--- title: "Sam Clark — author profile and papers" description: "Sam Clark, University of Washington: 2 papers on Elamur Research (2011–2013), h-index 3 — affiliations, topics, co-authors and who cites them in our…" canonical: "https://research.elamur.ai/free/en/authors/sam-clark/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/sam-clark/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Sam Clark # Sam Clark Sam Clark's papers here concern language processing for Twitter, where tools trained on newswire perform poorly because messages are short and noisy, with slang, irregular spelling and capitalization that cannot be relied on. The recurring question is how to adapt standard shallow-analysis components, chiefly [part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/) and [named entity recognition](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/), to such text when little annotated material exists. The methods typically combine a small amount of hand-labelled tweets with out-of-domain corpora, word clusters learned from unlabelled messages, and ways of reusing existing taggers. The most cited work is [Named Entity Recognition in Tweets](https://md.research.elamur.ai/free/en/papers/ritter-2011-named-entity-recognition-tweets-experimental/) (2011). It rebuilt the processing pipeline for Twitter step by step. A retrained tagger, a chunker and a classifier that judges whether a message's casing is informative supply features for entity segmentation, and entity types are assigned through distant supervision. [Twitter Part-of-Speech Tagging for All](https://md.research.elamur.ai/free/en/papers/derczynski-2013-twitter-part-speech-tagging-all/) (2013) evaluated several off-the-shelf taggers on tweets and classified their mistakes on unknown words. It then extended the Stanford tagger with treatment of rare and slang forms, priors for unambiguous tokens, and a bootstrapping scheme. In that scheme, an unlabelled tweet becomes training data only when two taggers with different tagsets agree on it. The 2013 tagging paper is also the newest here. It shifts attention from entity extraction toward the quality of training data. It uses agreement over an entire tweet as a filter for automatically labelled material and argues for reporting whole-sequence accuracy alongside token scores. Clark's papers here list the [University of Washington](https://md.research.elamur.ai/free/en/universities/university-of-washington/) between 2011 and 2013. [Alan Ritter](https://md.research.elamur.ai/free/en/authors/alan-ritter/) appears on both works. The entity recognition study was written with Mausam and Oren Etzioni, and the tagging paper with [Leon Derczynski](https://md.research.elamur.ai/free/en/authors/leon-derczynski/) and Kalina Bontcheva. Sam Clark has 2 papers in our library, in 2011–2013, most of them on [Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/), [Named entity recognition](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/); often with [Alan Ritter](https://md.research.elamur.ai/free/en/authors/alan-ritter/), [Kalina Bontcheva](https://md.research.elamur.ai/free/en/authors/kalina-bontcheva/), [Leon Derczynski](https://md.research.elamur.ai/free/en/authors/leon-derczynski/). 2 papers in our library · 2011–2013 [OpenAlex](https://openalex.org/A5004493815) · [**h-index 3**](https://openalex.org/authors/A5004493815) · [5 works](https://openalex.org/works?filter=author.id:A5004493815) · [2,001 citations](https://openalex.org/works?filter=author.id:A5004493815&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/sam-clark/#papers) · [🔗 358 citations in our library](https://md.research.elamur.ai/free/en/authors/sam-clark/cited-by/) · [⭐ Paper Star rank #1310](https://md.research.elamur.ai/free/en/paper-stars/page/14/#star-1310) 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=Sam+Clark) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Sam+Clark) · [dblp](https://dblp.org/search?q=Sam+Clark) · [ACL Anthology](https://aclanthology.org/search/?q=Sam+Clark) ## Affiliations [🎓**University of Washington** Seattle · United States 2011–2013 · 2 papers*current*](https://md.research.elamur.ai/free/en/universities/university-of-washington/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. ## Topics [Part-of-speech tagging (2)](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/) · [Named entity recognition (1)](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/) ## Works by year ### 2010s [**2013** 1 paper](https://md.research.elamur.ai/free/en/papers/derczynski-2013-twitter-part-speech-tagging-all/) · [**2011** 1 paper](https://md.research.elamur.ai/free/en/papers/ritter-2011-named-entity-recognition-tweets-experimental/) ## Papers The most cited first (OpenAlex and our library). 1. **[Named Entity Recognition in Tweets: An Experimental Study](https://md.research.elamur.ai/free/en/papers/ritter-2011-named-entity-recognition-tweets-experimental/)** *(2011 · 1,451 citations)* Standard NLP tools trained on newswire perform poorly on Twitter, where messages are short, noisy and full of nonstandard spelling and capitalization. 2. **[Twitter Part-of-Speech Tagging for All: Overcoming Sparse and Noisy Data](https://md.research.elamur.ai/free/en/papers/derczynski-2013-twitter-part-speech-tagging-all/)** *(2013 · 316 citations)* Standard PoS taggers trained on newswire perform badly on English tweets. Tweets are short, noisy and full of slang, and only a few small annotated tweet corpora exist. The authors compare four off-the-shelf taggers (TnT, SVMTool, TBL, Stanford) on Twitter data and categorise the errors they make on unknown words.… ## Co-authors here 5 co-authors with a page, the most papers together first. - [Alan Ritter](https://md.research.elamur.ai/free/en/authors/alan-ritter/) ×2 - [Kalina Bontcheva](https://md.research.elamur.ai/free/en/authors/kalina-bontcheva/) - [Leon Derczynski](https://md.research.elamur.ai/free/en/authors/leon-derczynski/) - [Mausam](https://md.research.elamur.ai/free/en/authors/mausam/) - [Oren Etzioni](https://md.research.elamur.ai/free/en/authors/oren-etzioni/) ## Cited by Papers of our library cite their works 358 times (of the 140,884 papers whose bibliographies we read); [35 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/sam-clark/cited-by/).