Related papers: Towards a Generic Framework for the Development of…
This paper describes the design and implementation of a Unicode-based GUISL (Graphical User Interface for Sindhi Language). The idea is to provide a software platform to the people of Sindh as well as Sindhi diasporas living across the…
Representing words and phrases into dense vectors of real numbers which encode semantic and syntactic properties is a vital constituent in natural language processing (NLP). The success of neural network (NN) models in NLP largely rely on…
This paper presents a novel combinational phonetic algorithm for Sindhi Language, to be used in developing Sindhi Spell Checker which has yet not been developed prior to this work. The compound textual forms and glyphs of Sindhi language…
Comprehensively searching for words in Sanskrit E-text is a non-trivial problem because words could change their forms in different contexts. One such context is sandhi or euphonic conjunctions, which cause a word to change owing to the…
Sindhi word segmentation is a challenging task due to space omission and insertion issues. The Sindhi language itself adds to this complexity. It's cursive and consists of characters with inherent joining and non-joining properties,…
Named Entity Recognition (NER) System aims to extract the existing information into the following categories such as: Persons Name, Organization, Location, Date and Time, Term, Designation and Short forms. Now, it is considered to be…
Writing systems of Indic languages have orthographic syllables, also known as complex graphemes, as unique horizontal units. A prominent feature of these languages is these complex grapheme units that comprise consonants/consonant…
This paper describes neural network based approaches to the process of the formation and splitting of word-compounding, respectively known as the Sandhi and Vichchhed, in Sanskrit language. Sandhi is an important idea essential to…
Searching for words in Sanskrit E-text is a problem that is accompanied by complexities introduced by features of Sanskrit such as euphonic conjunctions or sandhis. A word could occur in an E-text in a transformed form owing to the…
Developing culturally grounded multilingual AI systems remains challenging, particularly for low-resource languages. While synthetic data offers promise, its effectiveness in multilingual and multicultural contexts is underexplored. We…
Sandhi means to join two or more words to coin new word. Sandhi literally means `putting together' or combining (of sounds), It denotes all combinatory sound-changes effected (spontaneously) for ease of pronunciation. Sandhi-vicheda…
Disambiguation of word senses in context is easy for humans, but is a major challenge for automatic approaches. Sophisticated supervised and knowledge-based models were developed to solve this task. However, (i) the inherent Zipfian…
Urdu is a challenging language because of, first, its Perso-Arabic script and second, its morphological system having inherent grammatical forms and vocabulary of Arabic, Persian and the native languages of South Asia. This paper describes…
The performance of a text-to-speech (TTS) synthesis model depends on various factors, of which the quality of the training data is of utmost importance. Millions of data are collected around the globe for various languages, but resources…
Automated language processing is central to the drive to enable facilitated referencing of increasingly available Sanskrit E texts. The first step towards processing Sanskrit text involves the handling of Sanskrit compound words that are an…
In software, text is often represented using Unicode formats (UTF-8 and UTF-16). We frequently have to convert text from one format to the other, a process called transcoding. Popular transcoding functions are slower than state-of-the-art…
Recently, the supervised learning paradigm's surprisingly remarkable performance has garnered considerable attention from Sanskrit Computational Linguists. As a result, the Sanskrit community has put laudable efforts to build task-specific…
Statistical error Correction technique is the most accurate and widely used approach today, but for a language like Sindhi which is a low resourced language the trained corpora's are not available, so the statistical techniques are not…
Great progress has been made in unsupervised bilingual lexicon induction (UBLI) by aligning the source and target word embeddings independently trained on monolingual corpora. The common assumption of most UBLI models is that the embedding…
Unsupervised Domain Adaptation (UDA) addresses the problem of performance degradation due to domain shift between training and testing sets, which is common in computer vision applications. Most existing UDA approaches are based on…