Re: I'm talking to UIMA but it's not recognizing my voice..

[Marshall Schor <ms...@schor.com>]

On 8/17/2011 10:51 AM, Digital Maven wrote:
> 1. I've install UIMA-AS on my laptop - haha! Probably not a good idea :P

I use a laptop as my development machine :-)

>
> 2. For version UIMA-AS 2.3.1, I couldn't run the org.apache.uima.tutorial.RoomNumber demo 
> (eclipse Windows Vista 32 Centrino). I installed UIMAJ 2.3.1 but still had no luck. 
>
> 3. I pointed "UIMA_HOME" environment variable to my UIMA-AS 2.3.1 folder. The CLASSPATH 
> environment variable seems ok.
>
> 4. I can't see any UIMA menu options in my eclipse environment, although I can create UIMA 
> environment PEAR files, but can't import or read them.

When you installed UIMA-AS, did you also install the Eclipse plugins (from
http://www.apache.org/dist/uima/eclipse-update-site )?  This you would need in
order to see UIMA things in the Eclipse menu picks.

>
> 4b. Can I only run the eclipse IDE from an Administrator account?

I think the Eclipse IDE needs write access to the Eclipse workspace.  But we
have users who run from shared Eclipse installations (say in Linux environments)
where the IDE itself is in read-only disk space.

>
> 4c. Is UIMAJ 2.3.1 still in beta?.

No.
>
> 5. I found an annotated dictionary database. [1] 
>
> 6. I copied both UIMAJ 2.3.1 and UIMA-AS 2.3.1 JAR files to eclipse plugins folder (this 
> was probably a bad idea)

Yes, these are not eclipse plugins, so this was a strange thing to do.

>
> 7. The entire idea for me was to create a semantics engine that could determine the 
> subject and predicate of a sentence, and then share this to the UIMA site as a PEAR 
> plugin.
>
> 8. I wish it was Friday.

Just wait a couples of days :-)

-Marshall
> REFERENCES
> 1. "Princeton Annotated Gloss Corpus" - http://wordnet.princeton.edu/glosstag.shtml
>  
> ---------------------------------------------------------------------------------
>
>
> -Abdul_Shabazz
>

Re: I'm talking to UIMA but it's not recognizing my voice..

[Digital Maven <e3...@yahoo.com>]

Ah, thanks for the vote of confidence :) I also discovered Apache OpenNLP Natural Language Processing (Thanks Apache!)

I guess now I can follow up on IBM's 2010 patent: "Method and system for automatic computation creativity and specifically for story generation" - http://www.freepatentsonline.com/7333967.pdf

They've got the theme idea right, but where they fail to realized:

A Beat of action is an event, a scene is an event, a sequence is an event, and a story is an event, are all examples of events. Each event goes through the same processes of thought

* Big_Opening: Responsible for extracting and demonstrated strong character characterization and a 
ranked list of answer-bearing documents, using a query 
formulated using information provided by the CharacterProfile + PersonalityBias. 

* Catalyst | Goal Evaluator: Responsible for encountering the 
question to determine the information needed (question type, 
answer type, key terms, etc.). 

* Big_Event | Question Analyzer:  Responsible for analyzing the 
solution space to determine if the information need is available (question type, 
answer type, key terms, etc.). 

* Pinch | Retrieval Strategist (RS):  Responsible for extracting a 
ranked list of answer-bearing documents, using a query 
formulated using information provided by the Question 
Analyzer + CharacterProfile + PersonalityBias and now dedicated to the outcome of the event. 

* Crisis | Information eXtractor (IX):  Responsible for extracting 
and scoring/ranking answer candidates from the answer 
bearing documents, based on expectation bias. 

* Climax | Answer Generator (AG):  Responsible for removing 
duplicates and selecting/filtering answers. Formulated using information 
provided by the Reversal + opposing CharacterProfile + opposing PersonalityBias. 

* Realization | Post Catalyst Analysis: A pilot-like wave is generated backwards and tries to close the Story loop (or Story event)

* Denouement: Character acts and improves based on what he's learned. (based on adjusted CharacterProfile + new PersonalityBias)

because these steps, detailing the event, is scale invariant, no state machine is needed.

The lead character should employ goals-based learning, in a way consistent with dramatic story design. As complications are introduced (search frictions), the lead character should conduct collision detections to determine if he is getting closer to, or further from, achieving his goal.

Since the story engine is a probabilistic system, it searches for the best match for this query, not just for 
strings that meet all the criteria.  For instance, a document that referenced a communication involving 
[Theme:good triumphs over evil when good has no fear of failure][Location:Woods], [Context:woods], [subtext:triump], [George:Exhausted,Max:Determined] is likely 
to be ranked highly, even if it does not fall in the data range specified.  This allows users to enter highly 
detailed queries without the risk that important documents that do not exactly match 
the query will not be found.
 
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-Jericho West