The .5 confidence is for words not in the corpus at all ... So the strength
assigned to these words is kinda a arbitrary small number ... Maybe .5 is
too big a confidence but it doesn't matter much, since taking corpus
appearance as a proxy for concept frequency is a hack anyway ...  I mean ,
a rare word could connote a common concept and we are not trying to account
for that here .. But we are playing fast and loose here for basic
experimentation ...

On Tuesday, November 26, 2013, Cosmo Harrigan wrote:

> In opencog/python/conceptnet/to_scm.py:
>
> > +		return stv
> > +	except KeyError:
> > +		if ("  "+ word.upper()) in  corpus_dict:
> > +			stv.mean = float(corpus_dict[("  "+ word.upper())])/twf
> > +			stv.count = .95 # have no reason for this value
> > +			conceptnet_dict[word] = stv
> > +			return stv
> > +		else:	
> > +			stv.mean = 1/(twf + 1)
> > +			stv.count = .95  # have no reason for this value
> > +			conceptnet_dict[word] = stv
> > +			return stv
> > +		
> > +
> > +def write_file(cn_assertion, context): # assertion is a list
> > +	DEFAULT_TV = TruthValue(1,0.5) # 0.5 confidence is used(for links) because that is the weight given to most of the assertions on ConceptNet
>
> I understood about the approach with the node probabilities... I was
> actually curious about the 0.5 choice for link confidence.
>
> —
> Reply to this email directly or view it on GitHub<https://github.com/opencog/opencog/pull/403/files#r7911064>
> .
>


-- 
Ben Goertzel, PhD
http://goertzel.org

"In an insane world, the sane man must appear to be insane". -- Capt. James
T. Kirk