An LSTM-CRF Based Approach to Token-Level Metaphor Detection

Abstract

Automatic processing of figurative languages is gaining popularity in NLP community for their ubiquitous nature and increasing volume. In this era of web 2.0, automatic analysis of metaphors is important for their extensive usage. Metaphors are a part of figurative language that compares different concepts, often on a cognitive level. Many approaches have been proposed for automatic detection of metaphors, even using sequential models or neural networks. In this paper, we propose a method for detection of metaphors at the token level using a hybrid model of Bidirectional-LSTM and CRF. We used fewer features, as compared to the previous state-of-the-art sequential model. On experimentation with VUAMC, our method obtained an F-score of 0.674.

Neural Metaphor Detection in Context

“…Many neural models with various features and architectures were introduced in the 2018 VUA Metaphor Detection Shared Task. They include LSTM-based models and CRFs augmented by linguistic features, such as WordNet, POS tags, concreteness score, unigrams, lemmas, verb clusters, and sentence-length manipulation (Swarnkar and Singh, 2018;Pramanick et al, 2018;Mosolova et al, 2018;Bizzoni and Ghanimifard, 2018;Wu et al, 2018). Researchers also studied different word embeddings, such as embeddings trained from corpora representing different levels of language mastery (Stemle and Onysko, 2018) and binarized vectors that reflect the General Inquirer dictionary category of a word (Mykowiecka et al, 2018).…”

A Report on the 2018 VUA Metaphor Detection Shared Task

“…Baseline 2 is "all-16" in BeigmanKlebanov et al (2018). 6 https://github.com/bot-zen/naacl flp st MAP(Pramanick et al, 2018) used a hybrid architecture of Bi-directional LSTM and Conditional Random Fields (CRF) for metaphor detection, relying on features such as token, lemma and POS, and using word2vec embeddings trained on English Wikipedia. Specifically, the authors considered contextual information within a sentence for generating predictions.…”

Recent advances in neural metaphor processing: A linguistic, cognitive and social perspective

“…Most neural models treat metaphor identification as a sequence labelling task, outputing a sequence of metaphoricity labels for a sequence of input words (usually a sentence) (Bizzoni and Ghanimifard, 2018;Dankers et al, 2019;Gao et al, 2018;Gong et al, 2020;Mao et al, 2019;Mykowiecka et al, 2018;Pramanick et al, 2018;Su et al, 2020;Wu et al, 2018). The first sequence labelling systems typically represented an input sentence as a sequence of pre-trained word embeddings and produced a task-and context-specific sentence representation through bidirectional long short-term memory (BiLSTM) (Dankers et al, 2019;Gao et al, 2018;Mykowiecka et al, 2018;Pramanick et al, 2018). Bizzoni and Ghanimifard (2018) experimented with separating long sentences into smaller chunks, which led to a 6% increase in F-score when using a BiLSTM architecture.…”