TY  - THES
AB  - The task of answering natural language questions over structured data has received wide
interest in recent years. Structured data in the form of knowledge bases has been available
for public usage with coverage on multiple domains. DBpedia and Freebase are such knowledge
bases that include encyclopedic data about multiple domains. However, querying such
knowledge bases requires an understanding of a query language and the underlying ontology,
which requires domain expertise. Querying structured data via question answering systems
that understand natural language has gained popularity to bridge the gap between the data
and the end user.
In order to understand a natural language question, a question answering system needs
to map the question into query representation that can be evaluated given a knowledge base.
An important aspect that we focus in this thesis is the multilinguality. While most research
focused on building monolingual solutions, mainly English, this thesis focuses on building
multilingual question answering systems. The main challenge for processing language input
is interpreting the meaning of questions in multiple languages.
In this thesis, we present three different semantic parsing approaches that learn models
to map questions into meaning representations, into a query in particular, in a supervised
fashion. Each approach differs in the way the model is learned, the features of the model, the
way of representing the meaning and how the meaning of questions is composed. The first
approach learns a joint probabilistic model for syntax and semantics simultaneously from the
labeled data. The second method learns a factorized probabilistic graphical model that builds
on a dependency parse of the input question and predicts the meaning representation that is
converted into a query. The last approach presents a number of different neural architectures
that tackle the task of question answering in end-to-end fashion. We evaluate each approach
using publicly available datasets and compare them with state-of-the-art QA systems.
DA  - 2019
LA  - eng
PY  - 2019
TI  - Learning Multilingual Semantic Parsers for Question Answering over Linked Data. A comparison of neural and probabilistic graphical model architectures
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-29356195
Y2  - 2024-11-21T22:42:11
ER  -