Universal Networking Language Expressions Research Articles

Bangla DeConverter for Extraction of BanglaText from Universal Networking Language

The people in Bangladesh and two states (i.e., Tripura and West Bengal) in India, which is about 230 million of the world population, use Bengali as their first dialect. However, very few numbers of resources and tools are available for this language. This paper presents a Bangla DeConverter to extract Bangla texts from Universal Networking Language (UNL). It explains and illustrates the different phases of the proposed Bangla DeConverter. The syntactic linearization, the implementation of the results of the proposed Bangla DeConverter, and the extraction of a Bangla sentence from UNL expressions are presented in this paper. The Bangla DeConverter has been tested on UNL expressions of 300 Bangla sentences using a Russian and English Language Server. The proposed system generates 90% syntactically and semantically correct Bangla sentences with a UNL Bilingual Evaluation Understudy (BLEU) score of 0.76.

SANSUNL: A Sanskrit to UNL Enconverter System

ABSTRACT Machine translation has become the most challenging as well as demanding field of research and as a result, to develop a fully automatic 100% correct machine translation system has not been achieved till date. More than 7000 of languages are being used worldwide for communication purposes and in India alone 22 languages have been recognized as official languages for communication purposes. Various efforts have been made to solve this problem by different researchers using different approaches. With the enhancement in technology and internet, automatic translation has become more important to share the knowledge. The authors in this paper described a mechanism of translation using machines for one of the ancient Indian Language Sanskrit to the latest computer understandable language Universal Networking Language (UNL). The proposed system uses two databases: one for the analysis purpose consists of more than 300 rules and the other for the generation of UNL which consists of about 1500 rules. For the performance evaluation, the authors selected 500 Sanskrit sentences in such a way that covers maximum UNL relations from various sources. The sentences are manually converted from English to Sanskrit language taken from Spanish Language center, Hindi to English language taken from IIT Bombay Hindi–UNL expressions and NCERT class 8th Sanskrit book titled “Ruchita Part 3”. The proposed system achieves a BLEU score of 0.85. The proposed system gives 93.18% efficiency in resolving UNL relation successfully.

Construction of Word Dictionary for Bangla Vowel Ended Roots and Its Verbal Inflexions in UNL Based Machine Translation Scheme

This paper focuses on the development of word dictionary of Bangla vowel ended roots and their verbal inflexions to incorporating them into an interlingua representation called Universal Networking Language (UNL) processors. A considerable amount of work has been done on the development of Bangla morphological analysis on verbs, nouns, prefixes and suffixes for machine translation. As far as the researchers are aware, no attempts, however, have been made to integrate the previous developments on Bangla vowel ended roots and inflexions for a concrete computational output. This paper attempts to bridge the gap on Bangla vowel ended roots and inflexions in the framework of UNL system aiming to produce a Bangla word dictionary for UNL. The paper analyzes the Bangla vowel ended roots and verbal inflexions and develops their formats in the UNL structure. Dictionary entries of all vowel ended roots and their inflexions are developed in order to generate associated verbs for sentences. These verbs can be used to convert Bangla native language sentences into UNL expressions using semantic rules, which are then converted into required native languages using the language specific generation rules. Conversion of a Bangla language sentence into UNL expression has also been shown in this paper.

Multilingual Automatic Translation Based on UNL: A Case Study for the Vietnamese Language

In the field of natural language processing, Universal Networking Language (UNL) has been used by various researchers as an inter-lingual approach to automatic machine translation. The UNL system consists of two main components, namely, EnConverter for converting text from a source language to UNL, and DeConverter for converting from UNL to a target language. Currently, many projects are researching how to apply UNL to different languages. In this paper, we introduce the tools that are UNL’s applications and discuss how to reuse them to encode a Vietnamese sentence into UNL expressions and decode UNL expressions into a Vietnamese sentence. The testing was done with about 1,000 Vietnamese sentences (a dictionary that includes 4573 entries and 3161 rules). In addition, we compare the proportion of sentences translated based on a direct method (Google Translator) and another one based on UNL.

E-Content Based UNL Deconverter Framework for Tamil and Hindi Language

The enormous growth in E-content information in the web requires the content to be available in the natural language. The demand in converting the E-content from the one natural language to another natural language have been increased recently. E-content information was obtained in the target language from the Interlingua representation such as Universal Networking Language (UNL) rather than from the source language. A framework towards conversion of a UNL expression related E-content into the tamil and hindi language was presented in this paper. This approach uses the dictionary to select target language words for UWs in the UNL expression. The morphological rules were created to modify the headwords owing to the target language. Also the Hidden Markov Model technique has been adopted to define the word order in the generated sentence. Keywords: E-content, tamil, hindi, deconverter, dictionary, morphological rules, Hidden Markov Model.

UNL-Based Machine Translation Scheme for Bangla Locative Case Constructs

Case structure plays a vital role in grammatical structure of any language during language translation. This paper presents an in-depth analysis of Bangla locative case constructs based on UNL (Universal Networking Language) machine translation scheme. A set of analysis rules have been defined to convert various Bangla locative case sentences into UNL expressions that can later be converted to any native language using language independent deconversion rules. We have demonstrated five different analysis rules and illustrated how each of them can effectively convert Bangla sentences to UNL expressions

Punjabi DeConverter for generating Punjabi from Universal Networking Language

DeConverter is core software in a Universal Networking Language (UNL) system. A UNL system has EnConverter and DeConverter as its two major components. EnConverter is used to convert a natural language sentence into an equivalent UNL expression, and DeConverter is used to generate a natural language sentence from an input UNL expression. This paper presents design and development of a Punjabi DeConverter. It describes five phases of the proposed Punjabi DeConverter, i.e., UNL parser, lexeme selection, morphology generation, function word insertion, and syntactic linearization. This paper also illustrates all these phases of the Punjabi DeConverter with a special focus on syntactic linearization issues of the Punjabi DeConverter. Syntactic linearization is the process of defining arrangements of words in generated output. The algorithms and pseudocodes for implementation of syntactic linearization of a simple UNL graph, a UNL graph with scope nodes and a node having un-traversed parents or multiple parents in a UNL graph have been discussed in this paper. Special cases of syntactic linearization with respect to Punjabi language for UNL relations like ‘and’, ‘or’, ‘fmt’, ‘cnt’, and ‘seq’ have also been presented in this paper. This paper also provides implementation results of the proposed Punjabi DeConverter. The DeConverter has been tested on 1000 UNL expressions by considering a Spanish UNL language server and agricultural domain threads developed by Indian Institute of Technology (IIT), Bombay, India, as gold-standards. The proposed system generates 89.0% grammatically correct sentences, 92.0% faithful sentences to the original sentences, and has a fluency score of 3.61 and an adequacy score of 3.70 on a 4-point scale. The system is also able to achieve a bilingual evaluation understudy (BLEU) score of 0.72.

Development of Dictionary Entries for the Bangla Vowel Ended Roots for Universal Networking Language

Conversion from another language to native language is highly demanding due to increasing the usage of web based application. Firstly, the respective sentence of a native language is converted to Universal Networking Language (UNL) expressions and then UNL expressions can be converted to any native language. UNL system is developed for most of the languages already but a very little effort has been made to convert Bangla language to UNL expressions. In this paper, we have described Bangla verb, Verb root, Verbal Inflections and the finally developed the dictionary entries for the vowel ended roots and their respective verbal inflections. General Terms Universal Networking Language, Bangla Verb, Verb Root, Verbal Inflections

Punjabi to UNL enconversion system

This paper reports the work for the EnConversion of input Punjabi sentences to an interlingua representation called Universal Networking Language (UNL). The UNL system consists of two main components, namely, EnConverter (used for converting the text from a source language to UNL) and DeConverter (used for converting the text from UNL to a target language). This paper discusses the framework for designing the EnConverter for Punjabi language with a special focus on generation of UNL attributes and relations from Punjabi source text. It also describes the working of Punjabi Shallow Parser used for the processing of the input sentence, which performs the tasks of Tokenizer, Morph-analyzer, Part-of-Speech Tagger and Chunker. This paper also considers the seven phases used in the process of EnConversion of input Punjabi text to UNL representation. The paper highlights the EnConversion analysis rules used for the EnConverter and indicates its usage in the generation of UNL expressions. This paper also covers the results of implementation of Punjabi EnConverter and its evaluation on sample UNL sentences available at Spanish Language Server. The accuracy of the developed system has also been presented in this paper.

Analysis and Generation of Bengali Case Structure Constructs for Universal Networking Language

Universal Networking Language (UNL) deals with the communication across nations of different languages and involves with many different related discipline such as linguistics, epistemology, computer science etc. It helps to overcome the language barrier among people of different nations to solve problems emerging from current globalization trends and geopolitical interdependence. Case structure analysis forms the foundation for any natural language processing task. In this paper we propose the computational analysis of the Bangla case structure and conversion of Bangla sentence to UNL expression. We also provide the implementation of our analysis taking one of the cases as an example.

Natural-Language-based Generation of Search Results in a Meaning-based Multilingual Search Engine

Most of the current search engines are keyword based. They do not consider the meaning of the query posed to them and hence can be ineffective. In contrast to this, Agro Explorer [1] - a multilingual, meaning based search engine in the agricultural domain first extracts the meaning of the query and then performs a search based on this extracted meaning. Hence, search can be carried out even if the language of the query is different from the language of the documents. The meaning is represented in the form of Universal Networking Language (UNL) Expressions. The search is carried out using UNL expression matching. The relevant documents are in the UNL form. The Deconverter converts these documents into the language of the user's choice using a lexicon and a rule base. In this paper, we will discuss the design of the Deconverter developed by us, with Marathi as the target language, for Agro Explorer. The deconversion proceeds through the following four stages:a) Syntax Planning; b) Lexical Replacement; c) Case Mark Insertion; d) Morph Generation.