This paper presents a computational model for nonlinear morphology with illustrations from Syriac and Arabic. The model is a multitiered one in that it allows for multiple lexical representations corresponding to the multiple tiers of autosegmental phonology. The model consists of three main components: (i) a lexicon, which is made of sublexica, with each sublexicon representing lexical material from a specific tier, (ii) a rewrite rules component that maps multiple lexical representations into one surface form and vice versa, and (iii) a morphotactic component that employs regular grammars. The system is finite-state in that lexica and rules can be represented by multitape finite-state machines.

Multitiered nonlinear morphology using multitape finite automata : A case study on syriac and arabic

Lack of text data is one of the main issues encountered by Malay speech researchers. Currently, there are few established Malay text corpora to aid in their research. Text corpora are essential due to its ability to provide empirical data for researchers in the field of linguistics and are useful to construct word lists for speech intelligibility test, speech analysis across genders and automatic speech recognition. The text corpora also need to mimic the natural phoneme of the language it represents. To accomplish this, we need to know the phonetic distribution of the language. The purpose of this research is to devise a phoneme distribution for the Malay language based on the transcripts obtained from fifty two Friday sermons. The Friday sermon transcripts were obtained through the official government website and then standardized by removing images and foreign letters; expanding acronyms and short forms; converting numbers and symbols to appropriate Malay words. The transcripts were then phonetically transcribed by first identifying the language rules and wrote a program based on those rules. The program was written using Personal Home Page (PHP) and the data were then stored into MySQL (Sequential Query Language). The data were then retrieved and compared to the Malay words used in news broadcast. In conclusion, the Malay used in Friday sermon and news broadcast differs in the usage of the phonemes /a/, /e/, /o/, /d/, /p/, /t∫/, /n/, /l/, /h/ and /r/.

Identification of phoneme and its distribution of malay language derived from Friday sermon transcripts

Le corpus DTC réunit des textes historiographiques grecs d’époque byzantine. Ces textes ont été analysés semi-automatiquement (lemmatisation et catégorisation morphosyntaxique) avec les outils informatiques et les ressources linguistiques du projet GREgORI (UCLouvain, Louvain-la-Neuve, Belgique) spécialisé dans le traitement automatique du grec et des langues de l’Orient chrétien. Une seconde analyse a été menée en collaboration avec l’entreprise Calfa (Paris, France) spécialisée dans le traitement de l’arménien et la mise en oeuvre d’approches basées sur l’intelligence artificielle. Cette seconde analyse est réalisée par un réseau de neurones. Cette étude compare et évalue les résultats produits par les deux méthodes et propose une approche hybride pour le traitement automatique des langues concernées. 

Analyse automatique du grec ancien par réseau de neurones. Évaluation sur le corpus De Thessalonica Capta

Streaming information flow allows identification of linguistic similarities between language pairs in real time as it relies on pattern recognition of grammar rules, semantics and pronunciation especially when analyzing so called international terms, syntax of the language family as well as tenses transitivity between the languages. Overall, it provides a backbone translation knowledge for building automatic translation system that facilitates processing any of various abstract entities which combine to specify underlying phonological, morphological, semantic and syntactic properties of linguistic forms and that act as the targets of linguistic rules and operations in a source language following professional human translator. Streaming data flow is a process of mining source data into target language transformation during which any inference impedes the system effectiveness by producing incorrect translation. We address a research problem of exploring streaming data from source-target parallels for detection of linguistic similarities between languages originated from different groups.

Source-target mapping model of streaming data flow for machine translation

All human language technology demands substantial quantities of data for system training and development, plus stable benchmark data to measure ongoing progress. While creation of high quality linguistic resources is both costly and time consuming, such data has the potential to profoundly impact not just a single evaluation program but language technology research in general. GALE’s challenging performance targets demand linguistic data on a scale and complexity never before encountered. Resources cover multiple languages (Arabic, Chinese, and English) and multiple genres -- both structured (newswire and broadcast news) and unstructured (web text, including blogs and newsgroups, and broadcast conversation). These resources include significant volumes of monolingual text and speech, parallel text, and transcribed audio combined with multiple layers of linguistic annotation, ranging from word aligned parallel text and Treebanks to rich semantic annotation.

Data Acquisition and Linguistic Resources

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

William Staderman

Author Tools

Chat about Author

Papers

Data Acquisition and Linguistic Resources