Conditional Random Fields for Metaphor Detection

Mosolova, Anna; Bondarenko, Ivan; Fomin, Vadim

doi:10.18653/v1/w18-0915

Cited by 13 publications

(10 citation statements)

References 6 publications

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“…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).…”

Section: Related Workmentioning

confidence: 99%

Neural Metaphor Detection in Context

Gao¹,

Choi²,

Choi³

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

102

162

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We present end-to-end neural models for detecting metaphorical word use in context. We show that relatively standard BiLSTM models which operate on complete sentences work well in this setting, in comparison to previous work that used more restricted forms of linguistic context. These models establish a new state-of-the-art on existing verb metaphor detection benchmarks, and show strong performance on jointly predicting the metaphoricity of all words in a running text.

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Section: Related Workmentioning

confidence: 99%

Neural Metaphor Detection in Context

Gao¹,

Choi²,

Choi³

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

102

162

View full text Add to dashboard Cite

show abstract

“…nsu ai (Mosolova et al, 2018) used linguistic features based on unigrams, lemmas, POS tags, topical LDAs, concreteness, WordNet, VerbNet and verb clusters and trained a Conditional Random Field (CRF) model with gradient descent using the L-BFGS method to generate predictions.…”

Section: System Descriptionsmentioning

confidence: 99%

A Report on the 2018 VUA Metaphor Detection Shared Task

Leong¹,

Klebanov²,

Shutova³

2018

Proceedings of the Workshop on Figurative Language Processing

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As the community working on computational approaches to figurative language is growing and as methods and data become increasingly diverse, it is important to create widely shared empirical knowledge of the level of system performance in a range of contexts, thus facilitating progress in this area. One way of creating such shared knowledge is through benchmarking multiple systems on a common dataset. We report on the shared task on metaphor identification on the VU Amsterdam Metaphor Corpus conducted at the NAACL 2018 Workshop on Figurative Language Processing.

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“…Furthermore, the embeddings were frozen for the entire training period also to ensure a fair comparison. Similarly, for our CRF model from sklearn-crfsuite 73 , we trained the CRF model with monolingual (Setswana and IsiXhosa) FastText embeddings, and the best hyper-parameters are used to train a new CRF using cross-lingual embeddings. As a baseline of CRF, we also investigated Feature Engineering (FE) as input to CRF.…”

Section: Ner Modelmentioning

confidence: 99%

Zero-Shot Transfer Learning using Affix and Correlated Cross-Lingual Embeddings.

Modupe

Sindane

Marivate

2023

Preprint

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Learning morphologically supplemented embedding spaces using cross-lingual models has become an active area of research and facilitated many research breakthroughs in various applications such as machine translation, named entity recognition, document classification, and natural language inference. However, the field has not become customary for Southern African low-resourced languages. In this paper, we present, evaluate and benchmark a cohort of cross-lingual embeddings for the English-Southern African languages on two classification tasks: News Headlines Classification (NHC) and Named Entity Recognition (NER). Our methodology considers four agglutinative languages from the eleven official South African languages: Isixhosa, Sepedi, Sesotho, and Setswana. Canonical correlation analyses and VecMap are the two cross-lingual alignment strategies adopted for this study. Monolingual embeddings used in this work are Glove (source), and FastText (source and target) embeddings. Our results indicate that with enough comparable corpora, we can develop strong inter-joined representations between English and the considered Southern African languages. More specifically, the best zero-shot transfer results on the available Setswana NHC dataset were achieved using canonically correlated embeddings with Multi-layered perceptron as the training model (54.5% accuracy). Furthermore, our NER best performance was achieved using canonically correlated cross-lingual embeddings with Conditional Random Fields as the training model (96.4% F1 score). Collectively, this study’s results were competitive with the benchmarks of the explored NHC and NER datasets, on both zero-short NHC and NER tasks with our advantage being the use of very minimal resources.

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Conditional Random Fields for Metaphor Detection

Abstract: We present an algorithm for detecting metaphor in sentences which was used in Shared Task on Metaphor Detection by First Workshop on Figurative Language Processing. The algorithm is based on different features and Conditional Random Fields.

Cited by 13 publications

References 6 publications

Neural Metaphor Detection in Context

Neural Metaphor Detection in Context

A Report on the 2018 VUA Metaphor Detection Shared Task

Zero-Shot Transfer Learning using Affix and Correlated Cross-Lingual Embeddings.

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