Good afternoon again. Thank you all for joining. I'm very pleased to introduce the speaker
of our Moll lecture today, Professor Dr. Stefanie Ebert. Stefanie is a professor here at the
Moll. She is running the chair of computational linguistics, computational and composed linguistics.
Actually, she has a background as a bachelor in mathematics.
Well, okay. So, she will tell us more about the trajectory. But this is a particular case of
successful path in which from math you can get, you know, impactfully into different areas, like
in this case computational and composed linguistics. So, Stefanie is the vice spokesperson of Moll. So,
she has been with us from the very early of the conception of the center, that as you know was
as a merging point or a point of intersection of different groups and disciplines here at the Moll.
Certainly, this is all related to mathematics, mathematics and data science,
artificial intelligence, computational mathematics. So, this link us very naturally to other groups,
like the groups in computational mechanics here in the faculty of engineering, computer science,
and so on, but also through machine learning to computational and composed linguistics.
Maybe probably this is a very superficial understanding, but if somebody had any doubt
that there was a link between linguistics and mathematics, now I will check, check it,
and you will see that indeed, you know, there is a connected path of a steady configuration that
leads you from one configuration to the other. So, Stefanie Ebert, before she's a transgender woman,
so you want to find her full academic achievements, you will have also to pay attention to
the fact that earlier she was Estefan Ebert. So, you can see this name change, well, which nowadays
that, you know, counting in Google Scholar is so important makes, you know, is not totally irrelevant.
I'm still not sure how well Google Scholar has dealt with the name change. Are they counting well or not?
I'm not sure whether it gets, it seems to get, it seems as if you have difficulty getting a few of
the new papers, although I've specified both names there. Definitely. So, the best thing is to use
APA-like citations, or you just have abbreviated first names. We have this problem also because we use two family names
normally, officially, and then in the standard international system, they only take the last name,
so many, many people in Spain have to merge both names to make one full name so that, you know,
these systems recognize your work properly. Anyhow, so we are very delighted that today,
in principle, we are learning about this field in a way that we, you know, in particular,
us as very simple-minded mathematicians can understand. Thank you, Estefan.
Well, thanks very much. So, today I'm going to talk about a topic from my research field of
computational corpus linguistics, which means there will be a lot less mathematics in this
talk than we're used to from the lecture series. Instead, we're going to look at linguistic research
questions and certain fairly simple quantitative techniques we can use to find evidence to answer
these questions. So, thanks very much for coming anyway. I hope it'll still be interesting,
and there's a little bit of math there too. Okay, so the research question I'm addressing
actually is situated in a division, in an artificial division that has a long tradition
in linguistics. It's sort of something that we've overcome in the field, but for a long time,
people used to think of linguistics as a division between grammar and lexis. So, lexis is the
vocabulary, the words. Lexis is dealt with in dictionaries. Grammar is dealt with in
grammars of the language, often prescriptive grammars that tell you how, what the right way
is to form sentences from the words of a language. Grammar and lexis were seen as two entirely
different fields or different aspects of language because grammatical rules are fully schematic and
productive. That means they are regular. You can apply them in any case. You can form many
new sentences using these rules, and then applying a completely regular and transparent, or as we say,
compositional manner. Compositional means that the meaning of a sentence, when you use a grammatical
rule to form a sentence, the meaning of the sentence can be derived from the meaning of the
parts. So, there's this, it's meant to be unambiguous that you can get to the meaning of the sentence
from the meaning of the parts. On the other hand, the vocabulary, the lexis, was always seen as
completely fixed and arbitrary. So, the relationship between words and their meanings is arbitrary
convention. That's why we need to list all the words of the language in the dictionary.
Presenters
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01:34:15 Min
Aufnahmedatum
2024-11-13
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2024-11-18 18:56:07
Sprache
en-US
Date: Wed. November 13, 2024
Event: FAU MoD Lecture
Event type: On-site / Online
Organized by: FAU MoD, the Research Center for Mathematics of Data at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)
FAU MoD Lecture: Measuring productivity and fixedness in lexico-syntactic constructions
Speaker: Prof. Dr. Stephanie Evert
Affiliation: FAU MoD member/vice-spokesperson | CCL – Chair of Computational Corpus Linguistics. Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)
Abstract.
In cognitive linguistics, constructions are understood as pairings of form (i.e. a lexico-grammatical pattern) and meaning (as a parameterised function if the pattern contains variable elements), which constitute the fundamental building blocks of speakers’ linguistic knowledge. Between the extremes of purely syntactic constructions (such as the ditransitive) and purely lexical ones (individual words or multiword units), a large part of constructions fall somewhere in the middle of the lexis-grammar continuum. They often consist of multiple lexical and grammatical elements, which range from completely fixed lexical items to highly variable slots.
In this talk I argue that the variability of slots in a lexico-grammatical pattern forms a cline ranging from complete fixedness to full productivity. This cline cannot be quantified by a single integrated measure, but is a combination of three distinct, but overlapping aspects:
(i) fixedness is quantified by the frequency of an element (or rather, its conditional probability given the other items in the lexico-grammatical pattern);
(ii) at the opposite end of the cline, productivity is quantified by type-token measures and interpreted with the help of statistical LNRE models;
(iii) in the middle ground between productivity and fixedness, statistical association plays a central role in identifying salient, semi-fixed lexical items.
These methodological considerations are illustrated with a case study on shell noun constructions such as “It is a fact that you will have to listen to the entire talk.”
See more details of this FAU MoD lecture at: