Current lexical simplification approaches rely heavily on heuristics and corpus level features that do not always align with human judgment. We create a human-rated word-complexity lexicon of 15,000 English words and propose a novel neural readability ranking model with a Gaussian-based feature vectorization layer that utilizes these human ratings to measure the complexity of any given word or phrase. Our model performs better than the state-of-the-art systems for different lexical simplification tasks and evaluation datasets. Additionally, we also produce SimplePPDB++, a lexical resource of over 10 million simplifying paraphrase rules, by applying our model to the Paraphrase Database (PPDB).
There have been previous attempts to use manually created lexica for simplification. For example, Elhadad and Sutaria used UMLS lexicon @cite_9 , a repository of technical medical terms; asked non-native speakers to answer multiple-choice questions corresponding to 12,000 English words to study each user's familiarity of vocabulary; and used a dictionary of 5,404 Japanese words based on the elementary school textbooks; used a list of 3,000 most common English words; Lee and Yeung used an ensemble of vocabulary lists of different complexity levels. However, to the best of our knowledge, there is no previous study on manually building a large word-complexity lexicon with human judgments that has shown substantial improvements on automatic simplification systems. We were encouraged by the success of the word-emotion lexicon @cite_45 and the word-happiness lexicon @cite_53 @cite_41 .
@cite_41 @cite_9 @cite_45 @cite_53
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We present a semi-supervised approach to the problem of paradigm induction from inflection tables. Our system extracts generalizations from inflection tables, representing the resulting paradigms in an abstract form. The process is intended to be language-independent, and to provide human-readable generalizations of paradigms. The tools we provide can be used by linguists for the rapid creation of lexical resources. We evaluate the system through an inflection table reconstruction task using Wiktionary data for German, Spanish, and Finnish. With no additional corpus information available, the evaluation yields per word form accuracy scores on inflecting unseen base forms in different languages ranging from 87.81 (German nouns) to 99.52 (Spanish verbs); with additional unlabeled text corpora available for training the scores range from 91.81 (German nouns) to 99.58 (Spanish verbs). We separately evaluate the system in a simulated task of Swedish lexicon creation, and show that on the basis of a small number of inflection tables, the system can accurately collect from a list of noun forms a lexicon with inflection information ranging from 100.0 correct (collect 100 words), to 96.4 correct (collect 1000 words). Using human evaluation of 100,000 words spread across 24 corpora in 10 languages diverse in origin and culture, we present evidence of a deep imprint of human sociality in language, observing that (i) the words of natural human language possess a universal positivity bias, (ii) the estimated emotional content of words is consistent between languages under translation, and (iii) this positivity bias is strongly independent of frequency of word use. Alongside these general regularities, we describe interlanguage variations in the emotional spectrum of languages that allow us to rank corpora. We also show how our word evaluations can be used to construct physical-like instruments for both real-time and offline measurement of the emotional content of large-scale texts. Deriving prior polarity lexica for sentiment analysis – where positive or negative scores are associated with words out of context – is a challenging task. Usually, a trade-off between precision and coverage is hard to find, and it depends on the methodology used to build the lexicon. Manually annotated lexica provide a high precision but lack in coverage, whereas automatic derivation from pre-existing knowledge guarantees high coverage at the cost of a lower precision. Since the automatic derivation of prior polarities is less time consuming than manual annotation, there has been a great bloom of these approaches, in particular based on the SentiWordNet resource. In this paper, we compare the most frequently used techniques based on SentiWordNet with newer ones and blend them in a learning framework (a so called ‘ensemble method’). By taking advantage of manually built prior polarity lexica, our ensemble method is better able to predict the prior value of unseen words and to outperform all the other SentiWordNet approaches. Using this technique we have built SentiWords , a prior polarity lexicon of approximately 155,000 words, that has both a high precision and a high coverage. We finally show that in sentiment analysis tasks, using our lexicon allows us to outperform both the single metrics derived from SentiWordNet and popular manually annotated sentiment lexica. SemEval 2016 task 11 involved determining whether words in a sentence were complex or simple for a cohort of people with English as a second language. Training data consisted of 200 annotated sentences, representing the combined judgements of 20 human annota- tors, such that if any annotator of the group labelled a word as complex, then it was con- sidered to be complex. Testing was based on single annotator judgements. Our system used a random forest classifier with a variety of features, the most important of which were term frequency statistics garnered from four large corpora, and style lexicons built on two large corpora. Minor features in the final system include the presence or absence of words in various readability word lists; many other features we tried were not successful. Our rank- ing amongst submitted systems did not reflect the strength of our system, due to submitting a far from optimal weighting between complex and simple, but we show that when a more ap- propriate weighting is used, our system ranks amongst the best submitted systems.