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The role of linguistic information for shallow language processing

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Constantin Orasan
Constantin Orasan

The document discusses shallow language processing and summarization. It argues that while deep language understanding is limited, shallow methods can be improved by adding linguistic information. As an example, it shows how term frequency, anaphora resolution, discourse cues and genetic algorithms can select extractive summaries that better match human abstracts, without requiring full text comprehension.

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The Role of Linguistic Information for Shallow Language Processing Constantin Orasan Research Group in Computational Linguistics University of Wolverhampton http://www.wlv.ac.uk/~in6093/
[object Object],[object Object],[object Object],[object Object],[object Object]
Process language in a similar manner to humans ,[object Object],[object Object]
Deep vs. shallow linguistic processing ,[object Object],[object Object]
Purpose of this talk ,[object Object],[object Object],[object Object]
Structure ,[object Object],[object Object],[object Object],[object Object],[object Object]
Automatic summarisation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Automatic abstraction ,[object Object],[object Object],[object Object],[object Object]
FRUMP ,[object Object],[object Object],[object Object],[object Object],[object Object]
Example of script ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
System organisation ,[object Object],[object Object],[object Object],[object Object],[object Object]
The output ,[object Object],[object Object],[object Object],[object Object]
Limitations ,[object Object],[object Object]
Limitations (II) ,[object Object],[object Object],[object Object],[object Object]
Limitations (III) ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object]
Automatic extraction ,[object Object],[object Object],[object Object],[object Object]
Purpose of this research ,[object Object],[object Object],[object Object],[object Object]
Setting of this research ,[object Object],[object Object],[object Object],[object Object],[object Object]
Evaluation metric ,[object Object],[object Object]
Extracts vs. abstracts ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Extracts vs. abstracts ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Extracts vs. abstracts (II) ,[object Object],[object Object],[object Object]
Determining the upper limit ,[object Object],[object Object],[object Object],[object Object],[object Object]
The upper limit
Baseline ,[object Object],[object Object]
The upper and lower limit
Term-based summarisation ,[object Object],[object Object],[object Object],[object Object]
Term-frequency ,[object Object],[object Object],[object Object]
TF*IDF ,[object Object],[object Object],[object Object]
 
 
Indicating phrases ,[object Object],[object Object],[object Object],[object Object]
 
More accurate word frequencies ,[object Object],[object Object],[object Object],[object Object]
Mitkov’s Anaphora Resolution System (MARS) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
Combination of modules ,[object Object],[object Object],[object Object],[object Object],[object Object]
 
Discourse information ,[object Object],[object Object],[object Object],[object Object]
 
Conclusions ,[object Object],[object Object],[object Object],[object Object]
 
Thank you

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The role of linguistic information for shallow language processing

  • 1. The Role of Linguistic Information for Shallow Language Processing Constantin Orasan Research Group in Computational Linguistics University of Wolverhampton http://www.wlv.ac.uk/~in6093/
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