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WordNet is a lexical database of semantic relations between words that links words into semantic relations including synonyms, hyponyms, and meronyms. The synonyms are grouped into synsets with short definitions and usage examples. It can thus be seen as a combination and extension of a dictionary and thesaurus. While it is accessible to human users via a web browser, its primary use is in automatic text analysis and artificial intelligence applications. It was first created in the English language and the English WordNet database and software tools have been released under a BSD style license and are freely available for download from that WordNet website. There are now WordNets in more than 200 languages.
Malayalam is a Dravidian language spoken in the Indian state of Kerala and the union territories of Lakshadweep and Puducherry by the Malayali people. It is one of 22 scheduled languages of India. Malayalam was designated a "Classical Language of India" in 2013. Malayalam has official language status in Kerala, Lakshadweep and Puducherry (Mahé), and is also the primary spoken language of Lakshadweep. Malayalam is spoken by 35 million people in India. Malayalam is also spoken by linguistic minorities in the neighbouring states; with a significant number of speakers in the Kodagu and Dakshina Kannada districts of Karnataka, and Kanyakumari, Coimbatore and Nilgiris district of Tamil Nadu. It is also spoken by the Malayali Diaspora worldwide, especially in the Persian Gulf countries, due to the large populations of Malayali expatriates there. They are a significant population in each city in India including Mumbai, Bengaluru, Chennai, Delhi, Hyderabad etc.
Word-sense disambiguation is the process of identifying which sense of a word is meant in a sentence or other segment of context. In human language processing and cognition, it is usually subconscious.
Wiktionary is a multilingual, web-based project to create a free content dictionary of terms in all natural languages and in a number of artificial languages. These entries may contain definitions, images for illustration, pronunciations, etymologies, inflections, usage examples, quotations, related terms, and translations of terms into other languages, among other features. It is collaboratively edited via a wiki. Its name is a portmanteau of the words wiki and dictionary. It is available in 195 languages and in Simple English. Like its sister project Wikipedia, Wiktionary is run by the Wikimedia Foundation, and is written collaboratively by volunteers, dubbed "Wiktionarians". Its wiki software, MediaWiki, allows almost anyone with access to the website to create and edit entries.
Cross-language information retrieval (CLIR) is a subfield of information retrieval dealing with retrieving information written in a language different from the language of the user's query. The term "cross-language information retrieval" has many synonyms, of which the following are perhaps the most frequent: cross-lingual information retrieval, translingual information retrieval, multilingual information retrieval. The term "multilingual information retrieval" refers more generally both to technology for retrieval of multilingual collections and to technology which has been moved to handle material in one language to another. The term Multilingual Information Retrieval (MLIR) involves the study of systems that accept queries for information in various languages and return objects of various languages, translated into the user's language. Cross-language information retrieval refers more specifically to the use case where users formulate their information need in one language and the system retrieves relevant documents in another. To do so, most CLIR systems use various translation techniques. CLIR techniques can be classified into different categories based on different translation resources:
A parallel text is a text placed alongside its translation or translations. Parallel text alignment is the identification of the corresponding sentences in both halves of the parallel text. The Loeb Classical Library and the Clay Sanskrit Library are two examples of dual-language series of texts. Reference Bibles may contain the original languages and a translation, or several translations by themselves, for ease of comparison and study; Origen's Hexapla placed six versions of the Old Testament side by side. A famous example is the Rosetta Stone, whose discovery allowed the Ancient Egyptian language to begin being deciphered.
The Malayali people are a Dravidian ethnolinguistic group originating from the present-day state of Kerala & Union Territory of Lakshadweep in India, occupying its southwestern Malabar coast. They form the majority of the population in Kerala and Lakshadweep. They are predominantly native speakers of the Malayalam language, one of the eleven classical languages of India. The state of Kerala was created in 1956 through the States Reorganisation Act. Prior to that, since the 1800s existed the Kingdom of Travancore, the Kingdom of Cochin, Malabar District, and South Canara of the British India. The Malabar District was annexed by the British through the Third Mysore War (1790–92) from Tipu Sultan. Before that, the Malabar District was under various kingdoms including the Zamorins of Calicut, Kingdom of Tanur, Arakkal kingdom, Kolathunadu, Valluvanad, and Palakkad Rajas.
Language resource management – Lexical markup framework, produced by ISO/TC 37, is the ISO standard for natural language processing (NLP) and machine-readable dictionary (MRD) lexicons. The scope is standardization of principles and methods relating to language resources in the contexts of multilingual communication.
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BabelNet is a multilingual lexical-semantic knowledge graph, ontology and encyclopedic dictionary developed at the NLP group of the Sapienza University of Rome under the supervision of Roberto Navigli. BabelNet was automatically created by linking Wikipedia to the most popular computational lexicon of the English language, WordNet. The integration is done using an automatic mapping and by filling in lexical gaps in resource-poor languages by using statistical machine translation. The result is an encyclopedic dictionary that provides concepts and named entities lexicalized in many languages and connected with large amounts of semantic relations. Additional lexicalizations and definitions are added by linking to free-license wordnets, OmegaWiki, the English Wiktionary, Wikidata, FrameNet, VerbNet and others. Similarly to WordNet, BabelNet groups words in different languages into sets of synonyms, called Babel synsets. For each Babel synset, BabelNet provides short definitions in many languages harvested from both WordNet and Wikipedia.
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In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. Word embeddings can be obtained using language modeling and feature learning techniques, where words or phrases from the vocabulary are mapped to vectors of real numbers.
UBY is a large-scale lexical-semantic resource for natural language processing (NLP) developed at the Ubiquitous Knowledge Processing Lab (UKP) in the department of Computer Science of the Technische Universität Darmstadt . UBY is based on the ISO standard Lexical Markup Framework (LMF) and combines information from several expert-constructed and collaboratively constructed resources for English and German.
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OntoLex is the short name of a vocabulary for lexical resources in the web of data (OntoLex-Lemon) and the short name of the W3C community group that created it.
Mona Talat Diab is a computer science professor and director of Carnegie Mellon University's Language Technologies Institute. Previously, she was a professor at George Washington University and a research scientist with Facebook AI. Her research focuses on natural language processing, computational linguistics, cross lingual/multilingual processing, computational socio-pragmatics, Arabic language processing, and applied machine learning.
References
- ↑ B.R. Chakravarthi, M. Arcan, J.P. McCrae, Improving Wordnets for Under-Resourced Languages Using Machine Translation, in: Proceedings of the 9th Global WordNet Conference, The Global WordNet Conference 2018 Committee, 2018.
- ↑ Chakravarthi, Bharathi Raja; Arcan, Mihael; McCrae, John P. (2019). "Comparison of Different Orthographies for Machine Translation of Under-Resourced Dravidian Languages". 2nd Conference on Language, Data and Knowledge (LDK 2019). Schloss Dagstuhl – Leibniz-Zentrum für Informatik: 6:1–6:14. doi: 10.4230/OASIcs.LDK.2019.6 .
- ↑ Prakash Babu, Yandrapati; Eswari, Rajagopal; K Nimmi. "CIA_NITT@Dravidian-CodeMix-FIRE2020:Malayalam-English. Code Mixed Sentiment Analysis Using Sentence BERT And Sentiment Features" (PDF). ceur-ws.org.
- ↑ Chakravarthi, Bharathi Raja; Jose, Navya; Suryawanshi, Shardul; Sherly, Elizabeth; McCrae, John Philip (2020). Beermann, Dorothee; Besacier, Laurent; Sakti, Sakriani; Soria, Claudia (eds.). "A Sentiment Analysis Dataset for Code-Mixed Malayalam-English". Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced Languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL). Marseille, France: European Language Resources association: 177–184. arXiv: 2006.00210 . ISBN 979-10-95546-35-1.
- ↑ Chakravarthi, Bharathi Raja; Arcan, Mihael; McCrae, John P. (2019). Arcan, Mihael; Turchi, Marco; Du, Jinhua; Shterionov, Dimitar; Torregrosa, Daniel (eds.). "WordNet Gloss Translation for Under-resourced Languages using Multilingual Neural Machine Translation". Proceedings of the Second Workshop on Multilingualism at the Intersection of Knowledge Bases and Machine Translation. Dublin, Ireland: European Association for Machine Translation: 1–7.
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