--- title: "Eric Huang — author profile and papers" description: "Eric Huang: 2 papers on Elamur Research (2011–2012), h-index 18 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/eric-huang/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/eric-huang/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Eric Huang # Eric Huang Eric Huang works on neural representations of language, specifically how continuous vectors for words and longer stretches of text can be learned and then used for tasks such as sentiment prediction. Their papers here, from 2011 and 2012, share a concern with going beyond simple word counts and narrow context windows. They capture composition, word sense and topical context through learned vector spaces, ranking-style language models and clustering of word occurrences. The most cited of these works is [Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions](https://md.research.elamur.ai/free/en/papers/socher-2011-semi-supervised-recursive-autoencoders-predicting/) (2011). It builds sentence representations by repeatedly merging neighbouring word vectors into phrase vectors, choosing at each step the pair that is best reconstructed. This yields a tree structure and a fixed-length sentence vector without relying on a parser or hand-built sentiment lexica, and a classifier on the nodes predicts distributions over sentiment. The second, [Improving Word Representations via Global Context and Multiple Word Prototypes](https://md.research.elamur.ai/free/en/papers/huang-2012-improving-word-representations-global-context/) (2012), adds a document-level scoring term to a local window model, so that embeddings reflect topical cues as well as word order. It then clusters the contexts in which each word appears and learns a separate vector for each cluster, giving words several prototypes for different senses. The paper also introduces an evaluation in which word pairs are judged within whole sentences, with WordNet used to sample distinct senses. The 2012 paper is the newest work on the site. It moves the earlier focus on composing sentence meaning toward the question of how individual word vectors should handle polysemy and wider document context. The bylines place Huang at [Stanford University](https://md.research.elamur.ai/free/en/universities/stanford-university/) between 2011 and 2015. Both papers here were written with [Richard Socher](https://md.research.elamur.ai/free/en/authors/richard-socher/), [Christopher D. Manning](https://md.research.elamur.ai/free/en/authors/christopher-d-manning/) and [Andrew Y. Ng](https://md.research.elamur.ai/free/en/authors/andrew-y-ng/). The sentiment work falls under the topic [Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/). Eric Huang has 2 papers in our library, in 2011–2012, most of them on [Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/); often with [Andrew Y. Ng](https://md.research.elamur.ai/free/en/authors/andrew-y-ng/), [Christopher D. Manning](https://md.research.elamur.ai/free/en/authors/christopher-d-manning/), [Richard Socher](https://md.research.elamur.ai/free/en/authors/richard-socher/). 2 papers in our library · 2011–2012 [OpenAlex](https://openalex.org/A5080580204) · [ORCID](https://orcid.org/0000-0002-4745-2886) · [**h-index 18**](https://openalex.org/authors/A5080580204) · [51 works](https://openalex.org/works?filter=author.id:A5080580204) · [3,724 citations](https://openalex.org/works?filter=author.id:A5080580204&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/eric-huang/#papers) · [🔗 561 citations in our library](https://md.research.elamur.ai/free/en/authors/eric-huang/cited-by/) · [⭐ Paper Star rank #915](https://md.research.elamur.ai/free/en/paper-stars/page/10/#star-915) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Eric+Huang) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Eric+Huang) · [dblp](https://dblp.org/search?q=Eric+Huang) · [ACL Anthology](https://aclanthology.org/search/?q=Eric+Huang) ## Affiliations [🎓**Stanford University** Stanford · United States 2011–2015 · 6 papers](https://md.research.elamur.ai/free/en/universities/stanford-university/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed. ## Topics [Sentiment & emotion (1)](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) ## Works by year ### 2010s [**2012** 1 paper](https://md.research.elamur.ai/free/en/papers/huang-2012-improving-word-representations-global-context/) · [**2011** 1 paper](https://md.research.elamur.ai/free/en/papers/socher-2011-semi-supervised-recursive-autoencoders-predicting/) ## Papers The most cited first (OpenAlex and our library). 1. **[Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions](https://md.research.elamur.ai/free/en/papers/socher-2011-semi-supervised-recursive-autoencoders-predicting/)** *(2011 · 1,411 citations)* Most sentiment classifiers of the time relied on bag-of-words features, which ignore word order and composition. 2. **[Improving Word Representations via Global Context and Multiple Word Prototypes](https://md.research.elamur.ai/free/en/papers/huang-2012-improving-word-representations-global-context/)** *(2012 · 1,349 citations)* Most word embedding models learn one vector per word from a narrow window of neighbouring words. This ignores two things: words often have several unrelated or related senses, and the surrounding document carries topical cues. Huang et al. address both. They extend the Collobert and Weston ranking-loss language model… ## Co-authors here 4 co-authors with a page, the most papers together first. - [Andrew Y. Ng](https://md.research.elamur.ai/free/en/authors/andrew-y-ng/) ×2 - [Christopher D. Manning](https://md.research.elamur.ai/free/en/authors/christopher-d-manning/) ×2 - [Richard Socher](https://md.research.elamur.ai/free/en/authors/richard-socher/) ×2 - [Jeffrey Pennington](https://md.research.elamur.ai/free/en/authors/jeffrey-pennington/) ## Cited by Papers of our library cite their works 561 times (of the 140,884 papers whose bibliographies we read); [63 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/eric-huang/cited-by/).