Open Source Windows Linguistics Software

King Saud University Corpus of Classical Arabic (KSUCCA) is a pioneering 50 million tokens annotated corpus of Classical Arabic texts from the period of pre-Islamic era until the fourth Hijri century (equivalent to the period from the seventh until early eleventh century CE), which is the period of pure classical Arabic. The main aim of this corpus is to be used for studying the distributional lexical semantics of The Quran words. However, it can be used for other research purposes, such as: • Arabic linguistics, which includes: lexical, morphological, syntactic, semantic and pragmatic research. • Arabic computational linguistics, which includes: lexical, morphological, syntactic, semantic and pragmatic research including their various applications. • Arabic language teaching for both Arabs and non Arabs. • Artificial intelligence. • Natural language processing. • Information retrieval. • Question answering. • Machine translation.

We temporarily suspend the release of the program due to a patent application. -2020.09 AJ-JpnRa Tool is Japanese text readability analysis program, is mainly ordered by the guidelines of JLPT. You can analyze Japanese-Text Readability with the length and Chinese character level of the text by using the AJ-JpnRa Tool. And Chinese character level is analyzed by the database(AJ-JpnRa Tool), which was built according to essential Chinese character education guideline of Japan elementary and middle school and JLPT; 学年別漢字配当表, 常用漢字. Essential Chinese character education guideline of elementary school is called 学年別漢字配当表, has 1006 chinese characters. And middle school one has 1130 chainese characters. If you combine essential Chinese character of Elementary and Middle school, you can have the regular-use Chinese characters. And the AJ-JpnRa Tool has the Chinese character database of JLPT also, which was built by using the questions from previous JLPT and New JLPT tests last 23 years.

AFEWC corpus is a multilingual comparable text articles in Arabic, French, and English languages. Each triple article is related to the same topic (aligned at article level). AFEWC corpus is collected from Wikipedia. The corpus is available for free for research purposes only. It is composed of 40K aligned articles, 91.3M English words, 57.8M French words, 22M Arabic words, 2.8M English unique words, 1.9M French unique words, and 1.5M Arabic unique words. Wikipedia text is available under Creative Commons Attribution-ShareAlike 3.0 License. https://en.wikipedia.org/wiki/Wikipedia:About To cite the corpora: M. Saad, D. Langlois, and K. Smaïli. Extracting Comparable Articles from Wikipedia and Measuring their Comparabilities. Procedia - Social and Behavioral Sciences, 95(0):40 – 47, 2013. ISSN 1877-0428.

----------------- - What is it? - ----------------- The "Falcon Search" is a JAVA API and tool to search inside the documents. It was originally started to search the content in pdf files under the project "HAWK Search". Searching with this tool is query-based not word-based as in most of the document search tools OR document readers. It also takes care of jumbling of words within query and spelling mistakes. Commonly used techniques in this project are Natural Language Processing, Information Extraction and Question-Answering Architecture. ---------------------- - Latest Version - ---------------------- Details of latest version can be found on project website - http://geekdadaji.com --------------------------- - CONTACT DETAILS - --------------------------- CREATOR : SWAPNIL A JADHAV (saj1919) EMAIL ID : dadajibudhau@gmail.com WEBSITE : http://geekdadaji.com LICENSE : CC BY-NC 4.0

The Linguistic Analyzer (Almuhalil Alloghawy) is a free tool designed by a team from Al-Imam Muhammad bin Saud islamic university that can be used for corpus analysis and comparison in terms of the several linguistic characteristics, such as frequency lists generation, concordances, collocation extraction, the difference between two words, and keyword identification.

The main purpose of this tool is to help users in extracting syntagmatic relations between words, lemmas and roots available in the Quran; these relations include identifying significant collocates and words’ co-occurrences. In addition, the tool also provides other helpful functionalities that complement the primary purpose, which include a Key Word In Context (KWIC) concordance, in addition to frequency lists of all words, lemmas and roots in the holy Quran. The main intended users of this tool are Arabic Quranic scholars and linguists. The Meaning Explorer applies a new distributional semantic model to extract words’ significant co-occurrences from the Quran. This model is based on the Refined MI association measure applied to all words within a symmetric sliding window of five words surrounding the node word. The Refined MI association measure is chosen because of its remarkable ability ability to extract syntagmatic relations between the words of the Quran.

The semantic annotation system OPTIMA is the result of Andreas Vlachidis PhD work, (supervised by Prof. Douglas Tudhope, University of Glamorgan, UK). OPTIMA performs the NLP tasks of Named Entity Recognition, Relation Extraction, Negation Detection and Word Sense Disambiguation using hand-crafted rules and SKOS terminological resources (English Heritage Thesauri and Glossaries). The resulted semantic annotations are associated with classes of the (ISO 21127:2006) CIDOC Conceptual Reference Model (CRM) and its archaeological extension, CRM-EH. OPTIMA is also targeted at the detection and recognition of contextual relations between CRM entities. Such relations are modeled with respect to the CRM-EH archaeology extension. The pipeline targets the CIDOC-CRM entities; E19.Physical_Object, E53.Place, E49.Time_Appellation and E57.Material and the CRM-EH entities; EHE1001.Context_Event, EHE1002.Production_Event, EHE1004.Deposition_Event and P45.consists_of material property

IT WILL WORK WITH A JAVA DEVELOPMENT KIT 1.7 ONLY !!! This program is a data-miner and a knowledge-miner. It does exactly the opposite of what the text summarizers do. A text summarizer produces a shortened text given some text as an input. An inverse summarizer takes the shortened input, a similar or a same text and does the process in reverse. This results in an expanded text. It can be used with any text or notes that have the knowledge gaps. It is a great aid to any creative work and it simply pin-points to data that may be of some relevance. How to run this program? 1. Make sure JAVA 1.7 development is installed and running/compiling properly with all environment variables properly set. 2. Uncompress the LITE release into a desired directory. 3. Go to a src/ directory with a Terminal/Console/Command-Prompt and do the following: javac Te2.java java Te2 The program should open.

Java API and tools for performing a wide range of AI tasks such as: word sense disambiguation (released), optimization (5 Evolutionary Algorithms Implemented ETA February 2014), opinion mining (ETA November 2014) and text wikification (ETA July 2014). Gannu includes some graphical interfaces for scientific purposes. When using Gannu please cite: *Jiménez, F. V., Gelbukh, A. F. & Sidorov, G. (2013). Simple Window Selection Strategies for the Simplified Lesk Algorithm for Word Sense Disambiguation.. In F. Castro, A. F. Gelbukh & M. González (eds.), MICAI (1) (pp. 217-227), : Springer. ISBN: 978-3-642-45113-3 The zip file contains Gannu jar, source, API documentation and necessary resources for performing research. Gannu uses the following projects: Weka, JExcel API, Stanford POS Tagger and WordNet. Please cite them when using Gannu.