Documentation

Linglib.Fragments.English.NumeralModifiers

Semantic type of a numeral modifier.

Modifiers can be:

  • Tolerance-based: "around n" = λx. |n-x| ≤ y (with hidden tolerance y)
  • Interval-based: "between a b" = λx. a ≤ x ≤ b
  • Exactifier: "exactly n" = λx. x = n
  • Bound-setting: "at least n", "more than n", etc.
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      Pragmatic function of a numeral modifier.

      Following Égré et al. (2023): modifiers signal the shape of the speaker's private distribution over the true value.

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          Evaluative valence of a bound-setting modifier.

          Distinguishes modifiers with the same truth conditions but different framing:

          • "at most 100" (negative valence) → reversed framing (endorsed more in negative contexts)
          • "up to 100" (positive valence) → standard framing (endorsed more in positive contexts)
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              Lexical entry for a numeral modifier.

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                  "about": tolerance-based approximation.

                  The most common English approximator. Used in BSB2022's stimuli: "about fifty minutes" vs "fifty minutes" vs "forty-nine minutes."

                  ⟦about n⟧ = λy.λx. |n-x| ≤ y Pragmatically signals peaked private distribution centered on n.

                  Source: @cite{beltrama-solt-burnett-2023}

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                    "around": tolerance-based approximation.

                    ⟦around n⟧ = λy.λx. |n-x| ≤ y Pragmatically signals peaked private distribution centered on n.

                    Source: Égré et al. 2023

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                      "approximately": explicit tolerance marker.

                      Similar to "around" but more formal register. Interacts with roundness: "approximately 100" natural, "approximately 99" marked.

                      Source: Phenomena/Gradability/Imprecision/Numerals.lean

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                        "roughly": informal tolerance marker.

                        Behaves like "around" pragmatically.

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                          "between... and...": interval specification.

                          ⟦between a and b⟧ = λx. a ≤ x ≤ b Pragmatically signals flat distribution over [a,b]. Does NOT convey shape information (only support).

                          Source: Égré et al. 2023

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                            "exactly": precision enforcer.

                            ⟦exactly n⟧ = λx. x = n Removes imprecision. Point signal.

                            Source: Phenomena/Gradability/Imprecision/Numerals.lean (ExactlyModifierDatum)

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                              "precisely": formal exactifier.

                              Behaves like "exactly" semantically.

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                                "at least n": Class B lower bound (max ≥ n).

                                Generates ignorance implicatures because compatible with the bare reading. Neutral evaluative valence.

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                                  "at most n": Class B upper bound (max ≤ n).

                                  Generates ignorance implicatures. NEGATIVE evaluative valence: "at most" is endorsed more in negative contexts. @cite{claus-walch-2024} show this produces reversed framing effects.

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                                    "more than n": Class A lower bound (max > n).

                                    Does NOT generate ignorance implicatures (excludes the bare-numeral world). Neutral evaluative valence.

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                                      "fewer than n": Class A upper bound (max < n).

                                      Does NOT generate ignorance implicatures (excludes the bare-numeral world). Neutral evaluative valence.

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                                        "up to n": Class B upper bound (max ≤ n).

                                        Same truth conditions as "at most n", but POSITIVE evaluative valence. @cite{claus-walch-2024} show "up to" follows standard framing (endorsed more in positive contexts), unlike "at most".

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                                          "from n on": Class B lower bound (max ≥ n).

                                          Positive evaluative valence: invites positive evaluation of the quantity. Generates ignorance implicatures (compatible with bare reading).

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                                            "almost": proximal approximation with polar exclusion.

                                            ⟦almost n⟧ = λx. close(x, n) ∧ ¬(x = n) [or ¬(x ≥ n) under LB]

                                            Unlike tolerance modifiers ("around"), "almost" EXCLUDES the target value (the polar component). @cite{nouwen-2006} decomposes "almost" into proximal (close to p) and polar (¬p) components. This creates a key LB/BL divergence:

                                            • Under LB: "almost three" = close to 3 AND <3 → only values below 3
                                            • Under BL: "almost three" = close to 3 AND ≠3 → values above OR below 3

                                            The empirical asymmetry (below only) is argued by @cite{penka-2006} to favor LB. @cite{nouwen-2006} shows that polar orientation is in general context-dependent (e.g., "almost that warm" vs "almost that cold" orient in opposite directions depending on the scale).

                                            Source: @cite{penka-2006} "Almost there: The meaning of almost"; @cite{nouwen-2006} "Remarks on the Polar Orientation of Almost".

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                                              "nearly": synonym of "almost" with slight register difference.

                                              Same proximal + polar semantics as "almost".

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                                                Informativity scale for numeral modifiers.

                                                Ordered by how much information about the true value they convey: exactly > around > between

                                                "Exactly" gives the most information (point), "around" gives shape, "between" gives only support.

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                                                            No interval or exact modifiers convey shape information.

                                                            No Class A modifiers generate ignorance implicatures.

                                                            "at most" and "up to" differ only in evaluative valence.

                                                            Same modType, modClass, boundDir, but different evaluativeValence. This is the key @cite{blok-2015} / @cite{claus-walch-2024} observation.

                                                            Approximators are not sorites-susceptible (unlike tolerance modifiers).