# Novel Aficionados and Doppelg"angers: a referential task for semantic representations of individual entities

arXiv: https://arxiv.org/abs/2104.10270 · HTML: https://mlsanity.com/paper/2104.10270/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 54 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [oLMpics -- On what Language Model Pre-training Captures](https://mlsanity.com/paper/1912.13283/) (2019)
- [word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method](https://mlsanity.com/paper/1402.3722/) (2014)
- [Improving Coreference Resolution by Learning Entity-Level Distributed Representations](https://mlsanity.com/paper/1606.01323/) (2016)
- [Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking](https://mlsanity.com/paper/2003.05473/) (2020)
- [Fine-Grained Entity Typing for Domain Independent Entity Linking](https://mlsanity.com/paper/1909.05780/) (2019)
- [What's in a Name? Are BERT Named Entity Representations just as Good for any other Name?](https://mlsanity.com/paper/2007.06897/) (2020)
- [High-risk learning: acquiring new word vectors from tiny data](https://mlsanity.com/paper/1707.06556/) (2017)
- [Extraction and Analysis of Fictional Character Networks: A Survey](https://mlsanity.com/paper/1907.02704/) (2019)
- [Jointly Embedding Entities and Text with Distant Supervision](https://mlsanity.com/paper/1807.03399/) (2018)
- [Whodunnit? Crime Drama as a Case for Natural Language Understanding](https://mlsanity.com/paper/1710.11601/) (2017)
- [EntEval: A Holistic Evaluation Benchmark for Entity Representations](https://mlsanity.com/paper/1909.00137/) (2019)
- [What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue](https://mlsanity.com/paper/1905.06649/) (2019)
- [Instantiation](https://mlsanity.com/paper/1808.01662/) (2018)
- [Word Embeddings for Entity-annotated Texts](https://mlsanity.com/paper/1902.02078/) (2019)

## Abstract (arXiv)

In human semantic cognition, proper names (names which refer to individual entities) are harder to learn and retrieve than common nouns. This seems to be the case for machine learning algorithms too, but the linguistic and distributional reasons for this behaviour have not been investigated in depth so far. To tackle this issue, we show that the semantic distinction between proper names and common nouns is reflected in their linguistic distributions by employing an original task for distributional semantics, the Doppelg"anger test, an extensive set of models, and a new dataset, the Novel Aficionados dataset. The results indicate that the distributional representations of different individual entities are less clearly distinguishable from each other than those of common nouns, an outcome which intriguingly mirrors human cognition.