Reference resolution challenges for intelligent agents: The need for knowledge
|Reference resolution challenges for intelligent agents: The need for knowledge|
|Published in||IEEE Intelligent Systems|
|Keyword(s)||Unknown (Extra: Competing systems, Corpus-based methods, Message understanding conferences, NAtural language processing, Reference resolution, Semantic features, Statistical techniques, Wikipedia, Wordnet, Artificial intelligence, Computational linguistics, Natural language processing systems, Software agents, Intelligent agents)|
|Article||BASE, CiteSeerX, Google Scholar|
|Web||Ask, Bing, Google (PDF), Yahoo!|
|Download and mirrors|
|Local copy||Not available|
|Remote mirror(s)||Not available|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of journal articles|
The difficult cases of reference in natural language processing require intelligent agents that can reason about language and the machine-tractable knowledge. The knowledge-lean model relies on various statistical techniques that are trained over a manually defined collection, typically using a small number of features such as morphological agreement, the text distance between the entity and the potential coreferent, and various other features that do not require text understanding. The incorporation of some semantic features drawn from Wikipedia, and WordNet improves reference resolution for some referring expressions. One promoter of knowledge-lean corpus-based methods was the message understanding conference (MUC) reference resolution task, for which sponsors provided annotated corpora for the training and evaluation of the competing systems. The two requirements for the reference annotation strategy were the need for greater than 95 percent interannotator agreement and the ability to annotate quickly and cheaply.
- This section requires expansion. Please, help!
Probably, this publication is cited by others, but there are no articles available for them in WikiPapers. Cited 8 time(s)