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ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity

Research article (Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), 2017) · cited 79× · AI/ML
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ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity

Summary

ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity is a scholarly article[1].

Key Facts

  • ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity's instance of is recorded as scholarly article[2].

References

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity. Retrieved May 24, 2026, from https://4ort.xyz/entity/ecnu-at-semeval-2017-task-1-leverage-kernel-based-traditional-nlp-features-and-neural-networks-to-build-a-universal-mode
MLA “ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/ecnu-at-semeval-2017-task-1-leverage-kernel-based-traditional-nlp-features-and-neural-networks-to-build-a-universal-mode.
BibTeX @misc{4ortxyz_ecnu-at-semeval-2017-task-1-leverage-kernel-based-traditional-nlp-features-and-neural-networks-to-build-a-universal-mode_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity}}, year = {2026}, url = {https://4ort.xyz/entity/ecnu-at-semeval-2017-task-1-leverage-kernel-based-traditional-nlp-features-and-neural-networks-to-build-a-universal-mode}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity — https://4ort.xyz/entity/ecnu-at-semeval-2017-task-1-leverage-kernel-based-traditional-nlp-features-and-neural-networks-to-build-a-universal-mode (retrieved 2026-05-24)

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