We propose a new annotated corpus for metaphor interpretation by paraphrase, and a novel DNN model for performing this task. Our corpus consists of 200 sets of 5 sentences, with each set containing one reference metaphorical sentence, and four ranked candidate paraphrases. Our model is trained for a binary classification of paraphrase candidates, and then used to predict graded paraphrase acceptability. It reaches an encouraging 75% accuracy on the binary classification task, and high Pearson (.75) and Spearman (.68) correlations on the gradient judgment prediction task.
We present and compare two alternative deep neural architectures to perform word-level metaphor detection on text: a bi-LSTM model and a new structure based on recursive feedforward concatenation of the input. We discuss different versions of such models and the effect that input manipulation -specifically, reducing the length of sentences and introducing concreteness scores for words -have on their performance.
Metaphor is one of the most studied and widespread figures of speech and an essential element of individual style. In this paper we look at metaphor identification in Adjective-Noun pairs. We show that using a single neural network combined with pre-trained vector embeddings can outperform the state of the art in terms of accuracy. In specific, the approach presented in this paper is based on two ideas: a) transfer learning via using pre-trained vectors representing adjective noun pairs, and b) a neural network as a model of composition that predicts a metaphoricity score as output. We present several different architectures for our system and evaluate their performances. Variations on dataset size and on the kinds of embeddings are also investigated. We show considerable improvement over the previous approaches both in terms of accuracy and w.r.t the size of annotated training data.
We trace the evolution of Scientific English through the Late Modern period to modern time on the basis of a comprehensive corpus composed of the Transactions and Proceedings of the Royal Society of London, the first and longest-running English scientific journal established in 1665. Specifically, we explore the linguistic imprints of specialization and diversification in the science domain which accumulate in the formation of "scientific language" and field-specific sublanguages/registers (chemistry, biology etc.). We pursue an exploratory, data-driven approach using state-of-the-art computational language models and combine them with selected information-theoretic measures (entropy, relative entropy) for comparing models along relevant dimensions of variation (time, register). Focusing on selected linguistic variables (lexis, grammar), we show how we deploy computational language models for capturing linguistic variation and change and discuss benefits and limitations.
Text reuse refers to citing, copying or alluding text excerpts from a text resource to a new context. While detecting reuse in contemporary languages is well supported-given extensive research, techniques, and corporaautomatically detecting historical text reuse is much more difficult. Corpora of historical languages are less documented and often encompass various genres, linguistic varieties, and topics. In fact, historical text reuse detection is much less understood and empirical studies are necessary to enable and improve its automation. We present a linguistic analysis of text reuse in two ancient data sets. We contribute an automated approach to analyze how an original text was transformed into its reuse, taking linguistic resources into account to understand how they help characterizing the transformation. It is complemented by a manual analysis of a subset of the reuse. Our results show the limitations of approaches focusing on literal reuse detection. Yet, linguistic resources can effectively support understanding the non-literal text reuse transformation process. Our results support practitioners and researchers working on understanding and detecting historical reuse.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.