A Corpus and Semantic Parser for Multilingual Natural Language Querying of OpenStreetMap
By Carolin (Haas) Lawrence, Stefan Riezler. 2016
35 citations recorded by the source corpus.
We present a corpus of 2,380 natural language queries paired with machine readable formulae that can be executed against world wide geographic data of the OpenStreetMap (OSM) database. We use the corpus to learn an accurate semantic parser that builds the basis of a natural language interface to OSM. Furthermore, we use response-based learning on parser feedback to adapt a statistical machine translation system for multilingual database access to OSM. Our framework allows to map fuzzy natural language expressions such as “nearby”, “north of”, or “in walking distance” to spatial polygons on an interactive map. Furthermore, it combines syntactic complexity and compositionality with a reasonable lexical variability of queries, making it an interesting new publicly available dataset for research on semantic parsing.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Mohit Bansal
- Chris Dyer
- Graham Neubig
- Phil Blunsom
- Kyunghyun Cho
- Yoav Goldberg
- Yoshua Bengio
- Noah A. Smith
In Computer Vision and Pattern Recognition
- Mohit Bansal
- Edward Grefenstette
- Stefan Riezler
- Chris Dyer
- Carolin (Haas) Lawrence
- Phil Blunsom
- Wang Ling
- Karl Moritz Hermann
In Natural Language Processing
- Phil Blunsom
- Chris Dyer
- Graham Neubig
- Mohit Bansal
- Kyunghyun Cho
- Edward Grefenstette
- Karl Moritz Hermann
- Yoav Goldberg
Citations in the corpus
- Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback (2018, methodology): “Following the method used by Haas and Riezler (2016), we split the queries into individual tokens by taking a pre-order traversal of the original tree-like structure.”
- Semantic Parsing with Semi-Supervised Sequential Autoencoders (2016, background): “Recent work of Jia and Liang (2016) induces a synchronous context-free grammar and generates additional training examples (x, y), which is one way to address data scarcity issues.”
- NLmaps: A Natural Language Interface to Query OpenStreetMap (2016, methodology): “For the semantic parser to work in this setting we extended the parser described in Haas and Riezler (2016) in several ways 4 .”