--- title: "An English Dictionary for Computerized Syntactic and Semantic" description: "The authors propose a model meant to reduce storage and search costs. Among them: 6." authors: ["Raoul N. Smith", "Edward Maxwell"] year: 1973 venue: "COLING 1973 Volume 1: Computational And Mathematical Linguistics: Proceedings of the International Conference on Computational Linguistics" source: "https://aclanthology.org/C73-1027/" licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)" licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/smith-1973-english-dictionary-computerized-syntactic-semantic/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/smith-1973-english-dictionary-computerized-syntactic-semantic/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [1973](https://md.research.elamur.ai/free/en/papers/1973/) / An English Dictionary for Computerized Syntactic and Semantic Processing Systems # An English Dictionary for Computerized Syntactic and Semantic Processing Systems **Authors:** Raoul N. Smith and Edward Maxwell · **Year:** 1973 · **Venue:** [COLING 1973 Volume 1: Computational And Mathematical Linguistics: Proceedings of the International Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/coling/) ([COLING 1973](https://md.research.elamur.ai/free/en/venues/coling/1973/)) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/) **Links:** [Publisher's page](https://aclanthology.org/C73-1027/) · [PDF](https://aclanthology.org/C73-1027.pdf) · [OpenAlex](https://openalex.org/W1965069860) **Citations:** 6 citations (OpenAlex, 2026-10-14) ## Summary The authors argue that natural-language processing systems of the early 1970s fail partly because their lexicons are tiny, usually only a few hundred items. They plan a large computerized English dictionary that can serve two roles: a component inside language-processing programs, and a database for syntactic and semantic research. The paper identifies the main obstacles. Conventional dictionaries are circular, a realistic lexicon needs very large storage, and pointer-chasing lookups are slow. It then surveys the linguistic information such a resource should hold. Its main sources are the Soviet "explanatory-combinatory" dictionary of Apresjan, Mel'čuk and Žolkovsky, Fillmore's case frames and lexical requirements, McCawley's semantic atoms, factivity, genericity and specificity, and graded (fuzzy) features from Ross and Lakoff. The authors propose a model meant to reduce storage and search costs. Their appendices show machine-sorted adjective definitions drawn from a standard dictionary. The work matters as an early argument that a lexicon should encode relations, argument structure and alternations, not only glosses. ## Key points - The authors estimate that a usable English dictionary needs at least 200,000 entries, including inflected forms. - They give storage arithmetic to show why naive designs fail: - About 20 definition words per entry would mean 4,000,000 stored words. - Chomsky-style entries would need roughly 20 features each. - Definitions in standard dictionaries are circular. Roughly 50% of the vocabulary shows up inside definitions (Olney, personal communication). - The paper reviews the Apresjan/Mel'čuk/Žolkovsky entry format, which has ten fields, including syntactic potential, lexical functions, lexical universe and synonym discrimination. It lists lexical functions such as: - Syn, Conv, Anti and Gen - S0, A0, Sinstr and Mult - Figur, Oper, Fin and Son - The paper draws on Fillmore's set of case roles, which covers extent, time, place, goal, source, object, instrument, experiencer and agent. It also lists his eight kinds of information a lexicon must expose. The planned uses are AI systems, computer-assisted instruction (PLATO), lexicon testing and information retrieval. ## Interesting ideas - The Conv lexical function pairs predicates that describe one situation with swapped roles, such as sell/buy, precede/follow and beat/lose to. Storing these pairs explicitly lets different surface sentences converge on the same meaning. - Oper links an event noun to its typical support verb, as in lend support, suffer defeat and experience a recession. The verb then adds little meaning, and the noun carries the event. - Syntactic potential records which arguments may surface as subject. For example, "break" lets the object become subject ("the window broke"), but "hit" does not. - Predicates can constrain the role of their subject. "Be happy" demands an experiencer, while "be warm" accepts an object, place, time or instrument, and adverbs like "personally" act as diagnostics. - Non-circular definitions built from a small set of semantic atoms, as in redden = cause to come to be red, give an exact paraphrase instead of a gloss. ## For Mova How these ideas could serve Mova, our small open language model — its files, stages and tests: - **I1** (world): A Conv table over verb classes would let "A sold B to C" and "C bought B from A" produce one transfer event with identical holdings. - **I2** (frames): When a UD object of a known Oper verb is an event noun, build the frame from the noun and map the subject to the noun's participant role. - **I3** (frames): Encoding causative and instrument-subject alternations per verb would let role assignment correctly label a patient or instrument that appears in nsubj position. - **I4** (frames): Role constraints on adjectival predicates could disambiguate the subject's role and flag odd readings, keeping explanations explicit. - **I5** (back): Decomposing verbs into atoms like CAUSE/BECOME/STATE would give the graph-to-text step a principled way to paraphrase or lexicalize events. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* 📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works 🧭 Topics (1): [English](https://md.research.elamur.ai/free/en/topics/english/) · 2,379 works ## Cite Cite as: Raoul N. Smith and Edward Maxwell. 1973. An English Dictionary for Computerized Syntactic and Semantic Processing Systems. COLING 1973 Volume 1: Computational And Mathematical Linguistics: Proceedings of the International Conference on Computational Linguistics. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{smith1973english, title = {{An English Dictionary for Computerized Syntactic and Semantic Processing Systems}}, author = {Raoul N. Smith and Edward Maxwell}, booktitle = {COLING 1973 Volume 1: Computational And Mathematical Linguistics: Proceedings of the International Conference on Computational Linguistics}, year = {1973}, doi = {10.3115/992532.992563}, url = {https://doi.org/10.3115/992532.992563} } ``` - [← previous Hedges: A Study in Meaning Criteria and the Logic of Fuzzy Concepts](https://md.research.elamur.ai/free/en/papers/lakoff-1973-hedges-study-meaning-criteria/) - [next → Anglo-Saxon Onomastics in the Old English Andreas](https://md.research.elamur.ai/free/en/papers/robinson-1973-anglo-saxon-onomastics-old-english/) - [↑ back to papers of 1973](https://md.research.elamur.ai/free/en/papers/1973/) *Updated 2 November 2026*