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Page 1: What is the highest mountain on Earth? Mountain-height problem Measured from the sea level: Mt. Everest (8,848 m) From the base to the peak: Mauna Kea
Page 2: What is the highest mountain on Earth? Mountain-height problem Measured from the sea level: Mt. Everest (8,848 m) From the base to the peak: Mauna Kea

What is the highest mountain on Earth?

Page 3: What is the highest mountain on Earth? Mountain-height problem Measured from the sea level: Mt. Everest (8,848 m) From the base to the peak: Mauna Kea

Mountain-height problem

Measured from the sea level: Mt. Everest (8,848 m)

From the base to the peak: Mauna Kea (10,203 m)

To the Earth centre: Chimborazo (6,384 km )

There are many different meanings of the expression “mountain height” as there are different

procedures of measuring.

Page 4: What is the highest mountain on Earth? Mountain-height problem Measured from the sea level: Mt. Everest (8,848 m) From the base to the peak: Mauna Kea

Meaning = Algorithm

Meaning of an expression is a collection of procedures for computing its denotation.

In particular, meaning of a sentence can be identified with a way of checking whether it is

true or false.

Page 5: What is the highest mountain on Earth? Mountain-height problem Measured from the sea level: Mt. Everest (8,848 m) From the base to the peak: Mauna Kea

Quantifiers

Quantifiers occur very frequently in our speech.

Every mountain is higher than 300 m.

Some mountains are covered with snow.

At least 14 summits reach above 8000 m.

An even number of mountains are >8000 m.

Most of Asia is covered by mountains.

They underlie language expressivity.

Page 6: What is the highest mountain on Earth? Mountain-height problem Measured from the sea level: Mt. Everest (8,848 m) From the base to the peak: Mauna Kea

In the Thesis

What algorithms lie behind quantifiers?

What is their complexity?

How does it interplay with comprehension?

Why is it interesting for linguistics?

What are the relations with cognitive-science?

Page 7: What is the highest mountain on Earth? Mountain-height problem Measured from the sea level: Mt. Everest (8,848 m) From the base to the peak: Mauna Kea

Classifying Quantifiers by Complexity

An experiment

Page 8: What is the highest mountain on Earth? Mountain-height problem Measured from the sea level: Mt. Everest (8,848 m) From the base to the peak: Mauna Kea

Some cars are black.

Page 9: What is the highest mountain on Earth? Mountain-height problem Measured from the sea level: Mt. Everest (8,848 m) From the base to the peak: Mauna Kea

An even number of cars are black.

Page 10: What is the highest mountain on Earth? Mountain-height problem Measured from the sea level: Mt. Everest (8,848 m) From the base to the peak: Mauna Kea

At least 8 cars are purple.

Page 11: What is the highest mountain on Earth? Mountain-height problem Measured from the sea level: Mt. Everest (8,848 m) From the base to the peak: Mauna Kea

Most of the cars are red.

Page 12: What is the highest mountain on Earth? Mountain-height problem Measured from the sea level: Mt. Everest (8,848 m) From the base to the peak: Mauna Kea

Most circles are connected with each other.

Page 13: What is the highest mountain on Earth? Mountain-height problem Measured from the sea level: Mt. Everest (8,848 m) From the base to the peak: Mauna Kea

More than 1 square and more than 2 circles

are connected by lines.

Page 14: What is the highest mountain on Earth? Mountain-height problem Measured from the sea level: Mt. Everest (8,848 m) From the base to the peak: Mauna Kea

And…

I bet you had a feeling of increasing difficulty.

This is explained by computational complexity…

and corroborated via experiments.

Page 15: What is the highest mountain on Earth? Mountain-height problem Measured from the sea level: Mt. Everest (8,848 m) From the base to the peak: Mauna Kea

Summary

I systematically drew a distinction between tractable and intractable quantifiers.

Tractable Quantifiers Intractable Quantifiers

Monadic Collective

Standard polyadic Branching and Ramsey

Weak reciprocals Strong reciprocals

More in the book!