--- title: "Donald Hindle — author profile and papers" description: "Donald Hindle: 2 papers on Elamur Research (1990–1993), h-index 20 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/donald-hindle/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/donald-hindle/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Donald Hindle # Donald Hindle Donald Hindle's papers here apply statistical methods to large bodies of automatically parsed text to learn facts about English words and how they combine. A recurring question is how far word meaning and syntactic preference can be recovered from patterns of use alone, without hand-built dictionaries or hierarchies. The typical material is Associated Press newswire, processed by the partial parser Fidditch, from which grammatical relations between words are extracted and scored. The work touches on [lexical semantics & word embeddings](https://md.research.elamur.ai/free/en/topics/lexical-semantics/) and on the predicate–argument relations studied under [semantic role labeling](https://md.research.elamur.ai/free/en/topics/semantic-role-labeling/). The most cited of these papers is [Noun Classification From Predicate-Argument Structures](https://md.research.elamur.ai/free/en/papers/hindle-1990-noun-classification-predicate-argument-structures/) (1990). It puts Harris's distributional hypothesis to a larger test than earlier studies by comparing nouns through the verbs that take them as subjects or objects. Each link between a noun and a verb is weighted by mutual information, so that frequent but uninformative arguments count for little. The parser leaves uncertain relations unfilled, so its mistakes mostly drop evidence rather than add false evidence. The second paper, [Structural Ambiguity and Lexical Relations](https://md.research.elamur.ai/free/en/papers/hindle-1993-structural-ambiguity-lexical-relations/) (1993), addresses whether a prepositional phrase after a verb and its object attaches to the verb or to the noun. It notes that structural principles such as Right Association and Minimal Attachment predict human choices poorly. In their place it uses word-specific preferences, gathered from verb–noun–preposition triples in parsed newswire. The later paper thus carries the corpus-based approach of the noun-classification study from word similarity over to syntactic disambiguation, with lexical association again doing the main work. The bylines of Hindle's papers point mainly to [AT&T](https://md.research.elamur.ai/free/en/companies/at-t/), spanning 1989 to 2002, and earlier to the [University of Pennsylvania](https://md.research.elamur.ai/free/en/universities/university-of-pennsylvania/), between 1977 and 1982. Donald Hindle has 2 papers in our library, in 1990–1993, most of them on [Semantic role labeling](https://md.research.elamur.ai/free/en/topics/semantic-role-labeling/), [Lexical semantics & word embeddings](https://md.research.elamur.ai/free/en/topics/lexical-semantics/). 2 papers in our library · 1990–1993 [OpenAlex](https://openalex.org/A5110136545) · [**h-index 20**](https://openalex.org/authors/A5110136545) · [43 works](https://openalex.org/works?filter=author.id:A5110136545) · [3,162 citations](https://openalex.org/works?filter=author.id:A5110136545&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 2 works in our library](https://md.research.elamur.ai/free/en/authors/donald-hindle/#papers) · [🔗 266 citations in our library](https://md.research.elamur.ai/free/en/authors/donald-hindle/cited-by/) · [⭐ Paper Star rank #2256](https://md.research.elamur.ai/free/en/paper-stars/page/23/#star-2256) 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=Donald+Hindle) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Donald+Hindle) · [dblp](https://dblp.org/search?q=Donald+Hindle) · [ACL Anthology](https://aclanthology.org/search/?q=Donald+Hindle) ## Affiliations [🏢**AT&T** Bedminster · United States 1989–2002 · 8 papers](https://md.research.elamur.ai/free/en/companies/at-t/) · [🎓**University of Pennsylvania** Philadelphia · United States 1977–1982 · 2 papers](https://md.research.elamur.ai/free/en/universities/university-of-pennsylvania/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. ## Topics [Semantic role labeling (1)](https://md.research.elamur.ai/free/en/topics/semantic-role-labeling/) · [Lexical semantics & word embeddings (1)](https://md.research.elamur.ai/free/en/topics/lexical-semantics/) ## Works by year ### 1990s [**1993** 1 paper](https://md.research.elamur.ai/free/en/papers/hindle-1993-structural-ambiguity-lexical-relations/) · [**1990** 1 paper](https://md.research.elamur.ai/free/en/papers/hindle-1990-noun-classification-predicate-argument-structures/) ## Papers The most cited first (OpenAlex and our library). 1. **[Noun Classification From Predicate-Argument Structures](https://md.research.elamur.ai/free/en/papers/hindle-1990-noun-classification-predicate-argument-structures/)** *(1990 · 665 citations)* Hindle tackles the problem of grouping English nouns into semantically related sets without hand-built "is-a" hierarchies, which are costly to create and hard to derive from dictionaries. 2. **[Structural Ambiguity and Lexical Relations](https://md.research.elamur.ai/free/en/papers/hindle-1993-structural-ambiguity-lexical-relations/)** *(1993 · 146 citations)* The authors take a classic case of structural ambiguity: where to attach a prepositional phrase that follows a verb and its object, to the verb or to the noun. ## Cited by Papers of our library cite their works 266 times (of the 140,884 papers whose bibliographies we read); [31 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/donald-hindle/cited-by/).