---
title: "Commonsense Knowledge Base Completion — Li 2016"
description: "The authors build neural scorers that turn word embeddings into term representations, using either averaging or a… 51 references, cited by 77 papers here."
authors: ["Xiang Li", "Aynaz Taheri", "Lifu Tu", "Kevin Gimpel"]
year: 2016
venue: "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/P16-1137/"
doi: "10.18653/v1/p16-1137"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/li-2016-commonsense-knowledge-base-completion/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/li-2016-commonsense-knowledge-base-completion/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2016](https://md.research.elamur.ai/free/en/papers/2016/page/2/) / Commonsense Knowledge Base Completion

# Commonsense Knowledge Base Completion

**Authors:** [Xiang Li](https://md.research.elamur.ai/free/en/authors/xiang-li/), Aynaz Taheri, Lifu Tu and [Kevin Gimpel](https://md.research.elamur.ai/free/en/authors/kevin-gimpel/) · **Year:** 2016 · **Venue:** Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/P16-1137/) · [PDF](https://aclanthology.org/P16-1137.pdf) · [DOI 10.18653/v1/p16-1137](https://doi.org/10.18653/v1/p16-1137) · [OpenAlex](https://openalex.org/W2509019445)

**Citations:** 155 citations (OpenAlex, 2026-10-14) · [77](https://md.research.elamur.ai/free/en/papers/li-2016-commonsense-knowledge-base-completion/cited-by/) in our library, of the 140,884 papers whose bibliographies we read

## Summary
This work treats the low coverage of curated commonsense resources as a knowledge base completion (KBC) task. It focuses on ConceptNet, using only its Open Mind Common Sense (OMCS) tuples. Unlike Freebase-style KBC, where entities come from a closed inventory, ConceptNet terms are free-form phrases. A useful model must therefore score tuples whose terms never appeared in training. The authors build neural scorers that turn word embeddings into term representations, using either averaging or a bidirectional LSTM. These representations feed into either a bilinear relation-specific scorer or a one-hidden-layer DNN. Each model is trained to separate real tuples from automatically corrupted ones. All variants beat the baselines and perform similarly on held-out classification, and the simple averaging-based bilinear model is the recommended choice. In a human evaluation, new tuples ranked highly by this model were rated as good as medium-confidence ConceptNet entries. This suggests the approach can extend commonsense resources on the fly without relying on exact surface forms. Code, data, and models were released publicly.

## Key points
- Four architectures were compared: Bilinear AVG, Bilinear LSTM, DNN AVG, and DNN LSTM. The DNN LSTM reads both terms as a single sequence joined by a delimiter token.
- Of the training objectives tested, binary cross entropy with randomly generated negatives worked best and also trained fastest. Negatives were drawn within each mini-batch by swapping t1, R, or t2.
- Manual quality ratings used a 0–4 scale applied to the top 100 of 3000 sampled tuples:
  - high-confidence ConceptNet tuples: 3.68
  - medium-confidence ConceptNet tuples: 3.14
  - novel tuples ranked by Bilinear AVG: 3.20
  - novel tuples ranked by the MaxSim baseline: 2.74
- Candidate tuples were also mined from English Wikipedia sentences using POS-tag patterns and scored with Bilinear AVG. These were noisier, averaging 2.78, which is near "sometimes true."
- Unlike AnalogySpace or the approach of Angeli and Manning (2013), the models can score unseen terms without iterating over the training set at test time. The only requirement is that the terms' words are in the vocabulary.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [AI & machine learning](https://md.research.elamur.ai/free/en/shelves/ai/)

Topics: [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · [🕸️ Knowledge graphs & ontologies](https://md.research.elamur.ai/free/en/topics/knowledge-graphs/)

## Institutions

- [University of Chicago](https://md.research.elamur.ai/free/en/universities/university-of-chicago/) — University, Chicago, United States · 21 works · 49,048 citations
- [University of Illinois Chicago](https://md.research.elamur.ai/free/en/universities/university-of-illinois-chicago/) — University, Chicago, United States · 28 works · 18,688 citations
- [Toyota Technological Institute at Chicago](https://md.research.elamur.ai/free/en/universities/toyota-technological-institute-at-chicago/) — University, Chicago, United States · 22 works · 9,297 citations

## Cite

Cite as: Xiang Li, Aynaz Taheri, Lifu Tu and Kevin Gimpel. 2016. Commonsense Knowledge Base Completion. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{li2016commonsense,
  title = {{Commonsense Knowledge Base Completion}},
  author = {Xiang Li, Aynaz Taheri, Lifu Tu and Kevin Gimpel},
  booktitle = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2016},
  doi = {10.18653/v1/p16-1137},
  url = {https://doi.org/10.18653/v1/p16-1137}
}
```

- [← previous Learning to Understand Phrases by Embedding the Dictionary](https://md.research.elamur.ai/free/en/papers/hill-2016-learning-understand-phrases-embedding-dictionary/)
- [next → Membership Inference Attacks against Machine Learning Models](https://md.research.elamur.ai/free/en/papers/shokri-2016-membership-inference-attacks-against-machine/)
- [↑ back to papers of 2016](https://md.research.elamur.ai/free/en/papers/2016/page/2/)

## References

51 references, 15 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Basant Agarwal, Namita Mittal, Pooja Bansal, and Sonal Garg. 2015. Sentiment analysis using common-sense and context information. Comp. Int. and Neurosc. [find](https://scholar.google.com/scholar?q=Basant+Agarwal%2C+Namita+Mittal%2C+Pooja+Bansal%2C+and+Sonal+Garg%2E+2015%2E+Sentiment+analysis+using+common%2Dsense+and+context+information%2E+Comp%2E+Int%2E+and+Neurosc%2E)
2. Gabor Angeli and Christopher Manning. 2013. Philosophers are mortal: Inferring the truth of unseen facts. In Proc. of CoNLL. [find](https://scholar.google.com/scholar?q=Gabor+Angeli+and+Christopher+Manning%2E+2013%2E+Philosophers+are+mortal%3A+Inferring+the+truth+of+unseen+facts%2E+In+Proc%2E+of+CoNLL%2E)
3. Gabor Angeli and D. Christopher Manning. 2014. NaturalLI: Natural logic inference for common sense reasoning. In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=Gabor+Angeli+and+D%2E+Christopher+Manning%2E+2014%2E+NaturalLI%3A+Natural+logic+inference+for+common+sense+reasoning%2E+In+Proc%2E+of+EMNLP%2E)
4. Collin F Baker, Charles J Fillmore, and John B Lowe. 1998. The Berkeley FrameNet project. In Proc. of COLING. [our card](https://md.research.elamur.ai/free/en/papers/baker-1998-berkeley-framenet/)
5. Mohit Bansal, Kevin Gimpel, and Karen Livescu. 2014. Tailoring continuous word representations for dependency parsing. In Proc. of ACL. [our card](https://md.research.elamur.ai/free/en/papers/bansal-2014-tailoring-continuous-word-representations-dependency/)
6. Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 2008. Freebase: a collaboratively created graph database for structuring human knowledge. In Proc. of the ACM SIGMOD International Conference on Management of Data. [find](https://scholar.google.com/scholar?q=Kurt+Bollacker%2C+Colin+Evans%2C+Praveen+Paritosh%2C+Tim+Sturge%2C+and+Jamie+Taylor%2E+2008%2E+Freebase%3A+a+collaboratively+created+graph+database+for+structuring+human+knowledge%2E+In+Proc%2E+of+the+ACM+SIGMOD+Intern)
7. Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In Advances in NIPS. [find](https://scholar.google.com/scholar?q=Antoine+Bordes%2C+Nicolas+Usunier%2C+Alberto+Garcia%2DDuran%2C+Jason+Weston%2C+and+Oksana+Yakhnenko%2E+2013%2E+Translating+embeddings+for+modeling+multi%2Drelational+data%2E+In+Advances+in+NIPS%2E)
8. Antoine Bordes, Sumit Chopra, and Jason Weston. 2014a. Question answering with subgraph embeddings. In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=Antoine+Bordes%2C+Sumit+Chopra%2C+and+Jason+Weston%2E+2014a%2E+Question+answering+with+subgraph+embeddings%2E+In+Proc%2E+of+EMNLP%2E)
9. Antoine Bordes, Xavier Glorot, Jason Weston, and Yoshua Bengio. 2014b. A semantic matching energy function for learning with multi-relational data. Machine Learning, 94(2). [find](https://scholar.google.com/scholar?q=Antoine+Bordes%2C+Xavier+Glorot%2C+Jason+Weston%2C+and+Yoshua+Bengio%2E+2014b%2E+A+semantic+matching+energy+function+for+learning+with+multi%2Drelational+data%2E+Machine+Learning%2C+94%282%29%2E)
10. Erik Cambria, Daniel Olsher, and Kenneth Kwok. 2012. Sentic activation: A two-level affective common sense reasoning framework. In Proc. of AAAI. [find](https://scholar.google.com/scholar?q=Erik+Cambria%2C+Daniel+Olsher%2C+and+Kenneth+Kwok%2E+2012%2E+Sentic+activation%3A+A+two%2Dlevel+affective+common+sense+reasoning+framework%2E+In+Proc%2E+of+AAAI%2E)
11. Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam R Hruschka Jr, and Tom M Mitchell. 2010. Toward an architecture for never-ending language learning. In Proc. of AAAI. [find](https://scholar.google.com/scholar?q=Andrew+Carlson%2C+Justin+Betteridge%2C+Bryan+Kisiel%2C+Burr+Settles%2C+Estevam+R+Hruschka+Jr%2C+and+Tom+M+Mitchell%2E+2010%2E+Toward+an+architecture+for+never%2Dending+language+learning%2E+In+Proc%2E+of+AAAI%2E)
12. Luigi Di Caro, Alice Ruggeri, Loredana Cupi, and Guido Boella. 2015. Common-sense knowledge for natural language understanding: Experiments in unsupervised and supervised settings. In Proc. of AI*IA. [find](https://scholar.google.com/scholar?q=Luigi+Di+Caro%2C+Alice+Ruggeri%2C+Loredana+Cupi%2C+and+Guido+Boella%2E+2015%2E+Common%2Dsense+knowledge+for+natural+language+understanding%3A+Experiments+in+unsupervised+and+supervised+settings%2E+In+Proc%2E+of+AI%2AIA%2E)
13. John Duchi, Elad Hazan, and Yoram Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. JMLR. [find](https://scholar.google.com/scholar?q=John+Duchi%2C+Elad+Hazan%2C+and+Yoram+Singer%2E+2011%2E+Adaptive+subgradient+methods+for+online+learning+and+stochastic+optimization%2E+JMLR%2E)
14. Anthony Fader, Stephen Soderland, and Oren Etzioni. 2011. Identifying relations for open information extraction. In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=Anthony+Fader%2C+Stephen+Soderland%2C+and+Oren+Etzioni%2E+2011%2E+Identifying+relations+for+open+information+extraction%2E+In+Proc%2E+of+EMNLP%2E)
15. Alberto Garcı́a-Durán, Antoine Bordes, and Nicolas Usunier. 2014. Effective blending of two and three-way interactions for modeling multi-relational data. In Machine Learning and Knowledge Discovery in Databases. [find](https://scholar.google.com/scholar?q=Alberto+Garc%C4%B1%CC%81a%2DDura%CC%81n%2C+Antoine+Bordes%2C+and+Nicolas+Usunier%2E+2014%2E+Effective+blending+of+two+and+three%2Dway+interactions+for+modeling+multi%2Drelational+data%2E+In+Machine+Learning+and+Knowledge+Discovery+)
16. Matt Gardner, Partha Pratim Talukdar, Jayant Krishnamurthy, and Tom Mitchell. 2014. Incorporating vector space similarity in random walk inference over knowledge bases. In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=Matt+Gardner%2C+Partha+Pratim+Talukdar%2C+Jayant+Krishnamurthy%2C+and+Tom+Mitchell%2E+2014%2E+Incorporating+vector+space+similarity+in+random+walk+inference+over+knowledge+bases%2E+In+Proc%2E+of+EMNLP%2E)
17. Jonathan Gordon and Lenhart K Schubert. 2012. Using textual patterns to learn expected event frequencies. In Proc. of Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction. [find](https://scholar.google.com/scholar?q=Jonathan+Gordon+and+Lenhart+K+Schubert%2E+2012%2E+Using+textual+patterns+to+learn+expected+event+frequencies%2E+In+Proc%2E+of+Joint+Workshop+on+Automatic+Knowledge+Base+Construction+and+Web%2Dscale+Knowledge+Ex)
18. Jonathan Gordon and Benjamin Van Durme. 2013. Reporting bias and knowledge acquisition. In Proc. of Workshop on Automated Knowledge Base Construction. [find](https://scholar.google.com/scholar?q=Jonathan+Gordon+and+Benjamin+Van+Durme%2E+2013%2E+Reporting+bias+and+knowledge+acquisition%2E+In+Proc%2E+of+Workshop+on+Automated+Knowledge+Base+Construction%2E)
19. Jonathan Gordon, Benjamin Van Durme, and Lenhart K Schubert. 2010. Learning from the web: Extracting general world knowledge from noisy text. In Collaboratively-Built Knowledge Sources and AI. [find](https://scholar.google.com/scholar?q=Jonathan+Gordon%2C+Benjamin+Van+Durme%2C+and+Lenhart+K+Schubert%2E+2010%2E+Learning+from+the+web%3A+Extracting+general+world+knowledge+from+noisy+text%2E+In+Collaboratively%2DBuilt+Knowledge+Sources+and+AI%2E)
20. Jonathan Gordon. 2014. Inferential Commonsense Knowledge from Text. Ph.D. thesis, University of Rochester. [find](https://scholar.google.com/scholar?q=Jonathan+Gordon%2E+2014%2E+Inferential+Commonsense+Knowledge+from+Text%2E+Ph%2ED%2E+thesis%2C+University+of+Rochester%2E)
21. Kelvin Gu, John Miller, and Percy Liang. 2015. Traversing knowledge graphs in vector space. In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=Kelvin+Gu%2C+John+Miller%2C+and+Percy+Liang%2E+2015%2E+Traversing+knowledge+graphs+in+vector+space%2E+In+Proc%2E+of+EMNLP%2E)
22. Felix Hill, Roi Reichart, and Anna Korhonen. 2015. SimLex-999: Evaluating semantic models with (genuine) similarity estimation. Computational Linguistics. [our card](https://md.research.elamur.ai/free/en/papers/hill-2015-simlex-999-evaluating-semantic-models/)
23. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation. [find](https://scholar.google.com/scholar?q=Sepp+Hochreiter+and+Ju%CC%88rgen+Schmidhuber%2E+1997%2E+Long+short%2Dterm+memory%2E+Neural+computation%2E)
24. Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. In Proc. of CIKM. [find](https://scholar.google.com/scholar?q=Po%2DSen+Huang%2C+Xiaodong+He%2C+Jianfeng+Gao%2C+Li+Deng%2C+Alex+Acero%2C+and+Larry+Heck%2E+2013%2E+Learning+deep+structured+semantic+models+for+web+search+using+clickthrough+data%2E+In+Proc%2E+of+CIKM%2E)
25. Rodolphe Jenatton, Nicolas L. Roux, Antoine Bordes, and Guillaume R Obozinski. 2012. A latent factor model for highly multi-relational data. In Advances in NIPS. [find](https://scholar.google.com/scholar?q=Rodolphe+Jenatton%2C+Nicolas+L%2E+Roux%2C+Antoine+Bordes%2C+and+Guillaume+R+Obozinski%2E+2012%2E+A+latent+factor+model+for+highly+multi%2Drelational+data%2E+In+Advances+in+NIPS%2E)
26. Dan Klein and Christopher D. Manning. 2003. Accurate unlexicalized parsing. In Proc. of ACL. [our card](https://md.research.elamur.ai/free/en/papers/klein-2003-accurate-unlexicalized-parsing/)
27. Ni Lao, Tom Mitchell, and William W Cohen. 2011. Random walk inference and learning in a large scale knowledge base. In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=Ni+Lao%2C+Tom+Mitchell%2C+and+William+W+Cohen%2E+2011%2E+Random+walk+inference+and+learning+in+a+large+scale+knowledge+base%2E+In+Proc%2E+of+EMNLP%2E)
28. Douglas B Lenat and Ramanathan V Guha. 1989. Building large knowledge-based systems: representation and inference in the Cyc project. Addison-Wesley Longman Publishing Co., Inc. [find](https://scholar.google.com/scholar?q=Douglas+B+Lenat+and+Ramanathan+V+Guha%2E+1989%2E+Building+large+knowledge%2Dbased+systems%3A+representation+and+inference+in+the+Cyc+project%2E+Addison%2DWesley+Longman+Publishing+Co%2E%2C+Inc%2E)
29. Henry Lieberman, Alexander Faaborg, Waseem Daher, and José Espinosa. 2005. How to wreck a nice beach you sing calm incense. In Proc. of IUI. [find](https://scholar.google.com/scholar?q=Henry+Lieberman%2C+Alexander+Faaborg%2C+Waseem+Daher%2C+and+Jose%CC%81+Espinosa%2E+2005%2E+How+to+wreck+a+nice+beach+you+sing+calm+incense%2E+In+Proc%2E+of+IUI%2E)
30. Wang Ling, Chris Dyer, Alan W Black, Isabel Trancoso, Ramon Fermandez, Silvio Amir, Luis Marujo, and Tiago Luis. 2015. Finding function in form: Compositional character models for open vocabulary word representation. In Proc. of EMNLP. [our card](https://md.research.elamur.ai/free/en/papers/ling-2015-finding-function-form-compositional-character/)
31. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in NIPS. [our card](https://md.research.elamur.ai/free/en/papers/mikolov-2013-distributed-representations-words-phrases-their/)
32. George A Miller. 1995. WordNet: a lexical database for English. Communications of the ACM, 38(11). [our card](https://md.research.elamur.ai/free/en/papers/miller-1994-wordnet/)
33. Mike Mintz, Steven Bills, Rion Snow, and Daniel Jurafsky. 2009. Distant supervision for relation extraction without labeled data. In Proc of ACL. [find](https://scholar.google.com/scholar?q=Mike+Mintz%2C+Steven+Bills%2C+Rion+Snow%2C+and+Daniel+Jurafsky%2E+2009%2E+Distant+supervision+for+relation+extraction+without+labeled+data%2E+In+Proc+of+ACL%2E)
34. Arvind Neelakantan and Ming-Wei Chang. 2015. Inferring missing entity type instances for knowledge base completion: New dataset and methods. In Proc. of NAACL. [find](https://scholar.google.com/scholar?q=Arvind+Neelakantan+and+Ming%2DWei+Chang%2E+2015%2E+Inferring+missing+entity+type+instances+for+knowledge+base+completion%3A+New+dataset+and+methods%2E+In+Proc%2E+of+NAACL%2E)
35. Arvind Neelakantan, Benjamin Roth, and Andrew Mc-Callum. 2015. Compositional vector space models for knowledge base completion. In Proc. of ACL. [our card](https://md.research.elamur.ai/free/en/papers/neelakantan-2015-compositional-vector-space-models-knowledge/)
36. Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2011. A three-way model for collective learning on multi-relational data. In Proc. of ICML. [find](https://scholar.google.com/scholar?q=Maximilian+Nickel%2C+Volker+Tresp%2C+and+Hans%2DPeter+Kriegel%2E+2011%2E+A+three%2Dway+model+for+collective+learning+on+multi%2Drelational+data%2E+In+Proc%2E+of+ICML%2E)
37. Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2012. Factorizing YAGO: Scalable machine learning for linked data. In Proc. of WWW. [find](https://scholar.google.com/scholar?q=Maximilian+Nickel%2C+Volker+Tresp%2C+and+Hans%2DPeter+Kriegel%2E+2012%2E+Factorizing+YAGO%3A+Scalable+machine+learning+for+linked+data%2E+In+Proc%2E+of+WWW%2E)
38. Kristina Toutanova, Dan Klein, Christopher D Manning, and Yoram Singer. 2003. Feature-rich part-of-speech tagging with a cyclic dependency network. In Proc. of NAACL-HLT. [our card](https://md.research.elamur.ai/free/en/papers/toutanova-2003-feature-rich-part-speech-tagging/)
39. Kristina Toutanova, Danqi Chen, Patrick Pantel, Hoifung Poon, Pallavi Choudhury, and Michael Gamon. 2015. Representing text for joint embedding of text and knowledge bases. In Proc. of EMNLP. [our card](https://md.research.elamur.ai/free/en/papers/toutanova-2015-representing-text-joint-embedding-text/)
40. Luis von Ahn, Mihir Kedia, and Manuel Blum. 2006. Verbosity: a game for collecting common-sense facts. In Proc. of CHI. [find](https://scholar.google.com/scholar?q=Luis+von+Ahn%2C+Mihir+Kedia%2C+and+Manuel+Blum%2E+2006%2E+Verbosity%3A+a+game+for+collecting+common%2Dsense+facts%2E+In+Proc%2E+of+CHI%2E)
41. Robert West, Evgeniy Gabrilovich, Kevin Murphy, Shaohua Sun, Rahul Gupta, and Dekang Lin. 2014. Knowledge base completion via search-based question answering. In Proc. of WWW. [find](https://scholar.google.com/scholar?q=Robert+West%2C+Evgeniy+Gabrilovich%2C+Kevin+Murphy%2C+Shaohua+Sun%2C+Rahul+Gupta%2C+and+Dekang+Lin%2E+2014%2E+Knowledge+base+completion+via+search%2Dbased+question+answering%2E+In+Proc%2E+of+WWW%2E)
42. John Wieting, Mohit Bansal, Kevin Gimpel, Karen Livescu, and Dan Roth. 2015. From paraphrase database to compositional paraphrase model and back. Transactions of the ACL. [our card](https://md.research.elamur.ai/free/en/papers/wieting-2015-paraphrase-database-compositional-paraphrase-model/)
43. John Wieting, Mohit Bansal, Kevin Gimpel, and Karen Livescu. 2016. Towards universal paraphrastic sentence embeddings. In Proc. of ICLR. [our card](https://md.research.elamur.ai/free/en/papers/wieting-2015-towards-universal-paraphrastic-sentence-embeddings/)
44. Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2014. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575. [our card](https://md.research.elamur.ai/free/en/papers/yang-2014-embedding-entities-relations-learning-inference/)
45. Limin Yao, Sebastian Riedel, and Andrew McCallum. 2013. Universal schema for entity type prediction. In Proc. of Workshop on Automated Knowledge Base Construction. [find](https://scholar.google.com/scholar?q=Limin+Yao%2C+Sebastian+Riedel%2C+and+Andrew+McCallum%2E+2013%2E+Universal+schema+for+entity+type+prediction%2E+In+Proc%2E+of+Workshop+on+Automated+Knowledge+Base+Construction%2E)
46. Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global vectors for word representation. In Proc. of EMNLP. [our card](https://md.research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/)
47. Sebastian Riedel, Limin Yao, Andrew McCallum, and M. Benjamin Marlin. 2013. Relation extraction with matrix factorization and universal schemas. In Proc. of NAACL-HLT. [find](https://scholar.google.com/scholar?q=Sebastian+Riedel%2C+Limin+Yao%2C+Andrew+McCallum%2C+and+M%2E+Benjamin+Marlin%2E+2013%2E+Relation+extraction+with+matrix+factorization+and+universal+schemas%2E+In+Proc%2E+of+NAACL%2DHLT%2E)
48. Richard Socher, Danqi Chen, Christopher D Manning, and Andrew Ng. 2013. Reasoning with neural tensor networks for knowledge base completion. In Advances in NIPS. [our card](https://md.research.elamur.ai/free/en/papers/socher-2013-reasoning-neural-tensor-networks-knowledge/)
49. Robert Speer and Catherine Havasi. 2012. Representing general relational knowledge in ConceptNet 5. In Proc. of LREC. [find](https://scholar.google.com/scholar?q=Robert+Speer+and+Catherine+Havasi%2E+2012%2E+Representing+general+relational+knowledge+in+ConceptNet+5%2E+In+Proc%2E+of+LREC%2E)
50. Robert Speer, Catherine Havasi, and Henry Lieberman. 2008. AnalogySpace: Reducing the dimensionality of common sense knowledge. In Proc. of AAAI. [find](https://scholar.google.com/scholar?q=Robert+Speer%2C+Catherine+Havasi%2C+and+Henry+Lieberman%2E+2008%2E+AnalogySpace%3A+Reducing+the+dimensionality+of+common+sense+knowledge%2E+In+Proc%2E+of+AAAI%2E)
51. Robert Speer, Catherine Havasi, and Harshit Surana. 2010. Using verbosity: Common sense data from games with a purpose. In Proc. of FLAIRS. [find](https://scholar.google.com/scholar?q=Robert+Speer%2C+Catherine+Havasi%2C+and+Harshit+Surana%2E+2010%2E+Using+verbosity%3A+Common+sense+data+from+games+with+a+purpose%2E+In+Proc%2E+of+FLAIRS%2E)