A survey on opinion summarization technique s for social media

The volume of data on the social media is huge and even keeps increasing. The need for efficient processing of this extensive information resulted in increasing research interest in knowledge engineering tasks such as Opinion Summarization. This survey shows the current opinion summarization challenges for social media, then the necessary pre-summarization steps like preprocessing, features extraction, noise elimination, and handling of synonym features. Next, it covers the various approaches used in opinion summarization like Visualization, Abstractive, Aspect based, Query-focused, Real Time, Update Summarization, and highlight other Opinion Summarization approaches such as Contrastive, Concept-based, Community Detection, Domain Specific, Bilingual, Social Bookmarking, and Social Media Sampling. It covers the different datasets used in opinion summarization and future work suggested in each technique. Finally, it provides different ways for evaluating opinion summarization

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

Examining the Part-of-speech Features in Assessing the Readability of Vietnamese Texts

The readability of the text plays a very important role in selecting appropriate materials for the level of the reader. Text readability in Vietnamese language has received a lot of attention in recent years, however, studies have mainly been limited to simple statistics at the level of a sentence length, word length, etc. In this article, we investigate the role of word-level grammatical characteristics in assessing the difficulty of texts in Vietnamese textbooks. We have used machine learning models (for instance, Decision Tree, K-nearest neighbor, Support Vector Machines, etc.) to evaluate the accuracy of classifying texts according to readability, using grammatical features in word level along with other statistical characteristics. Empirical results show that the presence of POS-level characteristics increases the accuracy of the classification by 2-4%