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Minsky: to be added
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Schank & Abelson 1977, Scripts, Plans, Goals, and Understanding: This seminal book develops the idea that humans know typical scripts, that is, typical sequences of events, and use them for making sense of the world.
This idea has been further studied in many subsequent papers, among them:
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Bower/Black/Turner 1979, Scripts in Memory for Text They test people's knowledge of scripts, including one experiment where participants put slashes in given stories to indicate "natural parts"
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Liechtenstein/Brewer 1980, Memory for goal-directed events: Goals and subgoals as the main organizing force of scripts.
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Abbott/Black/Smith 1985, The representation of scripts in memory: They assume a 3-level hierarchy of scene actions (lowest), scene headers, script headers (highest). They find that people infer higher-level events from lower-level, but not vice versa. They hypothesize that there's a "goldilocks level", like basic kinds in concept representation theory, and it's scene headers.
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Zwaan/Radavansky 1998, Situation models in language comprehension and memory: Humans build mental models, not model-theoretic but visual, spatial imaginings of what is talked about
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Austin/Vancouver 1996, Goal constructs in psychology: Structure, process, and content They focus on defining the notion of goals in scripts
While neither "scripts" nor "generalized event knowledge" are exactly defined in the literature (and probably they cannot be), Generalized Event Knowledge is about knowledge at a smaller scale than the scripts above: While scripts seem to be mainly long sequences of events structured into segments, Generalized Event Knowledge is about a single event and its participants.
- Summary of this line of research in a single sentence (and an overview article): McRae and Matsuki 2009, People use their knowledge of common events to understand language, and do so as quickly as possible
In particular:
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McRae/Ferretti/Amyote 1997, Thematic roles as verb-specific concepts Selectional preferences as concepts with typical features. For example, “young naive gambler” is a more likely Theme than Agent of “manipulate”
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Mention of an event along with one argument will make the listener expect specific other arguments, for example "mechanic checked... brakes" is more expected than "mechanic checked... spelling", and conversely for "journalist checked...": Bicknell et al 2008, Hare et al 2009
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There is also an effect of loosely co-occurring objects and events: Metusalem et al 2012
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Elman 2009 speculates that given that Generalized Event Knowledge does not easily fit into word-specific lexicon entries, and given that Generalized Event Knowledge influences human sentence processing, maybe we might not need to postulate a lexicon, and Generalized Event Knowledge might be enough.
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Chersoni et al 2019 propose a computational model of Generalized Event Knowledge where (lexical) selectional preferences are re-ranked by event knowledge encoded in a graph. They also provide a challenge dataset.
Frame semantics: Fillmore 1982, Frame Semantics, 1985, Frames and the Semantics of Understanding, Fillmore et al 2003 Background to FrameNet
Charles Fillmore's frames are "a general cover term for the set of concepts variously known, in the literature on natural language understanding, as 'schema', 'script', 'scenario', 'ideational scaffolding', 'cognitive model' or 'folk theory'" (Fillmore 1982). Frames are cognitive objects, prototypical situations, chunks of background information that are being evoked whenever you hear a lexical unit (word, multiword expression, construction).
Fillmore 1982 argues that sentence understanding involves evoking and integrating frames for the words in the sentence. In Fillmore 1985 he also points to the importance of script knowledge in sentence understanding. His example is: "He pushed against the door. The room was empty." Fillmore writes: “we make the two sentences cohere by assuming that the goal somebody might have in pushing against a door is to get that door open, and that if one succeeded in getting the door open by such an act, one could then be in a position to notice whether the room was empty.” Recanati 2004, Literal Meaning, p.36 also makes the point that scripts (he calls them schemata) play a role in sentence understanding.
Situation semantics was introduced by Barwise and Perry, see Barwise and Perry 1983. For overviews, see Kratzer 2021, Situations in Natural Language Semantics and Stojanovic 2012, Situation Semantics. Devlin 2006, Situation Theory and Situation Semantics gives an overview of the formalization.
Kratzer: "Situation semantics was developed as an alternative to possible worlds semantics. In situation semantics, linguistic expressions are evaluated with respect to partial, rather than complete, worlds. There is no consensus about what situations are, just as there is no consensus about what possible worlds or events are. According to some, situations are structured entities consisting of relations and individuals standing in those relations. According to others, situations are particular"
Devlin writes: "Situation semantics provides a relational theory of meaning. In its simplest form, the meaning of an expression
More Devlin: "Information is always taken to be information about some situation, and is as- sumed to be built up from discrete informational items known as infons." An infon has the shape
Situation theory is helpful for describing the meaning of embedded clauses, whose meanings can now be described not as a sense/intension, but as a situation. Kratzer explains how that helps for drawing the right inferences from direct perception reports, such that, if "Beryl saw that Meryl sprinkled the white powder on Cheryl’s dinner", it does not follow that "Beryl saw that Meryl sprinkled the most deadly poison on Cheryl’s dinner."
Stojanovic describes Situation Theory as "breaking off with the Fregean heritage", with main themes including: primacy of situations ("reality consists primarily of situations, while other categories, such as individuals, properties, or locations, arise as uniformities across situations"), partiality, relational theory of meaning ("the idea that meaning should be seen as a relation between situations, rather than some kind of independent entity"), uniformities and constraints ("agents get attuned to various kinds of uniformities, which allow them to classify the reality in ways that enhance their capacities for action"; "constraints are then seen as uniformities that arise among the ways in which situations relate to one another"), truth as uniformity across situations ("truth is merely a device that helps us classify situations").
Theories of situations and generalized event knowledge in psychology, and also frame semantics, involve types of situations that reoccur often enough to be remembered as typical. Similarly, situation theory talks about constraints that are uniformities in relations between situations, for example between smoke and fire; but situation theory also makes use of particular situations and the inferences that they afford, like the "Beryl" situation above.
In situation theory, would generalized event knowledge and scripts count as constraints, uniformities in relations between situations, and would they thus be semantic in nature, rather than pragmatic/world knowledge?
Dobnik/Cooper/Lappin/Larsson 2015 propose a probabilistic semantics in which situations are atomic objects that have types that are (probabilistic) propositions, for example a situation s can be of the type "Kim is smiling" if Kim is smiling in s. (The paper argues for a situation-based probabilistic semantics on the grounds that basing a probabilistic semantics on probabilities of worlds is fundamentally flawed: Worlds are so gigantic that they are not cognitively plausible objects that a cognizer could form a probability distribution over.)
They mention Situation Theory as a background to the situation types that they use, but it didn't become clear to me what the exact connection is between Type Theory with Records and Situation Theory.
Goodman and Lassiter 2015 propose a probabilistic view of language and thought in which a cognizer has the capacity to probabilistically imagine/generate situations, or chunks of worlds, and the capacity to test whether a particular utterance would be true in an imagined world chunk. The size of these imagined chunks is limited by the question under discussion (which is why they don't fall prey to the Lappin's probabilistic-world-size problem from above).
What notion of situation do they draw on? Actually, not really on any, because their probabilistic approach doesn't care about whether its elements are worlds or situations, the question under discussion cuts it down to the right size anyway.
Venhuizen et al 2021 propose a distributional probabilistic model in which the listener incrementally builds a mental image of the situation presented in a sentence, and forms probabilistic inferences on that basis. This builds on Frank et al 2003
What notion of situations do they draw on?
Chambers and Jurafsky 2008, 2009
Pichotta and Mooney 2014, 2016
Balasubramanian et al 2013
Chambers 2013
Weber et al 2018, 2019, 2020
Wang et al "story salads"
Li et al language modeling with latent situations
Ruong Huang, automatically selecting narrative texts
Chambers 2017 "behind the scenes"
Mostafazadeh et al 2016 ROCstories
Kwon et al 2020 Preconditions: PeKo
Lal et al 2021: TellMeWhy
Valurupalli et al 2022, POQUE
Lee and Goldwasser
Li et al 2020 connecting the dots