Automatic Textprocessing
Module Description
| Course | Module Abbreviation | Credit Points |
|---|---|---|
| BA-2010[100%|75%] | CS-CL | 6 LP |
| BA-2010[50%] | BS-CL | 6 LP |
| BA-2010[25%] | BS-AC | 4 LP |
| BA-2010 | AS-CL | 8 LP |
| Master | SS-CL, SS-TAC | 8 LP |
| Lecturer | Michael Strube |
| Module Type | |
| Language | English |
| First Session | 23.10.2018 |
| Time and Place | Tuesday, 16:15-17:45, INF 327 / SR 4 |
| Commitment Period | tbd. |
Prerequisite for Participation
Studying Computational Linguistics (if not, please contact me)
Assessment
Inhalt
A text is more than a sequence of sentences. To understand a text, one needs to recognize how sentences are connected with each other and why they appear in a particular order. To capture the particular characteristics of text, we developed a range of methods in computational linguistics: local and global coherence models, anaphora and coreference resolution algorithms, methods for recognizing the rhetorical, the temporal, the causal and the argumentative structure of texts. In the module we first talk about linguistically well-founded classical models. Then we continue with more recent machine learning and graph based models before we turn to current neural models for text processing and understanding. The usefulness of such models can only be determined when they are integrated into applications. Hence the module will also deal with evaluating text processing algorithms within applications such as information extraction, machine translation, question answering, readability prediction, essay scoring, etc.
Module Overview
Schedule
190123 Schedule (PDF)Literature
