New data-driven approaches to text simplification
Many texts we encounter in our everyday lives are lexically and syntactically very complex. This makes them difficult to understand for people with intellectual or reading impairments, and difficult for various natural language processing systems to process. This motivated the need for text simplification (TS) which transforms texts into their simpler variants. Given that this is still a relatively new research area, many challenges are still remaining. The focus of this thesis is on better understanding the current problems in automatic text simplification (ATS) and proposing new data-driven approaches to solving them. We propose methods for learning sentence splitting and deletion decisions, built upon parallel corpora of original and manually simplified Spanish texts, which outperform the existing similar systems. Our experiments in adaptation of those methods to different text genres and target populations report promising results, thus offering one possible solution for dealing with the scarcity of parallel corpora for text simplification aimed at specific target populations, which is currently one of the main issues in ATS. The results of our extensive analysis of the phrase-based statistical machine
SÌtajner, Sanja
automatic text simplification ; easy-to-read ; sentence splitting ; sentence deletion ; monolingual machine translation ; text adaptation
New data-driven approaches to text simplification. Many texts we encounter in our everyday lives are lexically and syntactically very complex. This makes them difficult to understand for people with intellectual or reading impairments, and difficult for various natural language processing systems to process. This motivated the need for text simplification (TS) which transforms texts into their simpler variants. Given that this is still a relatively new research area, many challenges are still remaining. The focus of this thesis is on better understanding the current problems in automatic text simplification (ATS) and proposing new data-driven approaches to solving them. We propose methods for learning sentence splitting and deletion decisions, built upon parallel corpora of original and manually simplified Spanish texts, which outperform the existing similar systems. Our experiments in adaptation of those methods to different text genres and target populations report promising results, thus offering one possible solution for dealing with the scarcity of parallel corpora for text simplification aimed at specific target populations, which is currently one of the main issues in ATS. The results of our extensive analysis of the phrase-based statistical machine
new data_driven approach text many text encounter everyday_life lexically syntactically complex make_difficult people intellectual reading impairment difficult various natural_language process system process motivate need text transform texts variant given still relatively_new area many challenge still_remain focus well current problem automatic text propose_new data_driven approach solve them propose method learn sentence split deletion decision build_upon parallel_corpus original manually spanish text exist similar system adaptation method different text genre target population report promising thus_offer possible_solution deal scarcity parallel corpora text aim specific target population currently one_main issue extensive_analysis phrase based statistical_machine