Good, Great, Excellent: Global Inference of Semantic                     Intensities

Melo, Gerard de; Bansal, Mohit

doi:10.1162/tacl_a_00227

Cited by 27 publications

(14 citation statements)

References 21 publications

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“…consider words like 'small' and 'minuscule' as synonyms. These efforts showed the potential of using techniques based on word embeddings (Kim and de Marneffe, 2013), web-scale data (Sheinman et al, 2013;De Melo and Bansal, 2013), or their combination (Shivade et al, 2015;Kim et al, 2016) to determine the relative intensity of different words on a scale. By focusing on the ordering between adjectives, however, these works do not shed light on how the meaning of such expressions is determined by contextual information.…”

Section: Related Workmentioning

confidence: 99%

Is the Red Square Big? MALeViC: Modeling Adjectives Leveraging Visual Contexts

Pezzelle¹,

Fernández²

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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This work aims at modeling how the meaning of gradable adjectives of size ('big', 'small') can be learned from visually-grounded contexts. Inspired by cognitive and linguistic evidence showing that the use of these expressions relies on setting a threshold that is dependent on a specific context, we investigate the ability of multi-modal models in assessing whether an object is 'big' or 'small' in a given visual scene. In contrast with the standard computational approach that simplistically treats gradable adjectives as 'fixed' attributes, we pose the problem as relational: to be successful, a model has to consider the full visual context. By means of four main tasks, we show that state-of-the-art models (but not a relatively strong baseline) can learn the function subtending the meaning of size adjectives, though their performance is found to decrease while moving from simple to more complex tasks. Crucially, models fail in developing abstract representations of gradable adjectives that can be used compositionally.

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Section: Related Workmentioning

confidence: 99%

Is the Red Square Big? MALeViC: Modeling Adjectives Leveraging Visual Contexts

Pezzelle¹,

Fernández²

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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“…Similarly, for query "fast runner", models should select a large value from column speed or a small value from column time. There are some related works that address the intensity of adjectives (De Melo and Bansal, 2013;Ruppenhofer et al, 2014;Sharma et al, 2017), however, no existing work studies the relations between the value polarities of adjective-noun phrasing pairs.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL

Liu¹,

Fang²,

Qian³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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One key component in text-to-SQL is to predict the comparison relations between columns and their values. To the best of our knowledge, no existing models explicitly introduce external common knowledge to address this problem, thus their capabilities of predicting comparison relations are limited beyond training data. In this paper, we propose to leverage adjective-noun phrasing knowledge mined from the web to predict the comparison relations in text-to-SQL. Experimental results on both the original and the re-split Spider dataset show that our approach achieves significant improvement over state-of-the-art methods on comparison relation prediction.

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“…None of these lexicons, however, contain multi-word phrases. Manually created sentiment lexicons can be used to automatically generate larger sentiment lexicons using semisupervised techniques (Esuli and Sebastiani, 2006;Turney and Littman, 2003;De Melo and Bansal, 2013;Tang et al, 2014). (See Mohammad (2016) for a survey on manually created and automatically generated affect resources.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Negators, Modals, and Degree Adverbs on Sentiment Composition

Kiritchenko¹,

Mohammad²

2016

Proceedings of the 7th Workshop on Computational Approaches To Subjectivity, Sentiment and Social Media Analysis

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Negators, modals, and degree adverbs can significantly affect the sentiment of the words they modify. Often, their impact is modeled with simple heuristics; although, recent work has shown that such heuristics do not capture the true sentiment of multi-word phrases. We created a dataset of phrases that include various negators, modals, and degree adverbs, as well as their combinations. Both the phrases and their constituent content words were annotated with real-valued scores of sentiment association. Using phrasal terms in the created dataset, we analyze the impact of individual modifiers and the average effect of the groups of modifiers on overall sentiment. We find that the effect of modifiers varies substantially among the members of the same group. Furthermore, each individual modifier can affect sentiment words in different ways. Therefore, solutions based on statistical learning seem more promising than fixed hand-crafted rules on the task of automatic sentiment prediction.

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Good, Great, Excellent: Global Inference of Semantic Intensities

Cited by 27 publications

References 21 publications

Is the Red Square Big? MALeViC: Modeling Adjectives Leveraging Visual Contexts

Is the Red Square Big? MALeViC: Modeling Adjectives Leveraging Visual Contexts

Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL

The Effect of Negators, Modals, and Degree Adverbs on Sentiment Composition

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