Comprehending lyrics, as found in songs and poems, can pose a challenge to human and machine readers alike. This motivates the need for systems that can understand the ambiguity and jargon found in such creative texts, and provide commentary to aid readers in reaching the correct interpretation. We introduce the task of automated lyric annotation (ALA). Like text simplification, a goal of ALA i...

This paper examines the usefulness of semantic features based on word alignments for estimating the quality of text simplification. Specifically, we introduce seven types of alignment-based features computed on the basis of word embeddings and paraphrase lexicons. Through an empirical experiment using the QATS dataset (Štajner et al., 2016b), we confirm that we can achieve the state-of-the-art ...

We introduce a bootstrapping algorithm for regression that exploits word embedding models. We use it to infer four psycholinguistic properties of words: Familiarity, Age of Acquisition, Concreteness and Imagery and further populate the MRC Psycholinguistic Database with these properties. The approach achieves 0.88 correlation with humanproduced values and the inferred psycholinguistic features ...

Text simplification involves restructuring sentences by replacing particular syntactic constructs (like embedded clauses and appositives). The aim is to make the text easier to read for some target group (like aphasics and people with low reading ages) or easier to process by some program (like a parser or machine translation system). However, sentencelevel syntactic restructuring can wreak hav...

This paper introduces Japanese lexical simplification. Japanese lexical simplification is the task of replacing complex words in a given sentence with simple words to produce a new sentence without changing the original meaning of the sentence. We propose a method of supervised regression learning to estimate complexity ordering of words with statistical features obtained from two types of Japa...

This paper presents three systems that took part in the lexical simplification task at SEMEVAL 2012. Speculating on what the concept of simplicity might mean for a word, the systems apply different approaches to rank the given candidate lists. One of the systems performs second-best (statistically significant) and another one performs third-best out of 9 systems and 3 baselines. Notably, the th...

In recent years, several studies have approached the Text Simplification (TS) task as a machine translation (MT) problem. They report promising results in learning how to translate from ‘original’ to ‘simplified’ language using the standard phrasebased translation model. However, our results indicate that this approach works well only when the training dataset consists mostly of those sentence ...