Thanks a lot, Timothy. I really appreciate your help.
On Wed, Jan 16, 2019 at 8:16 AM Miller, Timothy < [email protected]> wrote: > No, SHARPn was a later project. I'm not sure if there is any overlap in > the datasets. > > There are 2 ways to look at the features, one is to read this paper: > https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0112774 > > and another is to look at the source: > > http://svn.apache.org/viewvc/ctakes/trunk/ctakes-assertion/src/main/java/org/apache/ctakes/assertion/medfacts/cleartk/AssertionCleartkAnalysisEngine.java?view=markup > > Tim > > -----Original Message----- > *From*: ouyeyu panyu <[email protected] > <ouyeyu%20panyu%20%[email protected]%3e>> > Reply-to: <[email protected]> > *To*: [email protected] > *Cc*: [email protected] <[email protected] > <%[email protected]%22%20%[email protected]%3e>> > *Subject*: Re: Question about negation [EXTERNAL] > *Date*: Wed, 16 Jan 2019 08:09:06 -0800 > > Hi Timothy, > > Thank you very much for the quick response. > > > https://pdfs.semanticscholar.org/8f2c/a8b638d216a3e9ec10cd1c21bdaeaa74a229.pdf > <https://urldefense.proofpoint.com/v2/url?u=https-3A__pdfs.semanticscholar.org_8f2c_a8b638d216a3e9ec10cd1c21bdaeaa74a229.pdf&d=DwMFaQ&c=qS4goWBT7poplM69zy_3xhKwEW14JZMSdioCoppxeFU&r=Heup-IbsIg9Q1TPOylpP9FE4GTK-OqdTDRRNQXipowRLRjx0ibQrHEo8uYx6674h&m=bdfSiGGOpy6_mnRe0CZd0-wjjUpY-DH7SrOU5_WMkZE&s=UhoZqDN8rO9tb4R791cI7gKRT7zn_O2yZ8VZpbsD3Ek&e=> > says > The Mayo-derived linguistically annotated corpus (Mayo) was developed > in-house and consisted of 273 clinical notes (100 650 tokens; 7299 > sentences; 61 consult; 1 discharge summary; 4 educational visit; 4 general > medical examination; 48 limited exam; 19 multi-system evaluation; 43 > miscellaneous; 1 preoperative medical evaluation; 3 report; 3 specialty > evaluation; 5 dismissal summary; 73 subsequent visit; 5 therapy; 3 > test-oriented miscellaneous). > > Is SHARPn based on the aforementioned 273 clinical notes? > Also is there a way for me to look into the trained SVM model? Say what > are features there and their weights? > > Best, > Yu Pan > > > On Wed, Jan 16, 2019 at 7:58 AM Miller, Timothy < > [email protected]> wrote: > > It uses an SVM model. The training data is from a project called SHARPn, > it is notes from Mayo Clinic with a variety of note types and specialties > represented. > > As for the example, is it a real example that someone wrote "Deny > hepatitis"? That sounds more like a command than documentation of a negated > concept ("denies" or "denied" would seem more common?). Even if that is a > real example, I think it's unusual enough that there are probably not > examples of "Deny X" in the training data. > > Tim > > > -----Original Message----- > *From*: ouyeyu panyu <[email protected] > <ouyeyu%20panyu%20%[email protected]%3e>> > Reply-to: <[email protected]> > *To*: [email protected], [email protected] > *Subject*: Question about negation [EXTERNAL] > *Date*: Wed, 16 Jan 2019 07:51:20 -0800 > > Hi ctakes dev team, > > > I have one question, hope someone can help me with it. > > For negation, "Denies hepatitis” returns polarity=-1, but "Deny hepatitis” > returns polarity=1. > > It is said CTAKES uses ClearTK’s PolarityCleartkAnalysisEngine for > negation, which is machine learning based. > > It seems this issue is caused by the training data. Is this true? And what > is the training data and what machine learning algorithm is used? > LogisticRegress, SVM, RandomForest or something else? > > Thanks. > >
