Word classification in the context
$begingroup$
I'm trying to solve a 'negation-like' classification problem, where I need to classify whether a certain word within the context has negative or positive label.
For example, how to identify whether a keyword has been prescribed or only mentioned:
['Aspirin has been prescribed to a patients'] -> {[('key_word': 'aspirin', 'prescribed': TRUE)]}
['If symptoms continue, the patient should consider taking Omeprazol'] -> {[('key_word': 'omeprazol', 'prescribed': FALSE)]}
[‘The plan is for him to commence 25mg of Trazodone as soon as he gets better.’] -> {[('key_word': 'trazodone ', 'prescribed': FALSE)]}
['her current meds are: sertraline 200 mg and olanzapine 5 mg'] -> {[('key_word': 'sertraline', 'prescribed': TRUE), ('key_word': 'olanzapine', 'prescribed': TRUE)]}
['if she continues to be depressed, then she needs to be started on Risperidone'] -> {[('key_word': 'risperidone', 'prescribed': FALSE)]}
I have a training data set for this task, but it is not clear how to formulate the classification problem. It is similar to sentiment classification problem, but here instead of predicting a label of the entire sentence, I need to predict a label of a certain keyword based on the context.
Any ideas?
classification nlp text
asked 14 hours ago
Arnold KleinArnold Klein
228312
$endgroup$
add a comment |
$begingroup$
I'm trying to solve a 'negation-like' classification problem, where I need to classify whether a certain word within the context has negative or positive label.
For example, how to identify whether a keyword has been prescribed or only mentioned:
['Aspirin has been prescribed to a patients'] -> {[('key_word': 'aspirin', 'prescribed': TRUE)]}
['If symptoms continue, the patient should consider taking Omeprazol'] -> {[('key_word': 'omeprazol', 'prescribed': FALSE)]}
[‘The plan is for him to commence 25mg of Trazodone as soon as he gets better.’] -> {[('key_word': 'trazodone ', 'prescribed': FALSE)]}
['her current meds are: sertraline 200 mg and olanzapine 5 mg'] -> {[('key_word': 'sertraline', 'prescribed': TRUE), ('key_word': 'olanzapine', 'prescribed': TRUE)]}
['if she continues to be depressed, then she needs to be started on Risperidone'] -> {[('key_word': 'risperidone', 'prescribed': FALSE)]}
I have a training data set for this task, but it is not clear how to formulate the classification problem. It is similar to sentiment classification problem, but here instead of predicting a label of the entire sentence, I need to predict a label of a certain keyword based on the context.
Any ideas?
classification nlp text
asked 14 hours ago
Arnold KleinArnold Klein
228312
$endgroup$
1
$begingroup$
You could try 3-grams and 4-grams TFIDF and then a Multinomial Naive Bayes Classifier. Is the key_word a part of the label?
$endgroup$
– Danny
10 hours ago
$begingroup$
@Danny, thanks. No, I provide thekey_wordto specify where the algorithm should be looking at (it shouldattendto my key_word and make decision about it).
$endgroup$
– Arnold Klein
9 hours ago
$begingroup$
Just make sure you remove the stop words and stem the words as well. I am afraid I still don't understand how the key_word is used. But, this approach should give you good enough results to visualise clusters and then see how can you improve your approach.
$endgroup$
– Danny
8 hours ago
add a comment |
$begingroup$
I'm trying to solve a 'negation-like' classification problem, where I need to classify whether a certain word within the context has negative or positive label.
For example, how to identify whether a keyword has been prescribed or only mentioned:
['Aspirin has been prescribed to a patients'] -> {[('key_word': 'aspirin', 'prescribed': TRUE)]}
['If symptoms continue, the patient should consider taking Omeprazol'] -> {[('key_word': 'omeprazol', 'prescribed': FALSE)]}
[‘The plan is for him to commence 25mg of Trazodone as soon as he gets better.’] -> {[('key_word': 'trazodone ', 'prescribed': FALSE)]}
['her current meds are: sertraline 200 mg and olanzapine 5 mg'] -> {[('key_word': 'sertraline', 'prescribed': TRUE), ('key_word': 'olanzapine', 'prescribed': TRUE)]}
['if she continues to be depressed, then she needs to be started on Risperidone'] -> {[('key_word': 'risperidone', 'prescribed': FALSE)]}
I have a training data set for this task, but it is not clear how to formulate the classification problem. It is similar to sentiment classification problem, but here instead of predicting a label of the entire sentence, I need to predict a label of a certain keyword based on the context.
Any ideas?
classification nlp text
asked 14 hours ago
Arnold KleinArnold Klein
228312
$endgroup$
I'm trying to solve a 'negation-like' classification problem, where I need to classify whether a certain word within the context has negative or positive label.
For example, how to identify whether a keyword has been prescribed or only mentioned:
['Aspirin has been prescribed to a patients'] -> {[('key_word': 'aspirin', 'prescribed': TRUE)]}
['If symptoms continue, the patient should consider taking Omeprazol'] -> {[('key_word': 'omeprazol', 'prescribed': FALSE)]}
[‘The plan is for him to commence 25mg of Trazodone as soon as he gets better.’] -> {[('key_word': 'trazodone ', 'prescribed': FALSE)]}
['her current meds are: sertraline 200 mg and olanzapine 5 mg'] -> {[('key_word': 'sertraline', 'prescribed': TRUE), ('key_word': 'olanzapine', 'prescribed': TRUE)]}
['if she continues to be depressed, then she needs to be started on Risperidone'] -> {[('key_word': 'risperidone', 'prescribed': FALSE)]}
I have a training data set for this task, but it is not clear how to formulate the classification problem. It is similar to sentiment classification problem, but here instead of predicting a label of the entire sentence, I need to predict a label of a certain keyword based on the context.
Any ideas?
classification nlp text
classification nlp text
asked 14 hours ago
Arnold KleinArnold Klein
228312
asked 14 hours ago
Arnold KleinArnold Klein
228312
asked 14 hours ago
Arnold KleinArnold Klein
228312
asked 14 hours ago
Arnold KleinArnold Klein
228312
asked 14 hours ago
Arnold KleinArnold Klein
228312
228312
1
$begingroup$
You could try 3-grams and 4-grams TFIDF and then a Multinomial Naive Bayes Classifier. Is the key_word a part of the label?
$endgroup$
– Danny
10 hours ago
$begingroup$
@Danny, thanks. No, I provide thekey_wordto specify where the algorithm should be looking at (it shouldattendto my key_word and make decision about it).
$endgroup$
– Arnold Klein
9 hours ago
$begingroup$
Just make sure you remove the stop words and stem the words as well. I am afraid I still don't understand how the key_word is used. But, this approach should give you good enough results to visualise clusters and then see how can you improve your approach.
$endgroup$
– Danny
8 hours ago
add a comment |
1
$begingroup$
You could try 3-grams and 4-grams TFIDF and then a Multinomial Naive Bayes Classifier. Is the key_word a part of the label?
$endgroup$
– Danny
10 hours ago
$begingroup$
@Danny, thanks. No, I provide thekey_wordto specify where the algorithm should be looking at (it shouldattendto my key_word and make decision about it).
$endgroup$
– Arnold Klein
9 hours ago
$begingroup$
Just make sure you remove the stop words and stem the words as well. I am afraid I still don't understand how the key_word is used. But, this approach should give you good enough results to visualise clusters and then see how can you improve your approach.
$endgroup$
– Danny
8 hours ago
1
1
$begingroup$
You could try 3-grams and 4-grams TFIDF and then a Multinomial Naive Bayes Classifier. Is the key_word a part of the label?
$endgroup$
– Danny
10 hours ago
$begingroup$
You could try 3-grams and 4-grams TFIDF and then a Multinomial Naive Bayes Classifier. Is the key_word a part of the label?
$endgroup$
– Danny
10 hours ago
$begingroup$
@Danny, thanks. No, I provide the
key_word to specify where the algorithm should be looking at (it should attend to my key_word and make decision about it).$endgroup$
– Arnold Klein
9 hours ago
$begingroup$
@Danny, thanks. No, I provide the
key_word to specify where the algorithm should be looking at (it should attend to my key_word and make decision about it).$endgroup$
– Arnold Klein
9 hours ago
$begingroup$
Just make sure you remove the stop words and stem the words as well. I am afraid I still don't understand how the key_word is used. But, this approach should give you good enough results to visualise clusters and then see how can you improve your approach.
$endgroup$
– Danny
8 hours ago
$begingroup$
Just make sure you remove the stop words and stem the words as well. I am afraid I still don't understand how the key_word is used. But, this approach should give you good enough results to visualise clusters and then see how can you improve your approach.
$endgroup$
– Danny
8 hours ago
add a comment |
0
active
oldest
votes
Your Answer
StackExchange.ifUsing("editor", function () {
return StackExchange.using("mathjaxEditing", function () {
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
});
});
}, "mathjax-editing");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "557"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f45375%2fword-classification-in-the-context%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
0
active
oldest
votes
0
active
oldest
votes
active
oldest
votes
active
oldest
votes
Thanks for contributing an answer to Data Science Stack Exchange!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f45375%2fword-classification-in-the-context%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
1
$begingroup$
You could try 3-grams and 4-grams TFIDF and then a Multinomial Naive Bayes Classifier. Is the key_word a part of the label?
$endgroup$
– Danny
10 hours ago
$begingroup$
@Danny, thanks. No, I provide the
key_wordto specify where the algorithm should be looking at (it shouldattendto my key_word and make decision about it).$endgroup$
– Arnold Klein
9 hours ago
$begingroup$
Just make sure you remove the stop words and stem the words as well. I am afraid I still don't understand how the key_word is used. But, this approach should give you good enough results to visualise clusters and then see how can you improve your approach.
$endgroup$
– Danny
8 hours ago