Metaphor Detection using Context and Concreteness
We report the results of our system on the Metaphor Detection Shared Task at the Second Workshop on Figurative Language Processing 2020. Our model is an ensemble, utilising contextualised and static distributional semantic representations, along with word-type concreteness ratings. Using these features, it predicts word metaphoricity with a deep multi-layer perceptron. We are able to best the state-of-the-art from the 2018 Shared Task by an average of 8.0{\%} F1, and finish fourth in both sub-tasks in which we participate.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Augmenting Neural Metaphor Detection with Concreteness
The idea that a shift in concreteness within a sentence indicates the presence of a metaphor has been around for a while. However, recent methods of detecting metaphor that have relied on deep neural models have ignored …
SentenceDi-LSTM Contrast : A Deep Neural Network for Metaphor Detection
The contrast between the contextual and general meaning of a word serves as an important clue for detecting its metaphoricity. In this paper, we present a deep neural architecture for metaphor detection which exploits th…
POSTopic ModelsWord EmbeddingsSupervised Word-Level Metaphor Detection: Experiments with Concreteness and Reweighting of Examples
Integrating Vision and Language Datasets to Measure Word Concreteness
We present and take advantage of the inherent visualizability properties of words in visual corpora (the textual components of vision-language datasets) to compute concreteness scores for words. Our simple method does no…
Image CaptioningImage RetrievalQuestion AnsweringExploring Concreteness Through a Figurative Lens
Static concreteness ratings are widely used in NLP, yet a word's concreteness can shift with context, especially in figurative language such as metaphor, where common concrete nouns can take abstract interpretations. Whi…