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
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.
Feel free to send feedback on this exercise to Manfred Sailer.
Analysis of simple sentences
Videos
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 third video shows which information is inside a lexcial entry.
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)