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Remembering Individuals and Remembering Scenes

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New Frontiers in Artificial Intelligence (JSAI-isAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13856))

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Abstract

In the object position of certain intensional transitive verbs (paradigmatically: remember), DPs are semantically ambiguous between individuals and scenes [= scenes that saliently feature these individuals]. This ambiguity cuts across the familiar intensionality-related distinctions (esp. specific/non-specific, referentially transparent/referentially opaque) and cannot be explained at the level of LF. As a result, it poses a challenge for existing semantics for intensional transitive verbs, esp. for Zimmermann’s property-based account, for Stephenson’s situation-theoretic account, and for Moltmann’s truthmaker-semantic account. My paper provides a uniform compositional semantics for ‘individual’- and for ‘scene’-interpretations of remember DP-reports that explains this ambiguity. To do this, it investigates the situations that feature in the proposition-type complement of remember. It finds that, if the referent of the object DP has different properties in these situations, the report receives an individual-interpretation. If the referent has the same properties in all situations, the report can receive an individual-interpretation (next to its scene-interpretation). The resulting semantics captures the intensionality and entailment properties of remember DP-reports and predicts the preferred individual-interpretation of strongly quantificational object DPs.

The paper has profited from discussions with Maria Aloni, Liz Coppock, James Openshaw, Dolf Rami, Florian Schwarz, Markus Werning, and Ede Zimmermann. The research for this paper is supported by the German Research Foundation, DFG, as part of Ede Zimmermann’s project Propositionalism in Linguistic Semantics (ZI 683/13-1) and of Kristina Liefke and Markus Werning’s project in the research unit FOR 2812: Constructing Scenarios of the Past (grant 397530566). It is further supported by the German Federal Ministry of Education and Research, BMBF (through Kristina Liefke’s WISNA professorship).

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Notes

  1. 1.

    Szabó’s analysis is much more sophisticated than is presented here. However, since it cannot be used to explain the individual/scene-ambiguity, I refrain from a more detailed presentation.

  2. 2.

    In some languages (e.g. German; see (\(\star \)) below), this deviance can be corrected by converting the modifier yesterday into a temporal preposition:

    figure h

    However, since this preposition modifies the DP gray-haired man rather than the silent predicate pace up and down the aisles, (\(\star \)) cannot be used to rectify the predictions of the structural ambiguity-account.

  3. 3.

    Since the object DP in remember-reports is always specific, this is different from the unavailability of de dicto-readings for strong quantificational objects of intensional transitive verbs in [25] (see also [8, pp. 148–149]).

  4. 4.

    To capture the context-dependence of the entailment from (14b) to (14a), I mark the left arrow, \(\Leftarrow \), in (14) with a superscript ‘c’.

  5. 5.

    Zimmermann [25, 26] and Moltmann [16] do not apply their accounts to the particular verb remember. My comparison of these approaches is based on their semantics for specific readings of depiction verbs (esp. paint, imagine).

  6. 6.

    This possibility assumes a Kratzer-style generalization of possible worlds (type s) to possible situations, events, and scenes (see [12]).

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Authors and Affiliations

  1. Department of Philosophy II, Ruhr University Bochum, 44780, Bochum, Germany

    Kristina Liefke

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  1. Kristina Liefke
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Editors and Affiliations

  1. Kansai University, Suita, Japan

    Katsutoshi Yada

  2. Tokyo Metropolitan University, Tokyo, Japan

    Yasufumi Takama

  3. Keio University, Tokyo, Japan

    Koji Mineshima

  4. National Institute of Informatics, Tokyo, Japan

    Ken Satoh

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Liefke, K. (2023). Remembering Individuals and Remembering Scenes. In: Yada, K., Takama, Y., Mineshima, K., Satoh, K. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2021. Lecture Notes in Computer Science(), vol 13856. Springer, Cham. https://doi.org/10.1007/978-3-031-36190-6_7

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  • DOI: https://doi.org/10.1007/978-3-031-36190-6_7

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