Hi,
I am an academic and am currently using entity enrichment for some machine learning problems. I have found the "Handbook of Data Mining" by Ye to have a wonderfull chapter on Clear-Forest and the engine that open-calais has evolved from, since in academic work I have to account for all system element details.
However I would like to know how the system (at least roughly) computes the RELEVANCE SCORE!!? I woul very much appreciate if you could get back to me on this.
I presume it would most likely be some TF-IDF value of the Entity Term, normalised into 0-1 range. However for counts of 1 the score varies quite a bit so I wonder what this score is based on? Does it maybe take into consideration the relationships and types of relationships found with the entity in question?
Thanks a lot,
Martin
ps.: I already asked it on the general geek topic forum-section, however this might seem a better place to ask this question. Thanks.
Hi,
I am an academic and am currently using entity enrichment for some machine learning problems. I have found the "Handbook of Data Mining" by Ye to have a wonderfull chapter on Clear-Forest and the engine that open-calais has evolved from, since in academic work I have to account for all system element details.
However I would like to know how the system (at least roughly) computes the RELEVANCE SCORE!!? I woul very much appreciate if you could get back to me on this.
I presume it would most likely be some TF-IDF value of the Entity Term, normalised into 0-1 range. However for counts of 1 the score varies quite a bit so I wonder what this score is based on? Does it maybe take into consideration the relationships and types of relationships found with the entity in question?
Thanks a lot,
Martin
ps.: I already asked it on the general geek topic forum-section, however this might seem a better place to ask this question. Thanks.
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