Learning Antonyms with Paraphrases and a Morphology-Aware Neural Network

Rajana, Sneha; Callison-Burch, Chris; Apidianaki, Marianna; Shwartz, Vered

doi:10.18653/v1/s17-1002

Cited by 7 publications

(6 citation statements)

References 21 publications

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“…"pre-existing conditions"), or in the form of natural language questions as seen in (Yu et al, 2019). Finally, identifying antonymy as studied in (Rajana et al, 2017) would be a valuable extension for more finegrained search results as synonyms and antonyms often occupy the same embedding space. 91…”

Section: Discussionmentioning

confidence: 99%

Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances

2021

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Domain-specific conceptual bases use key concepts to capture domain scope and relevant information. Conceptual bases serve as a foundation for various downstream tasks, including ontology construction, information mapping, and analysis. However, building conceptual bases necessitates domain awareness and takes time. Wikipedia navigational templates offer multiple articles on the same/similar domain. It is possible to use the templates to recognize fundamental concepts that shape the domain. Earlier work in this domain used Wikipedia's structured and unstructured data to construct open-domain ontologies, domain terminologies, and knowledge bases. We present a novel method for leveraging navigational templates to create domain-specific fuzzy conceptual bases in this work. Our system generates knowledge graphs from the articles mentioned in the template, which we then process using Wikidata and machine learning algorithms. We filter important concepts using fuzzy logic on network metrics to create a crude conceptual base. Finally, the expert helps by refining the conceptual base. We demonstrate our system using an example of RNA virus antiviral drugs.

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Section: Discussionmentioning

confidence: 99%

Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances

2021

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Section: Discussionmentioning

confidence: 99%

TopGuNN: Fast NLP Training Data Augmentation using Large Corpora

Iglesias-Flores¹,

Mishra²,

Patel³

et al. 2021

Proceedings of the Second Workshop on Data Science With Human in the Loop: Language Advances

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Acquiring training data for natural language processing systems can be expensive and timeconsuming. Given a few training examples crafted by experts, large corpora can be mined for thousands of semantically similar examples that provide useful variability to improve model generalization. We present TopGuNN, a fast contextualized k-NN retrieval system that can efficiently index and search over contextual embeddings generated from large corpora to easily retrieve new diverse training examples. TopGuNN is demonstrated for a semantic role labeling training data augmentation use case over the Gigaword corpus. Using approximate k-NN and an efficient architecture, TopGuNN performs queries over an embedding space of 4.63TB (approximately 1.5B embeddings) in less than a day.

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“…The most common application for antonym identification is opinion mining [29], [30]. The Deep Learning techniques could be used to identify the antonyms [31]. However, the simple language contradiction identification is not the object of our interest.…”

Section: Topic Modelling Approach and Antonyms Identificationmentioning

confidence: 99%

Automatic Extraction of Potentially Contradictory Parameters from Specific Field Patent Texts

Berdyugina

Cavallucci

2021

Creative Solutions for a Sustainable Development

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Nowadays, Altshuller contradiction matrix is used by many TRIZ practitioners, especially by beginners, thanks to its simplicity. However, establishing the link between user's specific problems issued from their experience in their domain of knowledge makes the use of the matrix often difficult. Applying specific terms of domain to formalized language of TRIZ tools necessitate an expertise that users often don't have time to build. Our previous finding based on Natural Languages Processing (NLP) tools and techniques, made possible to process a corpus of patents from a given field and thanks to Topic Modelling technique we achieved to link the technical parameters extracted out of patents to their context representation on a vector space in the text. However, this approach is not pertinent to identify the contradictory relations between extracted parameters. For this reason, we applied antonyms identification technique in order to better process the relations of oppositions between extracted parameters. The goal of this research it to extract automatically potential contradictions and set them up in an Altshuller-like matrix. Such an approach could facilitate the application of this famous TRIZ tool for practical user's problems. Moreover, setting up the matrix for patents of the new domain of knowledge could help to construct easily the state of art for these types of domain and keep the users informed without spending a lot of time and human resources for reading and analyzing large quantities of texts appearing continuously in each domains.

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Learning Antonyms with Paraphrases and a Morphology-Aware Neural Network

Cited by 7 publications

References 21 publications

Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances

Proceedings of the Second Workshop on Data Science with Human in the Loop: Language Advances

TopGuNN: Fast NLP Training Data Augmentation using Large Corpora

Automatic Extraction of Potentially Contradictory Parameters from Specific Field Patent Texts

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