SciELO - Scientific Electronic Library Online

 
vol.23 issue3Script Independent Morphological Segmentation for Arabic Maghrebi Dialects: An Application to Machine TranslationSentence Similarity Techniques for Short vs Variable Length Text using Word Embeddings author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • Have no similar articlesSimilars in SciELO

Share


Computación y Sistemas

On-line version ISSN 2007-9737Print version ISSN 1405-5546

Abstract

NAVALI, Samarth; KOLACHALAM, Jyothirmayi  and  VALA, Vanraj. Sentence Generation Using Selective Text Prediction. Comp. y Sist. [online]. 2019, vol.23, n.3, pp.991-998.  Epub Aug 09, 2021. ISSN 2007-9737.  https://doi.org/10.13053/cys-23-3-3252.

Text generation based on comprehensive datasets has been a well-known problem from several years. The biggest challenge is in creating a readable and coherent personalized text for specific user. Deep learning models have had huge success in the different text generation tasks such as script creation, translation, caption generation etc. Most of the existing methods require large amounts of data to perform simple sentence generation that may be used to greet the user or to give a unique reply. This research presents a novel and efficient method to generate sentences using a combination of Context Free Grammars and Hidden Markov Models. We have evaluated using two different methods, the first one is using a score similar to the BLEU score. The proposed implementation achieved 83% precision on the tweets dataset. The second method of evaluation being a subjective evaluation for the generated messages which is observed to be better than other methods.

Keywords : Text generation; sentence generation; context free grammar; CFG; hidden Markov model; HMM; selective text prediction.

        · text in English     · English ( pdf )