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This paper seeks to examine the further integration of machine translation technologies with cross language information access in providing web users the capabilities of accessing information beyond language barriers. Machine translation and cross language information access are related technologies, and yet they have their own unique contributions in handling information in multiple languages. This paper aims to demonstrate that there are many opportunities to further integrate machine translation with cross language information access, and the combination can greatly empower web users in their information access.

Using English and Chinese as the language pair for studying, this paper looks at machine translation in query translation‐based cross language information access at multiple important aspects, which include query translation, relevance feedback, interactive cross language information access, out‐of‐vocabulary term translation, and data fusion. The goal is to obtain more insights about the wide range usages of machine translation in cross language information access, and to help the community to identify promising future directions for both machine translation and cross language access.

Machine translation can be applied effectively in many places in the whole cross language information access process. Queries translated by a machine translation system are high quality and are more robust in handling potential untranslated terms. Translation enhancement, a relevance feedback method using machine translation generated returned documents, is not only a valid technique by itself, but also helps to generate more robust cross language information access performance when combined with other relevance feedback techniques. Machine translation is also found to play a significant role in resolving untranslated terms and in data fusion.

This set of comparative empirical studies on integrating machine translation and cross language information access was performed on a common evaluation framework, and examined integration at multiple points of the cross language access process. The experimental results demonstrate the value of further integrating machine translation in cross language information access, and identify interesting future directions for both machine translation and cross language information access research.

According to the Indian Sign Language Research and Training Centre (ISLRTC), India has approximately 300 certified human interpreters to help people with hearing loss. This paper aims to address the issue of Indian Sign Language (ISL) sentence recognition and translation into semantically equivalent English text in a signer-independent mode.

This study presents an approach that translates ISL sentences into English text using the MobileNetV2 model and Neural Machine Translation (NMT). The authors have created an ISL corpus from the Brown corpus using ISL grammar rules to perform machine translation. The authors’ approach converts ISL videos of the newly created dataset into ISL gloss sequences using the MobileNetV2 model and the recognized ISL gloss sequence is then fed to a machine translation module that generates an English sentence for each ISL sentence.

As per the experimental results, pretrained MobileNetV2 model was proven the best-suited model for the recognition of ISL sentences and NMT provided better results than Statistical Machine Translation (SMT) to convert ISL text into English text. The automatic and human evaluation of the proposed approach yielded accuracies of 83.3 and 86.1%, respectively.

It can be seen that the neural machine translation systems produced translations with repetitions of other translated words, strange translations when the total number of words per sentence is increased and one or more unexpected terms that had no relation to the source text on occasion. The most common type of error is the mistranslation of places, numbers and dates. Although this has little effect on the overall structure of the translated sentence, it indicates that the embedding learned for these few words could be improved.

Sign language recognition and translation is a crucial step toward improving communication between the deaf and the rest of society. Because of the shortage of human interpreters, an alternative approach is desired to help people achieve smooth communication with the Deaf. To motivate research in this field, the authors generated an ISL corpus of 13,720 sentences and a video dataset of 47,880 ISL videos. As there is no public dataset available for ISl videos incorporating signs released by ISLRTC, the authors created a new video dataset and ISL corpus.

The purpose of this paper is to address the knowledge acquisition bottleneck problem in natural language processing by introducing a new rule‐based approach for the automatic acquisition of linguistic knowledge.

The author has developed a new machine translation methodology that only requires a bilingual lexicon and a parallel corpus of surface sentences aligned at the sentence level to learn new transfer rules.

A first prototype of a web‐based Japanese‐English translation system called Japanese‐English translation using corpus‐based acquisition of transfer (JETCAT) has been implemented in SWI‐Prolog, and a Greasemonkey user script to analyze Japanese web pages and translate sentences via Ajax. In addition, linguistic information is displayed at the character, word, and sentence level to provide a useful tool for web‐based language learning. An important feature is customization; the user can simply correct translation results leading to an incremental update of the knowledge base.

This paper focuses on the technical aspects and user interface issues of JETCAT. The author is planning to use JETCAT in a classroom setting to gather first experiences and will then evaluate a real‐world deployment; also work has started on extending JETCAT to include collaborative features.

The research has a high practical impact on academic language education. It also could have implications for the translation industry by superseding certain translation tasks and, on the other hand, adding value and quality to others.

The paper presents an extended version of the paper receiving the Emerald Web Information Systems Best Paper Award at iiWAS2010.

Normalization is an important step in all the natural language processing applications that are handling social media text. The text from social media poses a different kind of problems that are not present in regular text. Recently, a considerable amount of work has been done in this direction, but mostly in the English language. People who do not speak English code mixed the text with their native language and posted text on social media using the Roman script. This kind of text further aggravates the problem of normalizing. This paper aims to discuss the concept of normalization with respect to code-mixed social media text, and a model has been proposed to normalize such text.

The system is divided into two phases – candidate generation and most probable sentence selection. Candidate generation task is treated as machine translation task where the Roman text is treated as source language and Gurmukhi text is treated as the target language. Character-based translation system has been proposed to generate candidate tokens. Once candidates are generated, the second phase uses the beam search method for selecting the most probable sentence based on hidden Markov model.

Character error rate (CER) and bilingual evaluation understudy (BLEU) score are reported. The proposed system has been compared with Akhar software and RB\_R2G system, which are also capable of transliterating Roman text to Gurmukhi. The performance of the system outperforms Akhar software. The CER and BLEU scores are 0.268121 and 0.6807939, respectively, for ill-formed text.

It was observed that the system produces dialectical variations of a word or the word with minor errors like diacritic missing. Spell checker can improve the output of the system by correcting these minor errors. Extensive experimentation is needed for optimizing language identifier, which will further help in improving the output. The language model also seeks further exploration. Inclusion of wider context, particularly from social media text, is an important area that deserves further investigation.

The practical implications of this study are: (1) development of parallel dataset containing Roman and Gurmukhi text; (2) development of dataset annotated with language tag; (3) development of the normalizing system, which is first of its kind and proposes translation based solution for normalizing noisy social media text from Roman to Gurmukhi. It can be extended for any pair of scripts. (4) The proposed system can be used for better analysis of social media text. Theoretically, our study helps in better understanding of text normalization in social media context and opens the doors for further research in multilingual social media text normalization.

Existing research work focus on normalizing monolingual text. This study contributes towards the development of a normalization system for multilingual text.