Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

Peer reviewe

Toward Formalization of Comprehensive Bilingual Dictionaries Creation Planning as Constraint Optimization Problem

This book explores key examples concerning the implementation of information technology and mathematical modeling to solve issues concerning environmental sustainability. The examples include using fuzzy weighted multivariate regression to predict the water quality index at Perak River in Malaysia; using wireless sensor networks (WSNs) for a remote river water pollution monitoring system; deriving biomass activated carbon from oil palm shell; and assessing the performance of a PV/T air solar collector. The book offers a valuable resource for all graduate students and researchers who are working in this rapidly growing area

USE OF LANGUAGE TECHNOLOGY TO IMPROVE MATCHING AND RETRIEVAL IN TRANSLATION MEMORY

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of PhilosophyCurrent Translation Memory (TM) tools lack semantic knowledge while matching. Most TM tools compute similarity at the string level, which does not take into account semantic aspects in matching. Therefore, semantically similar segments, which differ on the surface form, are often not retrieved. In this thesis, we present five novel and efficient approaches to incorporate advanced semantic knowledge in translation memory matching and retrieval. Two efficient approaches which use a paraphrase database to improve translation memory matching and retrieval are presented. Both automatic and human evaluations are conducted. The results on both evaluations show that paraphrasing improves matching and retrieval. An approach based on manually designed features extracted using NLP systems and resources is presented, where a Support Vector Machine (SVM) regression model is trained, which calculates the similarity between two segments. The approach based on manually designed features did not retrieve better matches than simple edit-distance. Two approaches for retrieving segments from a TM using deep learning are investigated. The first one is based on Long Short Term Memory (LSTM) networks, while the other one is based on Tree Structured Long Short Term Memory (Tree-LSTM) networks. Eight different models using different datasets and settings are trained. The results are comparable to a baseline which uses simple edit-distance