulation :\n - text generation : rules-based natural language generation can leverage the regularity of the IEML syntax.\n - out-of-vocabularies : every proposition in IEML is build from ~3500 words, all defined in the dictionary with their paradigmatic relationships.\n - semantic similarities : we can automatically compute semantic relations between IEML propositions.\n - as an interlingua : IEML does not use any existing natural language as its core semantics. But everything that can be said can be described in IEML.\n - interaperobility of ontologies : Each IEML expression will have an unique URL.\n\nEvery meaning has only one IEML representation and every IEML representation has only one meaning :\n - synonymy : the IEML language has an instrinsic order that ensures that every expression has only one valid representation.\n - polysemy : All the IEML expressions are built from semantic primitives (dictionary) with meanining composition operators (syntax). The dictionary is not polysemic, the operators are injetive and theirs resulting propositions can not be reduced to its constituents. Therefore, there is no polysemy in IEML.\nFrom there, we have in IEML a bijection between the signifiers and the signified.\nIEML is then a discrete coordinate system of the semantic space. The IEML expressions are called Uniform Semantic Locators (USLs).\n\nIEML is build from a set of [semantic primes](https://en.wikipedia.org/wiki/Semantic_primes) called the dictionary, and explicit rules of [compositions](https://fr.wikipedia.org/wiki/Combinatoire_s%C3%A9mantique).\n\nThe meaning of the words in the dictionary are conventional but not arbitrary, because they respects a set of constraints:\n - Each meaning must not already be present in the dictionary, and you can not express this meaning by composition of existing meanings of the dictionary.\n - The words are defined in systems (paradigms) and a paradigm must be the broadest possible in the concept.\n - A words defined in a paradigms is build from 3 more abstract words and the words must contains these meanings.\n \nAll the words have a french and english translation. A definition and synonyms in french and english will soon be available.\n\nThe linguist Anna Wierzbicka showed, with her theory [NSM](https://en.wikipedia.org/wiki/Natural_semantic_metalanguage), that we can define any concept described by a proposition in any natural languages with a set of 61 semantic primes, organised in paradigms.\n\nThe semantic of IEML is not a logic description, it can not be reduced to a description of a state of the world.\nA word is not true nor false. Only at least a sentence can be, and only in a descriptive enonciation.\n\n## Install\n\nThe library works with python 3.5+\n\nYou can install the ieml package with pip:\n```bash\npip install ieml\n```\nIf you want to install it from github:\n```bash\ngit clone https://github.com/IEMLdev/ieml\npython setup.py\n```\n## Quick start\n\n### Dictionary\n\nThe IEML dictionary is a set of around ~3500 basic semantics units. \n\nThe dictionay has its own syntax and primitives. The dictionary is organised in layers, from 0 (the most abstract) to 7 (the most specific). The words excepts the primitives are built from words of lower layers. \n\nThe last version of the IEML dictionary is automatically downloaded and installed when instanced:\n```python\nfrom ieml.dictionary import Dictionary\n\ndic = Dictionary()\ndic.index\n```\nThis return a list of all words defined in the dictionary.\nThere is an order defined on the terms of the dictionary, and d.index is the position of the words in this order.\n\nYou can access the translations of a word :\n ```python\nt = dic.index[100]\nt.translations.en\n```\nThere are for the moment two languages supported: french (fr) and english (en)\n\nThe dictionaryis a graph of semantic relationships (paradigmatic) between the words.\nAll the relations are computed automatically from the terms definitions.\n```python\nt.relations.neighbours\n```\nThis return a list of all the neighboors of term t and the type of relation they share.\n\nYou can also access the graph of relation as a numpy array of transitions :\n```python\nm = dic.relations_graph.connexity\n```\nReturn a dense numpy array of boolean where `m[i, j]` is true if there is a relation \nbetween the term number `i` and the term number `j`.\n```\nfrom ieml.dictionary import term\n\nt0 = term('wa.')\nt1 = term('we.')\nm[t0.index, t1.index]\n```\n\nThe `term` function with a string argument call the dictionary parser and\nreturn a Term if the string is a valid IEML expression of a term (defined in the dictionary).\n\n\n### Syntax\n\nA syntactic meaning unit is called an USL, for Uniform Semantic Locator. \nThere is five differents types of USL :\n - Word : the basic meaning constituent, you can find all the defined words in the [IEML dictionary](https://dictionary.ieml.io). \n - Topic: a topic aggregate Words into a root and a flexing morphem, a topic represents a subject, a process. \n - Fact : a fact is a syntactic tree of topics, a fact symbolizes an event, a description.\n - Theory: a theory is a tree of facts, it represents a set of sentence linked together by causal, temporal, logic links etc. \n - Text: a text is a set of Topic, Fact and Theory.\n\nTo instantiate an usl, you can use the USL parser with the `usl` function\nwith a string argument.\n\n```python\nfrom ieml.grammar import usl\n\nusl('[([wa.])]') # topic with a single word\nusl(\"[([t.u.-s.u.-d.u.-']+[t.u.-b.u.-'])*([b.i.-])]\") # topic with two words in his root morphem and one in flexing \n```\n\nYou can also create an usl with constructors :\n```python\nfrom ieml.grammar import word, topic, fact, theory, text\n\nw = word('wa.')\nt0 = topic([w])\nt1 = topic(['wa.', 'e.'])\nt2 = topic(root=[\"t.u.-s.u.-d.u.-'\", \"t.u.-b.u.-'\"], \n flexing=[\"b.i.-\"])\nf = fact([(t2, t0, t1)])\n\nt = text([t0, t1, t2, f])\n```\n\nFor any usls, you can access the words, topics, facts, theories and texts defined \nin the usl by accessing the dedicated property:\n\n```python\nt.words\nt.topics\nt.facts\nt.theories\nt.texts\n```\nEach of these properties returns a set of USLs of the specific type.\n\nFor any couple of usl, you can compute a semantic similarity measure based on the \nrelation matrix of the dictionary :\n```python\nfrom ieml.grammar.distance import dword\nfrom ieml.grammar.tools import random_usl\nu0 = random_usl(Topic)\nu1 = random_usl(Text)\n\ndword(u0, u1)\n```\n\nFor the moments, only a similarity using the words of the USL is defined.\n\n### Collection of USLs\nFor a list of USLs, you can compute a square matrix of relative order from each USLs :\n```python\nfrom ieml.distance.sort import square_order_matrix\n\nusl_list = [random_usl() for _ in range(100)]\n\nm = square_order_matrix(usl_list)\n\ni = 20\nordered_usls = [usl_list[k] for k in m[i, :]]\n```\nordered_usls is the list of usl ordered from USLs number i to the farrest USL from USL i in the collection.\nThis method use the semantic distance between words of the dictionary.\n\n \n \n", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": 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