Interpreting metaphor is a hard but important problem in natural language processing that has numerous applications. One way to address this task is by finding a paraphrase that can replace the metaphorically used word in a given context. This approach has been previously implemented only within supervised frameworks, relying on manually constructed lexical resources, such as WordNet. In contrast, we present a fully unsupervised metaphor interpretation method that extracts literal paraphrases for metaphorical expressions from the Web. It achieves a precision of , which is high for an unsupervised paraphrasing approach. Moreover, the method significantly outperforms both the baseline and the selectional preference-based method of Shutova employed in an unsupervised setting.

Introduction

Metaphor is an important language tool that supports the creative nature of human thought and communication, enabling us to reason in novel, imaginative ways. Besides, it is a very common linguistic phenomenon manifested on average in every third sentence in general-domain text, according to corpus studies [2]. This makes computational processing of metaphor a pressing problem in NLP.

It has been previously shown that a number of real-world NLP applications could benefit from a metaphor processing component, e.g. machine translation [3], opinion mining [4], creative information retrieval [5] and recognizing textual entailment (RTE) [6]. Shutova [3] presents an example from machine translation (MT), where she studied the patterns of metaphor translation from English into Russian by the MT system Google Translate (http://translate.google.com/). She found that the MT system often produces literal translations of metaphorically used terms, rather than their literal interpretation, which makes the translated sentences semantically infelicitous in the target language. A metaphor processing component could help to avoid such errors. Ahmed [4] has shown that metaphor is often used when expressing strong opinions, which makes its automatic processing important for sentiment and opinion mining. Although existing Web information retrieval systems [7] can only search for literal matches of user queries, [5] proposes a figurative language retrieval model that can interpret metaphorical usage of language. Recognizing Textual Entailment (RTE), that involves recognizing whether one piece of text entails another is an important task in several natural language processing tasks such as question answering, text summarization and information extraction [8]. Agerri [6] shows that there is a significant correlation between the performance of textual entailment systems and their ability to interpret metaphorical expressions in texts.

Metaphors arise when one concept is viewed in terms of the properties of another. For example, consider the question How can I kill a process? [9]. Here, the computational process is viewed as being alive and therefore, its forced termination is perceived as killing. Metaphors can be explained via a systematic association, or a mapping, between two concepts or conceptual domains: the source and the target [10]. In our example, the computational process, which is the target concept, is viewed in terms of a living being, the source concept. The existence of such a mapping enables us to metaphorically describe the target domain using terminology borrowed from the source domain.

Several guidelines have been proposed in previous work to decide whether a particular word is used metaphorically or literally in a given context. For example, Shutoval et al. [2] annotate a verb as metaphorical if a more basic meaning of this verb can be established in a given context. As defined in the framework of MIP [11], basic meanings normally are: (1) more concrete; (2) related to bodily action; (3) more precise (as opposed to vague); and (4) historically order. Following [1], we define the task of metaphor interpretation as follows. Given a verb , used metaphorically with a noun , metaphor interpretation is the task of finding a non-metaphorical (i.e. literal) paraphrase for that expresses the same meaning as when used with . For example, to interpret the metaphorically used verb kill in the expression “kill a process” describing the noun process, one needs to extract the verbal paraphrase terminate.

Despite the vast potential applications of metaphor paraphrasing, it remains a challenging task for several reasons. Firstly, unlike many existing paraphrase extraction methods that derive paraphrases for nouns in isolation [12]–[18], we must identify paraphrases for the metaphorically used verb in the context of a noun . For example, although assassinate is a valid paraphrase for the verb kill from the point-of-view of traditional paraphrase extraction, it is not suitable for our purpose of interpreting the metaphorical phrase “kill a process” because the verb assassinate is not used with computer processes. Secondly, an extracted paraphrase for a metaphorical verb must be literal in order for it to be appropriate as an interpretation of the metaphorical verb. For example, consider the metaphorical expression “reach an agreement”. Although arrive at is a valid paraphrase for the verb reach in the traditional setting of paraphrase extraction, it is not suitable for the purpose of interpreting the metaphorical verb reach because “arrive at an agreement” is still a metaphorical expression. A better interpretation in this case would be attain.

Our method takes the above restrictions into account. Unlike previously proposed approaches for metaphor interpretation, it does not rely on manually compiled resources such as WordNet. Instead, it makes use of a Web search engine to generate a list of candidate paraphrases, and is thus fully unsupervised. The use of the Web for metaphor interpretation is beneficial for a number of reasons. First of all, this allows the method to find a larger number and a wider range of candidate interpretations, than a lexical resource-based method. In addition, it enables us to capture emerging novel and creative ways in which metaphors are used in the Internet, and can quickly adapt to change, as opposed to a method relying on static pre-compiled corpora.

Figure 1 illustrates the main components of our metaphor interpretation system. Given a metaphorical verb and its argument , we first extract numerous lexical patterns from the Web to explicitly represent the semantic relation between and . Lexical patterns are sequences of continuous words that are extracted from the local context of two words to represent the semantic relations that exist between those two words. For example, given the two words ostrich and bird, some of the lexical patterns that represent the semantic relation between those two words would be X is a large Y, Ys such as X, and a large Y such as X. Here, we use X and Y respectively to denote the two words ostrich and bird in a lexical pattern. We use a pattern scoring method to select the highly representative lexical patterns for a particular semantic relation. For example, given the metaphorical expression “to mend a marriage”, one of the lexical patterns extracted by the proposed method is to M their A, in which the placeholder variables M and A respectively denote the metaphorically used verb mend and its argument (object) marriage. We use bold italics to represent placeholder variables. Next, we query a Web search engine using the selected set of lexical patterns to find candidate paraphrases for the metaphorical expression. In our current example, some of the candidates we extract are: correct, repair, and save. Due to the noise in Web texts, there may be irrelevant paraphrases in the set of extracted candidates. Besides, some candidates may be used metaphorically again such as repair. To filter those out, we use a selectional preference-based model inspired by the work of Shutova [1]. In addition, we prioritize candidate paraphrases that have a high degree of lexical substitutability with the metaphorical word and show that this helps to avoid antonymous paraphrasing which is a common bottleneck in unsupervised lexical substitution. If a particular word can be substituted for another word in some context without altering the meaning of the context, then those two words are said to be lexically substitutable. Specifically, if a particular literal paraphrase can be used to re-discover its metaphorical counterpart for a given argument , then such are considered to indicate higher meaning similarity and are ranked above other candidate paraphrases. Finally, a ranked list of candidates according to their appropriateness as literal paraphrases of the metaphorical verb in the given context (argument noun) is produced by the system.

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larger image TIFF original image Download: Figure 1. Outline of the proposed method. Given a noun and a metaphorical verb, we download snippets that contain the noun and the metaphorically used verb. Next, lexical patterns that represent the semantic relation between the noun and the verb are extracted and scored according to their representativeness. Then we use the top scored patterns to extract candidate paraphrases for the verb by searching for those patterns on the Web. Selectional preference is used to filter out metaphorical candidate verbs and a substitutability test is conducted to identify correct candidate paraphrases. Finally, a ranked list of non-metaphorical paraphrases for the original metaphorically used verb is returned. https://doi.org/10.1371/journal.pone.0074304.g001

We compare the performance of selectional preference and lexical substitutability-based models and evaluate them on verb–subject and verb–direct object constructions containing metaphorical verbs using the dataset of Shutova [1]. Our method achieves a precision score of , which is high for an unsupervised approach to lexical substitution. In particular, the proposed method significantly outperforms both a baseline method and the selectional preference-based method of [1] employed in an unsupervised setting. Moreover, the use of the Web enables us to discover paraphrases that are not listed in manually compiled resources for the metaphorical senses of verbs, which was one of the limitations of the approach of [1]. We also use a larger dataset of automatically extracted metaphorical expressions to further evaluate the proposed method for its scalability and robustness. Our proposed method outperforms two baselines in this evaluation demonstrating its applicability in a real-world metaphor interpretation system.