[Corpora-List] TextGraphs-6 and semantic networks

[Nathan Hu]

I strongly agree your knowledge representation model. Actually, I want to do
the similar thing. The bottleneck currently is how can we get the
high-precision results of coreference resolution to build completed
Conceptual Graphs from texts. So I focus on coreference resolution now.

I believe the precision of coreference resolution is more important than
recall and f-measure, especially for the secondary training set in
simi-supervised learning.

On Sun, Mar 13, 2011 at 4:44 AM, John F. Sowa <sowa at bestweb.net> wrote:

> On 3/12/2011 12:12 AM, Nathan Hu wrote:
>
>> I believe graph based knowledge representation models are on the right
>> direction.
>>
>> I proposed an instance based knowledge network and a graph matching
>> based method for word sense disambiguation recently.
>> (Incorporating Coreference Resolution into Word Sense Disambiguation -
>> Cicling2011)
>>
>> I'd like to discuss with anyone interested at this TextGraphs topic
>>
>
> I agree that such methods are promising.  And I very strongly agree
> with the opening sentences in the abstract of your article:
>
>  Word sense disambiguation (WSD) and coreference resolution are two
>> fundamental tasks for natural language processing. Unfortunately, they
>> are seldom studied together.In this paper, we propose to incorporate
>> the coreference resolution technique into a word sense disambiguation
>> system for improving disambiguation precision.
>>
>
> We do something similar at our company (VivoMind Research, LLC), and
> it has proved to very effective.  In fact, we store and index all the
> graphs that have been generated from previous sentences in the same
> text or related texts in a corpus.  Then we use high-speed methods
> to retrieve similar graphs in logarithmic time.  We use them for
> both disambiguation and coreference resolution.
>
> The following slides show some applications that illustrate the methods:
>
>   http://www.jfsowa.com/talks/pursue.pdf
>
> John
>
>
>
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