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Challenges in Finding Metaphorical Connections 寻找隐喻联系的挑战
Fig-Lang@NAACL-HLT Pub Date : 2018-06-01 DOI: 10.18653/v1/W18-0901
K. Gero, Lydia B. Chilton
{"title":"Challenges in Finding Metaphorical Connections","authors":"K. Gero, Lydia B. Chilton","doi":"10.18653/v1/W18-0901","DOIUrl":"https://doi.org/10.18653/v1/W18-0901","url":null,"abstract":"Poetry is known for its novel expression using figurative language. We introduce a writing task that contains the essential challenges of generating meaningful figurative language and can be evaluated. We investigate how to find metaphorical connections between abstract themes and concrete domains by asking people to write four-line poems on a given metaphor, such as “death is a rose” or “anger is wood”. We find that only 21% of poems successfully make a metaphorical connection. We present five alternate ways people respond to the prompt and release our dataset of 100 categorized poems. We suggest opportunities for computational approaches.","PeriodicalId":190853,"journal":{"name":"Fig-Lang@NAACL-HLT","volume":"402 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125371211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Literal, Metphorical or Both? Detecting Metaphoricity in Isolated Adjective-Noun Phrases 字面，隐喻，还是两者兼而有之?孤立形容词-名词短语的隐喻性检测
Fig-Lang@NAACL-HLT Pub Date : 2018-06-01 DOI: 10.18653/v1/W18-0904
A. Mykowiecka, M. Marciniak, A. Wawer
{"title":"Literal, Metphorical or Both? Detecting Metaphoricity in Isolated Adjective-Noun Phrases","authors":"A. Mykowiecka, M. Marciniak, A. Wawer","doi":"10.18653/v1/W18-0904","DOIUrl":"https://doi.org/10.18653/v1/W18-0904","url":null,"abstract":"The paper addresses the classification of isolated Polish adjective-noun phrases according to their metaphoricity. We tested neural networks to predict if a phrase has a literal or metaphorical sense or can have both senses depending on usage. The input to the neural network consists of word embeddings, but we also tested the impact of information about the domain of the adjective and about the abstractness of the noun. We applied our solution to English data available on the Internet and compared it to results published in papers. We found that the solution based on word embeddings only can achieve results comparable with complex solutions requiring additional information.","PeriodicalId":190853,"journal":{"name":"Fig-Lang@NAACL-HLT","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121882812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Di-LSTM Contrast : A Deep Neural Network for Metaphor Detection dii - lstm对比:隐喻检测的深度神经网络
Fig-Lang@NAACL-HLT Pub Date : 2018-06-01 DOI: 10.18653/v1/W18-0914
Krishnkant Swarnkar, Anil Kumar Singh
{"title":"Di-LSTM Contrast : A Deep Neural Network for Metaphor Detection","authors":"Krishnkant Swarnkar, Anil Kumar Singh","doi":"10.18653/v1/W18-0914","DOIUrl":"https://doi.org/10.18653/v1/W18-0914","url":null,"abstract":"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 this contrast. Additionally, we also use cost-sensitive learning by re-weighting examples, and baseline features like concreteness ratings, POS and WordNet-based features. The best performing system of ours achieves an overall F1 score of 0.570 on All POS category and 0.605 on the Verbs category at the Metaphor Shared Task 2018.","PeriodicalId":190853,"journal":{"name":"Fig-Lang@NAACL-HLT","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129560170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 18
Unsupervised Detection of Metaphorical Adjective-Noun Pairs 隐喻性形容词-名词对的无监督检测
Fig-Lang@NAACL-HLT Pub Date : 2018-06-01 DOI: 10.18653/v1/W18-0909
Malay Pramanick, Pabitra Mitra
{"title":"Unsupervised Detection of Metaphorical Adjective-Noun Pairs","authors":"Malay Pramanick, Pabitra Mitra","doi":"10.18653/v1/W18-0909","DOIUrl":"https://doi.org/10.18653/v1/W18-0909","url":null,"abstract":"Metaphor is a popular figure of speech. Popularity of metaphors calls for their automatic identification and interpretation. Most of the unsupervised methods directed at detection of metaphors use some hand-coded knowledge. We propose an unsupervised framework for metaphor detection that does not require any hand-coded knowledge. We applied clustering on features derived from Adjective-Noun pairs for classifying them into two disjoint classes. We experimented with adjective-noun pairs of a popular dataset annotated for metaphors and obtained an accuracy of 72.87% with k-means clustering algorithm.","PeriodicalId":190853,"journal":{"name":"Fig-Lang@NAACL-HLT","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130386679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Catching Idiomatic Expressions in EFL Essays 在英语作文中捕捉习语表达
Fig-Lang@NAACL-HLT Pub Date : 2018-06-01 DOI: 10.18653/v1/W18-0905
Michael Flor, Beata Beigman Klebanov
{"title":"Catching Idiomatic Expressions in EFL Essays","authors":"Michael Flor, Beata Beigman Klebanov","doi":"10.18653/v1/W18-0905","DOIUrl":"https://doi.org/10.18653/v1/W18-0905","url":null,"abstract":"This paper presents an exploratory study on large-scale detection of idiomatic expressions in essays written by non-native speakers of English. We describe a computational search procedure for automatic detection of idiom-candidate phrases in essay texts. The study used a corpus of essays written during a standardized examination of English language proficiency. Automatically-flagged candidate expressions were manually annotated for idiomaticity. The study found that idioms are widely used in EFL essays. The study also showed that a search algorithm that accommodates the syntactic and lexical exibility of idioms can increase the recall of idiom instances by 30%, but it also increases the amount of false positives.","PeriodicalId":190853,"journal":{"name":"Fig-Lang@NAACL-HLT","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130699749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Phrase-Level Metaphor Identification Using Distributed Representations of Word Meaning 基于语义分布表征的短语级隐喻识别
Fig-Lang@NAACL-HLT Pub Date : 2018-06-01 DOI: 10.18653/v1/W18-0910
Omnia Zayed, John P. McCrae, P. Buitelaar
{"title":"Phrase-Level Metaphor Identification Using Distributed Representations of Word Meaning","authors":"Omnia Zayed, John P. McCrae, P. Buitelaar","doi":"10.18653/v1/W18-0910","DOIUrl":"https://doi.org/10.18653/v1/W18-0910","url":null,"abstract":"Metaphor is an essential element of human cognition which is often used to express ideas and emotions that might be difficult to express using literal language. Processing metaphoric language is a challenging task for a wide range of applications ranging from text simplification to psychotherapy. Despite the variety of approaches that are trying to process metaphor, there is still a need for better models that mimic the human cognition while exploiting fewer resources. In this paper, we present an approach based on distributional semantics to identify metaphors on the phrase-level. We investigated the use of different word embeddings models to identify verb-noun pairs where the verb is used metaphorically. Several experiments are conducted to show the performance of the proposed approach on benchmark datasets.","PeriodicalId":190853,"journal":{"name":"Fig-Lang@NAACL-HLT","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122670372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
Detecting Figurative Word Occurrences Using Recurrent Neural Networks 使用循环神经网络检测比喻词的出现
Fig-Lang@NAACL-HLT Pub Date : 2018-06-01 DOI: 10.18653/v1/W18-0916
A. Mykowiecka, A. Wawer, M. Marciniak
{"title":"Detecting Figurative Word Occurrences Using Recurrent Neural Networks","authors":"A. Mykowiecka, A. Wawer, M. Marciniak","doi":"10.18653/v1/W18-0916","DOIUrl":"https://doi.org/10.18653/v1/W18-0916","url":null,"abstract":"The paper addresses detection of figurative usage of words in English text. The chosen method was to use neural nets fed by pretrained word embeddings. The obtained results show that simple solutions, based on words embeddings only, are comparable to complex solutions, using many sources of information which are not available for languages less-studied than English.","PeriodicalId":190853,"journal":{"name":"Fig-Lang@NAACL-HLT","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124775065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 11
Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection Bigrams和BiLSTMs两种序列隐喻检测的神经网络
Fig-Lang@NAACL-HLT Pub Date : 2018-06-01 DOI: 10.18653/v1/W18-0911
Yuri Bizzoni, M. Ghanimifard
{"title":"Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection","authors":"Yuri Bizzoni, M. Ghanimifard","doi":"10.18653/v1/W18-0911","DOIUrl":"https://doi.org/10.18653/v1/W18-0911","url":null,"abstract":"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 feed-forward 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.","PeriodicalId":190853,"journal":{"name":"Fig-Lang@NAACL-HLT","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131553004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 27
Neural Metaphor Detecting with CNN-LSTM Model CNN-LSTM模型的神经隐喻检测
Fig-Lang@NAACL-HLT Pub Date : 2018-06-01 DOI: 10.18653/v1/W18-0913
Chuhan Wu, Fangzhao Wu, Yubo Chen, Sixing Wu, Zhigang Yuan, Yongfeng Huang
{"title":"Neural Metaphor Detecting with CNN-LSTM Model","authors":"Chuhan Wu, Fangzhao Wu, Yubo Chen, Sixing Wu, Zhigang Yuan, Yongfeng Huang","doi":"10.18653/v1/W18-0913","DOIUrl":"https://doi.org/10.18653/v1/W18-0913","url":null,"abstract":"Metaphors are figurative languages widely used in daily life and literatures. It’s an important task to detect the metaphors evoked by texts. Thus, the metaphor shared task is aimed to extract metaphors from plain texts at word level. We propose to use a CNN-LSTM model for this task. Our model combines CNN and LSTM layers to utilize both local and long-range contextual information for identifying metaphorical information. In addition, we compare the performance of the softmax classifier and conditional random field (CRF) for sequential labeling in this task. We also incorporated some additional features such as part of speech (POS) tags and word cluster to improve the performance of model. Our best model achieved 65.06% F-score in the all POS testing subtask and 67.15% in the verbs testing subtask.","PeriodicalId":190853,"journal":{"name":"Fig-Lang@NAACL-HLT","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121396625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 73
Multi-Module Recurrent Neural Networks with Transfer Learning 具有迁移学习的多模块递归神经网络
Fig-Lang@NAACL-HLT Pub Date : 2018-06-01 DOI: 10.18653/v1/W18-0917
Filip Skurniak, M. Janicka, A. Wawer
{"title":"Multi-Module Recurrent Neural Networks with Transfer Learning","authors":"Filip Skurniak, M. Janicka, A. Wawer","doi":"10.18653/v1/W18-0917","DOIUrl":"https://doi.org/10.18653/v1/W18-0917","url":null,"abstract":"This paper describes multiple solutions designed and tested for the problem of word-level metaphor detection. The proposed systems are all based on variants of recurrent neural network architectures. Specifically, we explore multiple sources of information: pre-trained word embeddings (Glove), a dictionary of language concreteness and a transfer learning scenario based on the states of an encoder network from neural network machine translation system. One of the architectures is based on combining all three systems: (1) Neural CRF (Conditional Random Fields), trained directly on the metaphor data set; (2) Neural Machine Translation encoder of a transfer learning scenario; (3) a neural network used to predict final labels, trained directly on the metaphor data set. Our results vary between test sets: Neural CRF standalone is the best one on submission data, while combined system scores the highest on a test subset randomly selected from training data.","PeriodicalId":190853,"journal":{"name":"Fig-Lang@NAACL-HLT","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130710558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
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