SoSe23: Semantics 1

Course description

Semantics is the study of the (literal) meaning of words and sentences. The meaning of a sentence is usually predictable from the words in the sentence and its syntactic structure. Yet, this relationship between form and meaning is not a simple one-to-one mapping. Instead, it is rich in ambiguities, pleonastic marking and elements without any identifiable meaning contribution. We will work on an account that is founded on classical tools of semantic research but still directly addresses these empirical challenges. After the class, the participants will be able to identify - and partly analyze - interesting semantic phenomena in naturally occurring texts. They will have acquired a basic working knowledge in formal logic, which they will be able to apply in the description of meaning

Meeting 9

Videos

This video shows which information is inside a lexcial entry.

The second video shows the HPSG analysis of two simple sentences (37'):

Duncan died. (the first 18+ minutes)

Macbeth killed Duncan. (the rest of the video)

Lexical entries as Attribute-Value Matrix

Provide the required information on the lexical properties of the underlined words in the following sentences.
Note:

• Put a minus ("-") if a slot should not receive any filling.
• Write NP, PP, Det, VP into the valence lists, if such elements are selected.
• Use det, noun, prep or verb for the HEAD values.

1 Alex read a book yesterday.

PHON

HEAD

SUBJ < >

SPR < >

COMPS < >

2 Alex talked to a friend.

PHON

HEAD

SUBJ < >

SPR < >

COMPS < >

3 Pat liked this new documentary on African wild life.

PHON

HEAD

SUBJ < >

SSPR < >

COMPS < >

4 Alex talked to a friend.

PHON

HEAD

SUBJ < >

SPR < >

COMPS < >

Feel free to send feedback on this exercise to Manfred Sailer.

Analysis of simple sentences

Indicate the missing values of the VAL and the HEAD features using tags ([1], ...) or "-" for empty lists.

Alex snored.

syntactic structure:

Words:                                                                                                   Phrase:

Alex                                                             snored                                    S: Alex snored.

HEAD [4]noun                                  HEAD [5]verb                                    HEAD

SUBJ < >                                  SUBJ < >                                    SUBJ < >

SPR   < >                                  SPR < >                                     SPR < >

COMPS < >                              COMPS < >                               COMPS < >

Meeting 8

For the meeting

Watch:

Homework for meeting 9

Watch the following video (33') on the basic step in a syntactic analysis as we need it in our course.

The next video (14') introduces the way we talk about syntactic trees. Please watch it.

The final video is a more general video (12', produced in 2008) on basic steps in a syntactic analysis. Note, only steps 1-5 apply to our course (i.e. the first 9'30 of the video). Step 6 is based on a different syntactic theory.

Meeting 7

Course content:

Meeting 6

Variable assignment funcion

Task Variable assignment function
Start with the following variable assigment function g: g(u) = Romeo, g(v) = Juliet, g(w) = Romeo, g(x) = Laurence, g(y) = Mercutio, g(z) = Juliet

Provide the changed variable assignment function g[v/Paris].

Check your solutions here:

g[v/Paris](u) = g(u) = Romeo
g[v/Paris](v) = Paris
g[v/Paris](w) = g(w) = Romeo
g[v/Paris](x) = g(x) = Laurence
g[v/Paris](y) = g(y) = Mercutio
g[v/Paris](z) = g(z) = Juliet

Meeting 5

Formulae with one connective

The following video presents the step-by-step computation of the truth value of two formulae with connectives. The example uses a model based on Shakespeare's play Macbeth. The two formulae are:

• ¬ king(lady-macbeth)
• king(duncan) ∨ king(lady-macbeth)

Formulae with two connectives

The next video shows how the truth value of a more complex formula can be computed. The example contains two connectives:

kill(malcom,lady-macbeth) ∨ ¬thane(macbeth)

The video shows two different methods: top down and bottom up.

Truth tables

Truth tables are also useful to compute the truth value of complex formulae. This is shown in the following podcast, created by Lisa Günthner.

Meeting 4

Computing the truth value of complex formulae

The following video presents the step-by-step computation of the truth value of two formulae with connectives. The example uses a model based on Shakespeare's play Macbeth. The two formulae are:

• ¬ king(lady-macbeth)
• king(duncan) ∨ king(lady-macbeth)

Meeting 3

Computing the truth value of atomic formulae

The following video presents the step-by-step computation of the truth value of two atomic formulae. The example uses a model based on Shakespeare's play Macbeth. The two formulae are:

• kill2(macbeth,duncan)
• kill2(lady-macbeth,macbeth)

Meeting 2

Models

The following material is an adapted form of material created by student participants of the project e-Learning Resources for Semantics (e-LRS). Involved participants: Lisa, Marthe, Elisabeth, Isabelle.

Watch a short podcast what first-order models look like.

Based on this podcast, we can define a scenario as follows:

• Universe: U = {LittleRedRidingHood, Grandmother, Wolf}
  
• Properties:

RedHood = { < x> | x wears a read hood } = { <LittleRedRidingHood> }

Female = { <x> | x is female } = { <LittleRedRidingHood>, <Grandmother> }

BigMouth = { <x> | x has a big mouth } = { <Wolf> }

LiveInForest = { < x> | x lives in the forest } = { <Grandmother>, <Wolf>}

• Relations:

GrandChildOf = { <x,y> | x is y 's grandchild } = { <LittleRedRidingHood,Grandmother > }

AfternoonSnackOf = { <x,y> | x is y 's afternoon snack } = { <LittleRedRidingHood,Wolf > }

From this scenario, we can build a model M = < U, I >

• Universe: U = {LittleRedRidingHood, Grandmother, Wolf}
• Name symbols: NAME = {little-red-riding-hood}
  Note: In our model, only one individual has a name.
• Predicate symbols: PREDICATE = {red-hood1, female1, big-mouth, live-in-forest1, grand-child-of2, afternoon-snack-of2}
• Interpretation function I:

• for name symbols: I(little-red-riding-hood) = LittleRedRidingHood
• for predicate symbols:
  

I(red-hood1) = RedHood = { < x> | x wears a read hood } = { <LittleRedRidingHood> }

I(female) = Female = { <x> | x is female } = { <LittleRedRidingHood>, <Grandmother> }

I(big-mouth1) = BigMouth = { <x> | x has a big mouth } = { <Wolf> }

I(live-in-forest1) = LiveInForest = { < x> | x lives in the forest } = { <Grandmother>, <Wolf>}

I(grand-child-of2) = GrandChildOf = { <x,y> | x is y 's grandchild } = { <LittleRedRidingHood,Grandmother > }

I(afternoon-snack-of2) = AfternoonSnackOf = { <x,y> | x is y 's afternoon snack } = { <LittleRedRidingHood,Wolf > }

Meeting 1

Video

Challenging phenomena at the syntax-semantics interface

Literary scenario

Howl's moving castle:

• Wikipedia entry of the novel by Diana Wynne Jones (1986): https://en.wikipedia.org/wiki/Howl%27s_Moving_Castle
• Wikipedia entry of the 2004 movie: https://en.wikipedia.org/wiki/Howl%27s_Moving_Castle_(film)