WordNet

Miller, George A.

doi:10.3115/1075671.1075788

Cited by 44 publications

(5 citation statements)

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“…Using pairs encourages comparison and set reasoning within the pairs. Workers are asked to select two of the four pairs and write a sentence that is True for the selected pairs, but (Miller, 1993). Applied only to the non-animal synsets.…”

Section: Sentence Writingmentioning

confidence: 99%

A Corpus for Reasoning about Natural Language Grounded in Photographs

Suhr

Zhou²,

Zhang³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

299

241

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We introduce a new dataset for joint reasoning about natural language and images, with a focus on semantic diversity, compositionality, and visual reasoning challenges. The data contains 107,292 examples of English sentences paired with web photographs. The task is to determine whether a natural language caption is true about a pair of photographs. We crowdsource the data using sets of visually rich images and a compare-and-contrast task to elicit linguistically diverse language. Qualitative analysis shows the data requires compositional joint reasoning, including about quantities, comparisons, and relations. Evaluation using state-of-the-art visual reasoning methods shows the data presents a strong challenge. * Contributed equally. † Work done as an undergraduate at Cornell University. 1 In parts of this paper, we use the term compositional differently than it is commonly used in linguistics to refer to reasoning that requires composition. This type of reasoning often manifests itself in highly compositional language.The left image contains twice the number of dogs as the right image, and at least two dogs in total are standing.One image shows exactly two brown acorns in back-to-back caps on green foliage.

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Section: Sentence Writingmentioning

confidence: 99%

A Corpus for Reasoning about Natural Language Grounded in Photographs

Suhr

Zhou²,

Zhang³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

299

241

View full text Add to dashboard Cite

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“…SSL exploits both few labeled data and large unlabeled data [30]. Data augmentation in natural language processing (NLP) is generally based on knowledge resources (e.g., WordNet for token replacement and the Google Translate API for back-translation) [31]- [33]. These methods provide good, generalizable parameters using additional techniques and data.…”

Section: Related Workmentioning

confidence: 99%

Exploiting All Samples in Low-Resource Sentence Classification: Early Stopping and Initialization Parameters

Choi

Lee

2023

IEEE Access

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To improve deep-learning performance in low-resource settings, many researchers have redesigned model architectures or applied additional data (e.g., external resources, unlabeled samples). However, there have been relatively few discussions on how to make good use of small amounts of labeled samples, although it is potentially beneficial and should be done before applying additional data or redesigning models. In this study, we assume a low-resource setting in which only a few labeled samples (i.e., 30-100 per class) are available, and we discuss how to exploit them without additional data or model redesigns. We explore possible approaches in the following three aspects: training validation splitting, early stopping, and weight initialization. Extensive experiments are conducted on six public sentence classification datasets. Performance on various evaluation metrics (e.g., accuracy, loss, and calibration error) significantly varied depending on the approaches that were combined in the three aspects. Based on the results, we propose an integrated method, which is to initialize the model with a weight averaging method and use a nonvalidation stop method to train all samples. This simple integrated method consistently outperforms the competitive methods; e.g., the average accuracy of six datasets of this method was 1.8% higher than those of conventional validation-based methods. In addition, the integrated method further improves the performance when adapted to several state-of-the-art models that use additional data or redesign the network architecture (e.g., self-training and enhanced structural models). Our results highlight the importance of the training strategy and suggest that the integrated method can be the first step in the low-resource setting. This study provides empirical knowledge that will be helpful when dealing with low-resource data in future efforts. Our code is publicly available at https://github.com/DMCB-GIST/exploit_all_samples.

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“…For instance, assume the term "car" represents i w in a given i A D ∈ and that "car" ∈ w H . Synonymous terms such as "auto" and "automobile", as well as the hypernyms such as "motor vehicle" and "automotive vehicle" are obtained when using WrodNet [31] semantic resource. We would like to point out that although WordNet has been widely used in similar research works, we believe that it will not be sufficient in our work.…”

Section: Problem Formulationmentioning

confidence: 99%

A Knowledge-based Model for Semantic Oriented Contextual Advertising

Maree¹,

Hodrob²,

Belkhatir³

et al. 2020

KSII TIIS

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Proper and precise embedding of commercial ads within Webpages requires Ad-hoc analysis and understanding of their content. By the successful implementation of this step, both publishers and advertisers gain mutual benefits through increasing their revenues on the one hand, and improving user experience on the other. In this research work, we propose a novel multi-level context-based ads serving approach through which ads will be served at generic publisher websites based on their contextual relevance. In the proposed approach, knowledge encoded in domain-specific and generic semantic repositories is exploited in order to analyze and segment Webpages into sets of contextually-relevant segments. Semantically-enhanced indexes are also constructed to index ads based on their textual descriptions provided by advertisers. A modified cosine similarity matching algorithm is employed to embed each ad from the Ads repository into one or more contextually-relevant segments. In order to validate our proposal, we have implemented a prototype of an ad serving system with two datasets that consist of (11429 ads and 93 documents) and (11000 documents and 15 ads), respectively. To demonstrate the effectiveness of the proposed techniques, we experimentally tested the proposed method and compared the produced results against five baseline metrics that can be used in the context of ad serving systems. In addition, we compared the results produced by our system with other state-of-the-art models. Findings demonstrate that the accuracy of conventional ad matching techniques has improved by exploiting the proposed semantically-enhanced context-based ad serving model.

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WordNet

Cited by 44 publications

References 0 publications

A Corpus for Reasoning about Natural Language Grounded in Photographs

A Corpus for Reasoning about Natural Language Grounded in Photographs

Exploiting All Samples in Low-Resource Sentence Classification: Early Stopping and Initialization Parameters

A Knowledge-based Model for Semantic Oriented Contextual Advertising

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