Parsing/identifying sections in job descriptions
I'm trying to solve quite a difficult problem - building a generic parser for job descriptions. The idea is, given a job description, the parser should be able to identify and extract different sections such as job title, location, job description, responsibilities, qualifications etc. The job description will basically be scraped from a web page.
A rule based approach (such as regular expressions) doesn't work since the scenario is too generic. My next approach was to train a custom NER classifier using SpaCy; I've done this numerous times before. However, I'm running into several problems.
The entities can be very small in size (location, job title etc.) or very large (responsibilities, qualifications etc.). I'm not sure how well NER works if the entities are several lines or a paragraph long? Most of the use cases I've seen are those in which the entities aren't longer than a few words max. Does Spacy's NER work well if the text of the entities I want to identify is quite long in size? (I can give examples if required to make it clearer).
Is there any other strategy besides NER that I can use to parse these job descriptions as I've mentioned?
Any help here would be greatly appreciated. I've been banging my head along different walls for a few months, and I have made some progress, but I'm not sure if I'm on the right track, or if a better approach exists.
python parsing nlp spacy ner
asked Jan 3 at 13:41
Azfar ImtiazAzfar Imtiaz
12
add a comment |
I'm trying to solve quite a difficult problem - building a generic parser for job descriptions. The idea is, given a job description, the parser should be able to identify and extract different sections such as job title, location, job description, responsibilities, qualifications etc. The job description will basically be scraped from a web page.
A rule based approach (such as regular expressions) doesn't work since the scenario is too generic. My next approach was to train a custom NER classifier using SpaCy; I've done this numerous times before. However, I'm running into several problems.
The entities can be very small in size (location, job title etc.) or very large (responsibilities, qualifications etc.). I'm not sure how well NER works if the entities are several lines or a paragraph long? Most of the use cases I've seen are those in which the entities aren't longer than a few words max. Does Spacy's NER work well if the text of the entities I want to identify is quite long in size? (I can give examples if required to make it clearer).
Is there any other strategy besides NER that I can use to parse these job descriptions as I've mentioned?
Any help here would be greatly appreciated. I've been banging my head along different walls for a few months, and I have made some progress, but I'm not sure if I'm on the right track, or if a better approach exists.
python parsing nlp spacy ner
asked Jan 3 at 13:41
Azfar ImtiazAzfar Imtiaz
12
Exactly how much variation is in these job descriptions since you say they come from "a" (singular) web page? You could probably pull out location with NER, but you might have more success getting something like job responsibilities with dependency parsing.
– Alex L
Jan 3 at 21:41
@AlexL The job descriptions are not coming from a singular web page. It can be different job sites such as Indeed, Monster, Google Jobs etc. Location isn't that hard since there are APIs for that, and NER works better for that too. Can you elaborate more on how I can get job responsibilities via dependency parsing?
– Azfar Imtiaz
Jan 4 at 14:50
add a comment |
I'm trying to solve quite a difficult problem - building a generic parser for job descriptions. The idea is, given a job description, the parser should be able to identify and extract different sections such as job title, location, job description, responsibilities, qualifications etc. The job description will basically be scraped from a web page.
A rule based approach (such as regular expressions) doesn't work since the scenario is too generic. My next approach was to train a custom NER classifier using SpaCy; I've done this numerous times before. However, I'm running into several problems.
The entities can be very small in size (location, job title etc.) or very large (responsibilities, qualifications etc.). I'm not sure how well NER works if the entities are several lines or a paragraph long? Most of the use cases I've seen are those in which the entities aren't longer than a few words max. Does Spacy's NER work well if the text of the entities I want to identify is quite long in size? (I can give examples if required to make it clearer).
Is there any other strategy besides NER that I can use to parse these job descriptions as I've mentioned?
Any help here would be greatly appreciated. I've been banging my head along different walls for a few months, and I have made some progress, but I'm not sure if I'm on the right track, or if a better approach exists.
python parsing nlp spacy ner
asked Jan 3 at 13:41
Azfar ImtiazAzfar Imtiaz
12
I'm trying to solve quite a difficult problem - building a generic parser for job descriptions. The idea is, given a job description, the parser should be able to identify and extract different sections such as job title, location, job description, responsibilities, qualifications etc. The job description will basically be scraped from a web page.
A rule based approach (such as regular expressions) doesn't work since the scenario is too generic. My next approach was to train a custom NER classifier using SpaCy; I've done this numerous times before. However, I'm running into several problems.
The entities can be very small in size (location, job title etc.) or very large (responsibilities, qualifications etc.). I'm not sure how well NER works if the entities are several lines or a paragraph long? Most of the use cases I've seen are those in which the entities aren't longer than a few words max. Does Spacy's NER work well if the text of the entities I want to identify is quite long in size? (I can give examples if required to make it clearer).
Is there any other strategy besides NER that I can use to parse these job descriptions as I've mentioned?
Any help here would be greatly appreciated. I've been banging my head along different walls for a few months, and I have made some progress, but I'm not sure if I'm on the right track, or if a better approach exists.
python parsing nlp spacy ner
python parsing nlp spacy ner
asked Jan 3 at 13:41
Azfar ImtiazAzfar Imtiaz
12
asked Jan 3 at 13:41
Azfar ImtiazAzfar Imtiaz
12
asked Jan 3 at 13:41
Azfar ImtiazAzfar Imtiaz
12
asked Jan 3 at 13:41
Azfar ImtiazAzfar Imtiaz
12
asked Jan 3 at 13:41
Azfar ImtiazAzfar Imtiaz
12
12
Exactly how much variation is in these job descriptions since you say they come from "a" (singular) web page? You could probably pull out location with NER, but you might have more success getting something like job responsibilities with dependency parsing.
– Alex L
Jan 3 at 21:41
@AlexL The job descriptions are not coming from a singular web page. It can be different job sites such as Indeed, Monster, Google Jobs etc. Location isn't that hard since there are APIs for that, and NER works better for that too. Can you elaborate more on how I can get job responsibilities via dependency parsing?
– Azfar Imtiaz
Jan 4 at 14:50
add a comment |
Exactly how much variation is in these job descriptions since you say they come from "a" (singular) web page? You could probably pull out location with NER, but you might have more success getting something like job responsibilities with dependency parsing.
– Alex L
Jan 3 at 21:41
@AlexL The job descriptions are not coming from a singular web page. It can be different job sites such as Indeed, Monster, Google Jobs etc. Location isn't that hard since there are APIs for that, and NER works better for that too. Can you elaborate more on how I can get job responsibilities via dependency parsing?
– Azfar Imtiaz
Jan 4 at 14:50
Exactly how much variation is in these job descriptions since you say they come from "a" (singular) web page? You could probably pull out location with NER, but you might have more success getting something like job responsibilities with dependency parsing.
– Alex L
Jan 3 at 21:41
Exactly how much variation is in these job descriptions since you say they come from "a" (singular) web page? You could probably pull out location with NER, but you might have more success getting something like job responsibilities with dependency parsing.
– Alex L
Jan 3 at 21:41
@AlexL The job descriptions are not coming from a singular web page. It can be different job sites such as Indeed, Monster, Google Jobs etc. Location isn't that hard since there are APIs for that, and NER works better for that too. Can you elaborate more on how I can get job responsibilities via dependency parsing?
– Azfar Imtiaz
Jan 4 at 14:50
@AlexL The job descriptions are not coming from a singular web page. It can be different job sites such as Indeed, Monster, Google Jobs etc. Location isn't that hard since there are APIs for that, and NER works better for that too. Can you elaborate more on how I can get job responsibilities via dependency parsing?
– Azfar Imtiaz
Jan 4 at 14:50
add a comment |
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Exactly how much variation is in these job descriptions since you say they come from "a" (singular) web page? You could probably pull out location with NER, but you might have more success getting something like job responsibilities with dependency parsing.
– Alex L
Jan 3 at 21:41
@AlexL The job descriptions are not coming from a singular web page. It can be different job sites such as Indeed, Monster, Google Jobs etc. Location isn't that hard since there are APIs for that, and NER works better for that too. Can you elaborate more on how I can get job responsibilities via dependency parsing?
– Azfar Imtiaz
Jan 4 at 14:50