6 research outputs found
A Lightweight Regression Method to Infer Psycholinguistic Properties for Brazilian Portuguese
Psycholinguistic properties of words have been used in various approaches to
Natural Language Processing tasks, such as text simplification and readability
assessment. Most of these properties are subjective, involving costly and
time-consuming surveys to be gathered. Recent approaches use the limited
datasets of psycholinguistic properties to extend them automatically to large
lexicons. However, some of the resources used by such approaches are not
available to most languages. This study presents a method to infer
psycholinguistic properties for Brazilian Portuguese (BP) using regressors
built with a light set of features usually available for less resourced
languages: word length, frequency lists, lexical databases composed of school
dictionaries and word embedding models. The correlations between the properties
inferred are close to those obtained by related works. The resulting resource
contains 26,874 words in BP annotated with concreteness, age of acquisition,
imageability and subjective frequency.Comment: Paper accepted for TSD201
Data-driven sentence simplification: Survey and benchmark
Sentence Simplification (SS) aims to modify a sentence in order to make it easier to read and understand. In order to do so, several rewriting transformations can be performed such as replacement, reordering, and splitting. Executing these transformations while keeping sentences grammatical, preserving their main idea, and generating simpler output, is a challenging and still far from solved problem. In this article, we survey research on SS, focusing on approaches that attempt to learn how to simplify using corpora of aligned original-simplified sentence pairs in English, which is the dominant paradigm nowadays. We also include a benchmark of different approaches on common datasets so as to compare them and highlight their strengths and limitations. We expect that this survey will serve as a starting point for researchers interested in the task and help spark new ideas for future developments