LING 165C Lecture Notes - Lecture 6: Logical Consequence

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Published on 3 Feb 2019
School
UCLA
Department
Linguistics
Course
LING 165C
Professor
1/9/18 Lecture 1
-Model Theoretic Semantics: Abstracting actual sentences and what they refer to, and
instead describe sentences with model
- “John left”:
- Sentence: don’t have speakers, what we are interested in because not
associated with sentences
- Utterance: have speakers
- The Model
- We need dots inside the circle(model) that helps define sentences differently
depending on their meaning
- Examples
- [The sky is blue] = True
- [The sky is red] = False
-Problem 1:
Non Declarative sentences,
Two things in the model that helps us
make distinctions. But not conclusive enough for theory, because two different
sentences may give out the same result
- [There is a cat] = true, but [The sky is blue] is also true
- [Are you cold] falsely predicts the meaning given the only two things in the
model These are nondeclarative, things that don’t have truth
expressions
-Problem 2: Names
are expressions, and have meanings, but they are not
correctly represented given the world that only has true or false. i.e [John]
-Problem 3: Other sentences that comes out as undefined. i.e [I am here], [It's
raining in Omaha] because we just don’t know as of now
-Problem 4: Homogeneity, The problem with [Its raining in Omaha] when it is
actually raining in half of Omaha, the sentence can be both true or false.
- Problem 5: Sentences that contains vague predicates. [John is tall] shows that
our model is not enough
- Improved Model
- Adding more dots (possible worlds) that represents how things could be. They
don’t have internal structures yet, and represents states of affair
- [The sky is blue] can be anything in the model
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Document Summary

Model theoretic semantics : abstracting actual sentences and what they refer to, and instead describe sentences with model. Sentence: don"t have speakers, what we are interested in because not associated with sentences. We need dots inside the circle(model) that helps define sentences differently depending on their meaning. Problem 1 : non declarative sentences, two things in the model that helps us make distinctions. But not conclusive enough for theory, because two different sentences may give out the same result. [there is a cat] = true, but [the sky is blue] is also true. [are you cold] falsely predicts the meaning given the only two things in the model these are nondeclarative, things that don"t have truth expressions. Problem 2 : names are expressions, and have meanings, but they are not correctly represented given the world that only has true or false. i. e [john] Problem 3: other sentences that comes out as undefined. i. e [i am here], [it"s.