Semantics 1, WiSe 2018/19 (Sailer)
Contents
General information
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
Time and place
- Tuesday 08:15-9.45
- Starting: 16.10.2018
- Room: IG 251 (IG-Farben-Haus)
Olat course
Direct link: https://olat-ce.server.uni-frankfurt.de/olat/auth/RepositoryEntry/5912854558
Password: Please send an e-mail to the lecturer (sailer@em.uni-frankfurt.de)
Modules
- Lehramt Englisch (L2/5, L3): FW 2A, FW 2B
- BA English Studies: 3.4(1)
- BA Empirische Sprachwissenschaft: K 6.1
Contact
Manfred Sailer
e-mail: sailer@em.uni-frankfurt.de
office: IG 3.214
office hours: contact via e-mail!
www: http://user.uni-frankfurt.de/~sailer/index.htm
Course requirements
L2 and L5
- regular attendance
- pass all assignment sheets
- Modulprüfung (optional): 90 min written exam (2 CP): 19.2.2019
L3
- regular attendance
- pass all assignment sheets
- Modulprüfung (optional):
- 20 min. oral exam
- not possible: kleine Hausabeit
MSc Wirtschaftspädagogik
- regular attendance
- do all assignment sheets
- Modulprüfung (optional): 90 min written exam (2 CP): 19.2.2019
BA English Studies
- regular attendance
- pass all assignment sheets
- literary scenario:
- Part 1: Extract 15 ambiguous sentences from the text such that all types of ambiguity covered in class are represented provide unambiguous paraphrases of the readings determine the type of ambiguity
- Part 2:
- Define a formal model consisting of 3 characters from your text, which contains 2 properties, 1 2-place relation
- Formulate 2 atomic formulae and compute their truth value.
- Formulate 4 complex formulae with at least 1 logical connective in each and compute their truth value.
- Formulate 1 complex formula with at least 2 logical connectives in
it and compute its truth value.
BA Empirische Sprachwissenschaft
K 6.1
- regular attendance
- Modulprüfung (obligatory): 90min. written exam: 19.2.2019
En 4.1
not possible: You have done this course as part of K6.1, so you can directly do constraint-based Semantics 2.
DH 6.1
not possible: You have done this course as part of K6.1, so you can directly do constraint-based Semantics 2.
Erasmus 6 CP
- regular attendance
- pass the assignment sheets
- 90min. written exam: 19.2.2019
- small literary scenario:
- Part 1: Extract 4 ambiguous sentences from the text such that different types of ambiguity covered in class are represented provide unambiguous paraphrases of the readings determine the type of ambiguity
- Part 2:
- Define a formal model consisting of 3 characters from your text, which contains 2 properties, 1 2-place relation
- Formulate 2 atomic formulae and compute their truth value.
- Formulate 2 complex formulae with at least 1 logical connective in each and compute their truth value.
- Formulate 1 complex formula with at least 2 logical connectives in it and compute its truth value.
The grade will be determined by the result of the written exam.
Mock exam
The mock exam is available as a pdf file: mock-exam-wise1819.pdf
See the tasks with solutions at: Mock exam, WiSe 2018/19 (Sailer) (old version)
See an updated version, which is inline with the upcoming exam at: Mock exam, WiSe 2018/19 (Sailer)
Meeting 14
Meeting 13
Basic combinatorics: Canonical examples
(the following exercises are adapted from the textbook material to Chapter 5.
Possible EX-CONT values
Given the following PARTS lists, what are possible EX-CONT values (if we do not assume other restrictions)
1. PARTS < pat, alex,like, like(__,__) >
Check your answer
like(pat,alex)
like(alex,pat)
2. PARTS < alex,snore, snore(__), ¬(__) >
Check your answer
¬(snore(alex))
3. PARTS < alex,alex,snore >
Check your answer
There is no possible EX-CONT value because the three elements on the PARTS list cannot be combined.
3. PARTS < alex,alex,snore, snore(__) >
Check your answer
snore(alex)
4. PARTS < alex,alex,snore, snore(__), __ ∧ __ >
Check your answer
snore(alex) ∧ snore(alex)
Meeting 12
Meeting 11
There is no face-to-face meeting January 15!
Video
Analysis of simple sentences
In the following examples, we assume a feature SYNSEM (for "syntax and semantics") whose value comprises the features HEAD and VAL. We also assume that we find SYNSEM-values on the valence lists.
Feel free to send feedback on this exercise to Manfred Sailer.
Meeting 10
Meeting 9
There is no face-to-face meeting December 11!
Video
Watch the following video (33') on the basic step in a syntactic analysis as we need it in our course.
This 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.
The next video (14') introduces the way we talk about syntactic trees. Please watch it.
The last video (21') introduces the way we will write down lexical entries in our course.
Basic syntactic notions
Parts of speech
Feel free to send feedback on this exercise to Manfred Sailer.
Syntactic categories
Feel free to send feedback on this exercise to Manfred Sailer.
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
- Use det, noun, prep or verb for the HEAD values.
Feel free to send feedback on this exercise to Manfred Sailer.
Homework for December 18
Task 1: Provide the 5 steps in the syntactic analysis for the sentences in (1)
(1) a. Harry survived.
- b. His grand-mother sent Neville a remebrall.
Task 2: Write the lexical entries in AVM-form for 4 words from the above sentences.
Meeting 8
Meeting 7
Video
Watch the following video on logical determiners:
Exercises
After having watched the video, work on the following tasks.
Task 1 Identify the determiners in the following sentence.
(a) Juliet talked to some stranger at the party.
(b) Every Capulet is an enemy to some Montague.
(c) Many people in Verona are not happy about the Capulet-Montague feud.
Check your solutions here:
(a) some
(b) every, some
(c) many
Task 2 Identify the formula that corresponds to the translation of the sentence.
Task 3 The sentence: Some Tybalt loved some Montague. is translated into the formula
∃ y (montague_{1}(y) : love_{2}(tybalt,y).
Given this table, is the overall formula true or false? (Give a reason for your answer.)
Check your solutions here:
The formula is false, because there is no individual in our model for which both the restrictor and the scope are true.
Task 4 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 6
Variables
Task 4 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
Video
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.
Meeting 4
Computing the truth value of complex formulae
Connectives
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)
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.
Back to the course page.
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)
Back to the course page.
Meeting 2
Our literary scenario
Literary scenario: book/film series Harry Potter
Wikipedia entry: https://en.wikipedia.org/wiki/Harry_Potter
Why it is too difficult to go directly from language to the world
The following architecture is extremely useful when talking about semantics:
- A natural language expressions: Harry meets Hagrid.
- ... is mapped to some expression from a formal language (here: predicate logic): meet2(harry,hagrid)
- This logical expression is then interpreted with respect to our scenario/world: The formula meet2(harry,hagrid) is true, because, in our scenario, Harry meets Hagrid.
The following properties of natural language make it useful to use the intermediate step of a logical language:
- The same expression can have different meanings (ambiguity).
- Different expressions can have the same meaning (synonyms, paraphrases)
Find examples for the above-mentioned properties (ambiguity, synonymy, paraphrases).
Check your answers
1. one form, two meaingns: Ambiguity: (see earlier in this meeting and the slides of last week's meeting)
1.a Ambiguous words: date (fruit or point in time); bank (financial institute or bank of a river)
1.b. Ambiguous sentences: Sycorax and Prospero were stranded on the island with their children.
2. two forms, one meaning:
2.a Synonymous words: couch - sofa; instant - moment
2.b Paraphrases:
- active-passive pairs: Prospero set Ariel free. - Ariel was set free by Prospero.
- cleft sentences: Prospero set Ariel free. - It was Prospero who set Ariel free.
- different ways to express a possessor: Sycorax was the first inhabitant of the island. and Sycorax was the island's first inhabitant.
Towards a formal model
First steps
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.
You can think of building a formal model like being the producer of a film who has to collect everything that should be included in the film.
Here is a very simple story from which we can derive an example model.
The universe and name symbols
Task: Assume three individuals from our Game of Thrones-scenario.
Formally we collect the individuals of our model in a so-called universe (U). For the fairy-tale story, we can define the universe as follows:
U = {Redridinghood, Grandmother, Wolf}
Do a similar definition for your own scenario.
We can introduce name symbols for some of our individuals. For example: redridinghood, grandmother, wolf.
We link the name symbols to the individuals in our modal. To do this, we introduce the interpretation function. We will written the interpretation function as as I.
This function can be defined in the following way:
I(grandmother) = Grandmother
I(redridinghood) = Red Riding Hood
I(wolf) = Wolf
Properties and predicate symbols
A property is a specification that either holds of an individual or not. In the little story, having a big mouth is a property of the Wolf, but of noone else in the story. Being female holds of both Little Red Riding Hood and the Grandmother.
We can think of a property as the set of individuals that have this property. Under this view, the property of being female would be the set {Redridinghood, Grandmother}.
Alternatively it is convenient to think of properties as 1-place relations. Under this view, the property of being female would be a set of lists of length 1. This is what the property of being female then looks like: { <Redridinghood>, <Grandmother> }
Task: Using your Game of Thrones universe, define two properties in the format of 1-place relations.
Just like before, we want to have symbols that we can use in the logical language. For our example, let's take the predicate symbols female_1 and has-big-mouth_1. There interpretation is defined as follows:
I(female_1) = { < x > | x is female } = { <Redridinghood>, <Grandmother> }.
Task: For each of your properties, invent an appropriate predicate symbol. Define its interpretation.