31 research outputs found
African Wordnet: isiZulu 1.0
No full textDeveloped using the expand model with Princeton WordNet 2.0 as basis. Each wordnet contains synsets with at least the following fields:\nWord form (lemma; synonym)\nID (linking to the Princeton Wordnet 2.0)\nPart of speech\nDomain\nSUMO/MILO classification\n\nAdditional data may include the following fields:\nUsage example(s)\nDefinition\nHypernym\nHyponym\nStamp\nNotes\nNon-lexicalisation\n\nPlease see https://africanwordnet.wordpress.com/ for all details on the projec
African Wordnet version 1.0
No full textDeveloped using the expand model with Princeton WordNet 3.1 as basis.
Please see https://africanwordnet.wordpress.com/ for all details on the project. This work builds on previously released data and is under active development. New releases will be made available at the end of every significant development phase
African Wordnet as a tool to identify semantic relatedness and semantic similarity
No full textSemantic relatedness and semantic similarity play a vital role in information retrieval of words in natural language processing applications. The multilingual nature of South Africa affords people a superficial (as opposed to an expert) knowledge of many languages, resulting in them finding it difficult to distinguish between semantic relatedness and semantic similarity. Computing semantic relatedness and semantic similarity using African Wordnet could help in developing a proper understanding of the meaning of certain words in the various languages. This article explores how African Wordnet can be used to identify semantic relatedness and semantic similarity. The focus of this article will be on isiZulu as one of the selected languages used in the African Wordnet Project. The purpose of the African Wordnet Project is the development of aligned wordnets for African languages spoken in South Africa. This article further discusses and analyses the examples of semantic relatedness and semantic similarity in the chosen language with a view to giving accounts of the two terms by providing examples and the usages of the words
Introducing the Arabic WordNet Project
No full textArabic is the official language of hundreds of millions of people in twenty Middle East and northern African countries, and is the religious language of all Muslims of various ethnicities around the world. Surprisingly little has been done in the field of computerised language and lexical resources. It is therefore motivating to develop an Arabic (WordNet) lexical resource that discovers the richness of Arabic as described in Elkateb (2005). This paper describes our approach towards building a lexical resource in Standard Arabic. Arabic WordNet (AWN) will be based on the design and contents of the universally accepted Princeton WordNet (PWN) and will be mappable straightforwardly onto PWN 2.0 and EuroWordNet (EWN), enabling translation on the lexical level to English and dozens of other languages. Several tools specific to this task will be developed. AWN will be a linguistic resource with a deep formal semantic foundation. Besides the standard wordnet representation of senses, word meanings are defined with a machine understandable semantics in first order logic. The basis for this semantics is the Suggested Upper Merged Ontology (SUMO) and its associated domain ontologies. We will greatly extend the ontology and its set of mappings to provide formal terms and definitions equivalent to each synset
Annotation of conceptual co-reference and text Mining the Qur'an
No full textThis research contributes to the area of corpus annotation and text mining by developing novel domain specific language resources. Most practical text mining applications restrict their domain. This research restricts the domain to the Qur'anic Text.
In this thesis, a number of pre-processing steps were undertaken and annotation information were added to the Qur'an. The raw Arabic Qur'an was pre-processed into morphological units using the Qur'anic Arabic Corpus (QAC). Qur'anic terms were indexed and converted into a vector space model using techniques in Information Retrieval (IR). In parallel, nearly 24,000 Qur'anic personal pronouns were annotated with information on their referents. These referents are consolidated and organized into a total of over 1,000 ontological concepts. Moreover, a dataset of nearly 8,000 pairs of related Qur'anic verses are compiled from books of scholarly commentary on the Qur'an. This vector space model, the pronoun tagging, the verse relatedness dataset, and the part-of-speech tags available in QAC all together served for a number of Qur'anic text mining applications which were rendered online for public use. Among these applications: lemma concordance, collocation, POS search of the Qur'an, verse similarity measures, concept clouds of a given verse, pronominal anaphora and Qur'anic chapter similarity.
Furthermore, machine learning experiments were conducted on automatic detection of verse similarity/relatedness as well as categorization of Qur'anic chapters based on their chronology of revelation. Domain specific linguistic features were investigated to induct learning algorithms. Results show that deep linguistic and world knowledge is needed to reach the human upper bound in certain computational tasks such as detecting text relatedness, question answering and textual entailment. However, many useful queries can be addressed using text mining techniques and layers of annotations made available through this research. The works presented here can be extended to include other similar texts like Hadith (i.e., saying of Prophet Muhammad), or other scriptures like the Gospels
African languages — is the writing on the screen?
No full textThe trends emerging in the natural language processing (NLP) of African languages spoken in South Africa, are explored in order to determine whether research in and development of such NLP is keeping abreast of international developments. This is done by investigating the past, present and future of NLP of African languages, keeping especially the multidisciplinary nature of the field and the role of the linguist in mind. A Human Sciences Research Council (HSRC) report of 1986, expressed concern about the backlog in South Africa regarding NLP, and called for dynamic action. As computational power increased and became less expensive, more interest began to be shown in NLP in South Africa. Pockets of expertise that have developed at various institutions over the past 20 years are discussed and the importance of cooperation in the field, across disciplines, is illustrated in this paper.
In order to facilitate coordinated action and prevent the duplication of language resources and the development of basic enabling technologies, the implementation of the concept of the Basic Language Resource Kit (BLARK) is recommended, while a new project, which aims to create a platform for WordNet development for African languages, is cited as prime example of international collaboration.Southern African Linguistics and Applied Language Studies 2007, 25(2): 169–18
Strategies for building wordnets for under-resourced languages: The case of African languages
No full textThe African Wordnet Project (AWN) aims at building wordnets for five African languages: Setswana, isiXhosa, isiZulu, Sesotho sa Leboa (also referred to as Sepedi or Northern Sotho) and Tshivenda. Currently, the so-called expand model, based on the structure of the English Princeton WordNet (PWN), is used to continually develop the African Wordnets manually. This is a labour-intensive work that needs to be performed by linguistic experts, guided by several considerations such as the level of lexicalisation of a term in the African language. Up to now, linguists were responsible for identifying and translating appropriate synsets without much help from electronic resources because in the case of African languages even basic resources such as computer readable and electronic bilingual wordlists are usually not freely available. Methods to speed up the manual development of synsets and ease the workload of the human language experts were recently investigated. These centred around utilising the minimal amount of information available in bilingual dictionaries to identify synsets in the PWN that should be included in the AWN, transferring information from dictionaries to the wordnet and presenting the potential synsets to linguists for final approval and inclusion in the wordnets. In this article, we describe the methodology developed for building the African Wordnets, a potentially significant resource for natural language processing applications. Available resources that could be taken advantage of and resources that had to be developed are investigated, and initial results and future plans are explained.</jats:p
Strategies for building wordnets for under-resourced languages: The case of African languages
No full textThe African Wordnet Project (AWN) aims at building wordnets for five African languages: Setswana, isiXhosa, isiZulu, Sesotho sa Leboa (also referred to as Sepedi or Northern Sotho) and Tshivenda. Currently, the so-called expand model, based on the structure of the English Princeton WordNet (PWN), is used to continually develop the African Wordnets manually. This is a labour-intensive work that needs to be performed by linguistic experts, guided by several considerations such as the level of lexicalisation of a term in the African language. Up to now, linguists were responsible for identifying and translating appropriate synsets without much help from electronic resources because in the case of African languages even basic resources such as computer readable and electronic bilingual wordlists are usually not freely available. Methods to speed up the manual development of synsets and ease the workload of the human language experts were recently investigated. These centred around utilising the minimal amount of information available in bilingual dictionaries to identify synsets in the PWN that should be included in the AWN, transferring information from dictionaries to the wordnet and presenting the potential synsets to linguists for final approval and inclusion in the wordnets. In this article, we describe the methodology developed for building the African Wordnets, a potentially significant resource for natural language processing applications. Available resources that could be taken advantage of and resources that had to be developed are investigated, and initial results and future plans are explained
Strategies for building wordnets for under-resourced languages: The case of African languages
No full textConference Contribution, Faculty of Humanities (SA Centre for Digital Language Resources (SADiLar)--Northwest University, Potchefstroom CampusThe African Wordnet Project (AWN) aims at building wordnets for five African languages: Setswana, isiXhosa, isiZulu, Sesotho sa Leboa (also referred to as Sepedi or Northern Sotho) and Tshivenda. Currently, the so-called expand model, based on the structure of the English Princeton WordNet (PWN), is used to continually develop the African Wordnets manually. This is a labour-intensive work that needs to be performed by linguistic experts, guided by several considerations such as the level of lexicalisation of a term in the African language. Up to now, linguists were responsible for identifying and translating appropriate synsets without much help from electronic resources because in the case of African languages even basic resources such as computer readable and electronic bilingual wordlists are usually not freely available. Methods to speed up the manual development of synsets and ease the workload of the human language experts were recently investigated. These centred around utilising the minimal amount of information available in bilingual dictionaries to identify synsets in the PWN that should be included in the AWN, transferring information from dictionaries to the wordnet and presenting the potential synsets to linguists for final approval and inclusion in the wordnets. In this article, we describe the methodology developed for building the African Wordnets, a potentially significant resource for natural language processing applications. Available resources that could be taken advantage of and resources that had to be developed are investigated, and initial results and future plans are explained
On the evaluation and improvement of arabic wordnet coverage and usability
No full textThe final publication is available at Springer via http://dx.doi.org/10.1007/s10579-013-9237-0[EN] Built on the basis of the methods developed for Princeton WordNet and EuroWordNet, Arabic WordNet (AWN) has been an interesting project which combines WordNet structure compliance with Arabic particularities. In this paper, some AWN shortcomings related to coverage and usability are addressed. The use of AWN in question/answering (Q/A) helped us to deeply evaluate the resource from an experience-based perspective. Accordingly, an enrichment of AWN was built by semi-automatically extending its content. Indeed, existing approaches and/or resources developed for other languages were adapted and used for AWN. The experiments conducted in Arabic Q/A have shown an improvement of both AWN coverage as well as usability. Concerning coverage, a great amount of named entities extracted from YAGO were connected with corresponding AWN synsets. Also, a significant number of new verbs and nouns (including Broken Plural forms) were added. In terms of usability, thanks to the use of AWN, the performance for the AWN-based Q/A application registered an overall improvement with respect to the following three measures: accuracy (+9.27 % improvement), mean reciprocal rank (+3.6 improvement) and number of answered questions (+12.79 % improvement).The work presented in Sect. 2.2 was done in the framework of the bilateral Spain-Morocco AECID-PCI C/026728/09 research project. The research of the two first authors is done in the framework of the PROGRAMME D'URGENCE project (grant no. 03/2010). The research of the third author is done in the framework of WIQEI IRSES project (grant no. 269180) within the FP 7 Marie Curie People, DIANA-APPLICATIONS-Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) research project and VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems. 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