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Stanford University · Symbolic Systems
Active 1995–2025
Arto Anttila is an Associate Professor of Linguistics at Stanford University, with a background that includes an M.A. from the University of Helsinki in General Linguistics and English Philology (1990) and a Ph.D. from Stanford University in Linguistics (1998). His academic appointments include serving as an Associate Professor in the Department of Linguistics. His research interests encompass cognitive science and natural language, focusing on linguistic theory and its applications. He has held various editorial roles in prominent linguistic journals, such as being a member of the editorial boards for Natural Language and Linguistic Theory, Virittäjä, Finno-Ugric Languages and Linguistics, Language and Linguistics Compass, Studies in Language Variation, and the Nordic Journal of Linguistics. Additionally, he has been involved with professional organizations, including serving as Secretary of the Linguistic Association of Finland and being a member of the Linguistic Society of America since 1992. His honors include being a Postdoctoral Fellow at the National University of Singapore, a Hellman Faculty Scholar at Stanford, an Internal Faculty Fellow at the Stanford Humanities Center, and an International Scholar at Kyung Hee University in Seoul. He also holds an adjunct position as a Professor (dosentti) in General Linguistics at the University of Helsinki, Finland.
What do harmony-based grammars exclude?
HAL (Le Centre pour la Communication Scientifique Directe) · 2025-01-01
International audience
Paradoxes of MaxEnt markedness
Proceedings of the Annual Meetings on Phonology · 2023-05-13
Giorgio Magri
Vivienne Fong
Stanford University
Scott Borgeson
Jong‐Bok Kim
Kyung Hee University
Štefan Beňuš
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Over the past two decades, theoretical linguistics has taken a probabilistic turn. Maximum entropy (ME) has been endorsed as a model of probabilistic phonology because of its classical guarantees for grammatical inference. Yet, little is known about the basic organizing principles of ME phonology beyond circumstantial evidence of ME’s ability to fit specific patterns of empirical frequencies. The study of ME typologies is difficult because they consist of infinitely many grammars that cannot be exhaustively listed and directly inspected. Uniform Probability Inequalities (Anttila and Magri 2018) are a new tool that solves the problem: they characterize cases where one phonological mapping has a probability smaller than another mapping and this probability inequality holds uniformly for every grammar in the typology. In other words, uniform probability inequalities are universals of probabilistic grammars. We present a new generalization about ME uniform probability inequalities and argue that they are phonologically paradoxical and prune the set of ME universals down to almost nothing. This suggests that ME is not a suitable model of phonology.
Sensitivity to string length and feature count subverts MaxEnt universals
HAL (Le Centre pour la Communication Scientifique Directe) · 2022-01-01
International audience
Tone and morphological level ordering in Dagaare
Phonology · 2022-08-01 · 1 citations
Abstract Dagaare is a language of northern Ghana and adjoining areas of Burkina Faso. There are two tones, H and L, and contrastive downstep H ! H that involves a non-automatic pitch drop between two H tones. The challenge is to explain the extensive morphological conditioning of tonal processes, including dissimilation, downstep and spreading. Our solution involves level ordering: tones are introduced at different morphological levels (stems, words and phrases) and later processes can make earlier processes opaque. Tonal differences between nouns (spreading) versus verbs (no spreading) and stems (dissimilation) versus words (downstep) arise from constraint ranking differences within and across levels. There are two kinds of downsteps: stem-level downsteps are underlying L tones affiliated with some morpheme; word-level downsteps are L tones inserted by a general process of word-final lowering. Only one downstep per word is allowed. If more would arise, the morphologically inner downstep blocks the morphologically outer downstep.
To Predict or to Memorize: Prominence in Inaugural Addresses
Proceedings of the Annual Meetings on Phonology · 2021
The assignment of phrasal prominence has been variously attributed to syntactic structure, part of speech, predictability, informativity, and speaker's intent. A recent account asserts that prominence is memorized on a by-word basis as Accent Ratio (AR), the likelihood that a word is accented (Nenkova et al. 2007). We examined whether AR outperforms the traditional predictors, in particular syntax and informativity, and if not, whether the traditional predictors shed light on the variance left unexplained by AR. We used a corpus of spoken American English consisting of the first inaugural addresses of six recent American presidents, hand-annotated for stress by two native English speakers. Regression models fitted to the data revealed that AR, syntax, and informativity all independently matter. Dividing the data into high-prominence and low-prominence tokens further revealed that AR and informativity are significant among low-prominence words, but only syntax is significant among high-prominence words. We conclude that although AR is a highly successful predictor, certain aspects of phrasal prominence require reference to syntax and informativity.
On the Phonology and Semantics of Deaccentuation
Proceedings of the Annual Meetings on Phonology · 2021 · 1 citations
The deaccentuation of given and/or repeated elements is familiar from many dialects of English. We propose that deaccentuation is essentially an optional postlexical phonological process of stress retraction triggered by two constraints: *Stress-Copy, which assigns a violation to a stress peak on a word with a segmentally identical copy in the left context, and Rightmost, which assigns a violation to every word between a stress peak and the right phrase edge. We quantify deaccentuation by defining it as being perceived with less stress than expected, where expected stress is calculated by an implementation of Liberman and Prince's (1977) phrasal stress algorithm. We provide empirical evidence for our analysis based on the first inaugurals of six former U.S. presidents.
Sentence stress in presidential speeches
De Gruyter eBooks · 2020 · 30 citations
Sentential prominence is not represented in writing, it is hard to measure phonetically, and it is highly variable, yet it undoubtedly exists. Here we report preliminary findings from our study of sentential prominence in the inaugural addresses of six U.S. presidents. We confirm the familiar hypothesis that sentential prominence has two sources (Jespersen 1920): it is partly mechanical and depends on syntax (Chomsky & Halle 1968, Liberman & Prince 1977, Cinque 1993) and partly meaningful in that it highlights informative material (Bolinger 1972). Both contribute independently to perceived prominence. Pursuing the view that sentential prominence is amatter of stress,we provide evidence for the linguistic reality of the Nuclear Stress Rule (Chomsky & Halle 1968) as well as the view that information coincides with stress peaks in good prose (Bolinger 1957).We also observe that part of speech matters to sentence stress: noun and adjective stresses are loud and mechanical; verb and function word stresses are soft and meaningful. We suggest that thismay explainwhy parts of speech differ inword phonology as well.
Equiprobable Mappings in Weighted Constraint Grammars
ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · 2020-01-01 · 2 citations
We show that MaxEnt is so rich that it can distinguish between any two different input-output mappings: there always exists a nonnegative weight vector that assigns them different MaxEnt probabilities. Stochastic HG instead does admit equiprobable mappings, namely mappings that have the same probability for every weight vector, and we give a complete formal characterization of their violation profiles. Empirically, we show that the predictions of MaxEnt and Stochastic HG differ for a grammar of Finnish secondary stress. We test the predictions of both models against a large internet corpus and find preliminary support for Stochastic HG and against MaxEnt.
Equiprobable mappings in weighted constraint grammars
2019-01-01 · 1 citations
We show that MaxEnt is so rich that it can distinguish between any two different mappings: there always exists a nonnegative weight vector which assigns them different MaxEnt probabilities. Stochastic HG instead does admit equiprobable mappings and we give a complete formal characterization of them. We compare these different predictions of the two frameworks on a test case of Finnish stress.
Does MaxEnt Overgenerate? Implicational Universals in Maximum Entropy Grammar.
HAL (Le Centre pour la Communication Scientifique Directe) · 2018-01-01 · 3 citations
International audience
Naomi Tachikawa Shapiro
Adams Bodomo
Andries W. Coetzee