I'm developing a application to categorize requirements in a requirement specification in to categories like database, front end, back end, etc. Requirement specification is a single document where I want to see the underlying categories in it. Can I use NER to get the categories? Sentences are divided in to categories if they contain certain words that match that particular category.
Example
data should be stored in a secured database.
If we consider above given sentence is a requirement it should be categorized in to database category considering the words it contains (database, data).
To my best of knowledge, NER pre-built tools would not help, but I recommend you to use spaCy. It is a NER tools with state-of-the-art accuracy and support re-train your DL model and customize it. Hope this helps!
Related
My task is to extract information from a various web-pages of a particular site. Now, the information to be extracted can be of the form as product name, product id, price, etc. The information is given in text using natural language. Also, I have been asked to extract that information using some Machine Learning algorithm. I thought of using NER (Named Entity Recognition) and training it on custom training data (which I can prepare using the scraped data and manually labeling the integers/data as required). I wanted to know if the model can even work this way?
Also, let me know if I can improve this question further.
You say a particular site. I am assuming that it means you have some fair idea of what the structures of webpages are, if the data is in table form or a free text form, how the website generally looks. In this case, a simple regex (prices, ids etc) supported by some POS tagger to extract product names and all is enough for you. A supervised approach is definitely an overkill and might underperform than the simpler regex.
I am using AngelList DB to categorize startups based on their industries since these startups are categorized based on community input which is misleading most of the time.
My business objective is to extract keywords that indicate to which industry this specific startup belongs to then map it to one of the industries specified in LinkedIn sheet https://developer.linkedin.com/docs/reference/industry-codes
I experimented with Azure Machine learning, where I pushed 300 startups descriptions and analyzed the keyword extraction was pretty bad and was not even close to what I am trying to achieve.
I would like to know how data scientists will approach this problem? where should I look? and where I should not? is keyword analysis tools (like Google Adwords keyword planner is a viable option)
Using Text Classification...
To be able to treat this as a classification problem, you need a training set, which is a set of AngelList entries that are labeled with correct LinkedIn categories. This can be done manually, or you can hire some Mechanical Turks to do the job for you.
Since you have ~150 categories, I'd imagine you need at least 20-30* AngelList entries for each of them. So your training set will be {input: angellist_description, result: linkedin_id}
After that, you need to dig through text classification techniques to try and optimize the accuracy/precision of your results. The book "Taming Text" has a full chapter on text classification. And a good tool to implement a text-based classifier would be Apache Solr or Apache Lucene.
* 20-30 is a quick personal estimate and not based on a scientific method. You can look up some methods online for a good estimation method.
Using Text Clustering.
Step #1
Use text clustering to extract main 'topics' from all the descriptions. (Carrot2 can be helpful here)
Input corpus of all descriptions
Process: Text Clustering using Carrot2
Output each document will be labeled with a topic
Step #2
Manually map the extracted topics into LinkedIn's categories.
Step #3
Use the output of the first two steps to traverse from company -> extracted topic -> linkedin category
So I have a data set which consists of tweets from various news organizations. I've loaded it into RapidMiner, tokenized it, and produced some n-grams of it. Now I want to be able to have RapidMiner automatically classify my data into various categories based on the topic of the tweets.
I'm pretty sure RapidMiner can do this, but according to the research I've done into it, I need a training data set to be able to show RapidMiner how I want things classified. So I need a training data set, though given the categories I wanted to classify things into, I might have to create my own.
So my questions are these:
1) Is there a training data set for twitter data that focuses more on the topic of the tweet as opposed to a sentiment analysis publicly available?
2) If there isn't one publicly available, how can I create my own? My idea to do it was to go through the tweets themselves and associate the tokens and n-grams with the categories I want. Some concerns I have with that are that I won't be able to manually classify enough tweets to create a training data set comprehensive enough so that I can get a good accuracy rate for the automatic classifier.
3) Any general advice for topical classification of text data would be great. This is the first time that I've done a project like this, and I'm sure there are things I could improve on. :)
There may be training corpora that work for you, but you need to say what your topic or categories are to identify it. The fact that this is Twitter may be relevant, but the data source is likely to be much less relevant to the classification accuracy you will achieve than the topic is. So if you take the infamous 20 newsgroups data set this is likely to work on Twitter as well, but only if the categories you are after are the 20 categories from that data set. If you want to classify cats vs dogs or Android vs iPhone you need to find a data set for that.
In most cases you will have to create initial labels manually, which is, as you say, a lot of work. One workaround might be to start with something simpler like a keyword search to create subsets of your tweets for which you know they deal with a particular category. Then you create the model on top of that and hope that it generalizes to identify the same categories even though the original keywords do not occur.
Alternatively, depending on your application (and if you actually want to build an applicaion), you may as well start with only a small data set and accept that you have poor classification. Then you generate classifications, show them to the users of your apps, and collect some form of explicit or implicit feedback on the classification (e.g. users can flag tweets as incorrectly classified). This way you improve your training corpus and periodically update your model.
Finally, if you do not know what your topics are and you want RapidMiner to identify the topics, you may want to try clustering as opposed to classification. Just create a few clusters and look at the top words for each cluster. They may well be quite dissimilar and describe what the respective clusters are about.
I believe your third question may be a bit broad for stackoverflow and is probably better answered by a text book.
I have know how to communicate with twitter and how to retrieve tweets but I am looking for further working on these tweets.
I have two categories food and sports. Now I want to categorize tweets into food and sports. Can anyone please suggest me how to categorize on basis of computer algorithm?
regards
Gaurav
I've been doing some work recently with Latent Dirichlet Allocation. The general idea is that documents contain words that are generated from topics. What you could try doing is loading a corpus of documents known to be about the topics you are interested in, update with the tweets of interest, and then select tweets that have strong probabilities for the same topics as your known documents.
I use R for LDA (package:topicmodels and package:lda), but I think there are some prebuilt python tools for this too. I would probably steer away from trying to write your own unless you have a solid grounding in Bayesian statistics.
Here's the documentation for the topicmodels package: http://cran.r-project.org/web/packages/topicmodels/vignettes/topicmodels.pdf
I doubt that a set of algorithm could possibly categorize tweets in open domain. In other words I don't think a set of rules can possibly categorizes open domain tweets. You need to parse tweets into a semantic representation customized for the categorization.
I have a bunch of text documents that describe diseases. Those documents are in most cases quite short and often only contain a single sentence. An example is given here:
Primary pulmonary hypertension is a progressive disease in which widespread occlusion of the smallest pulmonary arteries leads to increased pulmonary vascular resistance, and subsequently right ventricular failure.
What I need is a tool that finds all disease terms (e.g. "pulmonary hypertension" in this case) in the sentences and maps them to a controlled vocabulary like MeSH.
Thanks in advance for your answers!
Here are two pipelines that are specifically designed for medical document parsing:
Apache cTAKES
NLM's MetaMap
Both use UMLS, the unified medical language system, and thus require that you have a (free) license. Both are Java and more or less easy to set up.
See http://www.ebi.ac.uk/webservices/whatizit/info.jsf
Whatizit is a text processing system that allows you to do textmining
tasks on text. The tasks come defined by the pipelines in the drop
down list of the above window and the text can be pasted in the text
area.
You could also ask biostars: http://www.biostars.org/show/questions/
there are many tools to do that. some popular ones:
NLTK (python)
LingPipe (java)
Stanford NER (java)
OpenCalais (web service)
Illinois NER (java)
most of them come with some predefined models, i.e. they've already been trained on some general datasets (news articles, etc.). however, your texts are pretty specific, so you might want to first constitute a corpus and re-train one of those tools, in order to adjust it to your data.
more simply, as a first test, you can try a dictionary-based approach: design a list of entity names, and perform some exact or approximate matching. for instance, this operation is decribed in LingPipe's tutorial.
Open Targets has a module for this as part of LINK. It's not meant to be used directly so it might require some hacking and tinkering, but it's the most complete medical NER (named entity recognition) tool I've found for python. For more info, read their blog post.
a bash script that has as example a lexicon generated from the disease ontology:
https://github.com/lasigeBioTM/MER