I am not an expert in Machine Learning, so I will try to be as accurate as possible...
I am currently analyzing financial documents that are giving information on a specific fund. What I would like to do is to be able to extract the fund name.
For this, I am using Named Entity Recognition (NER) in Azure Machine Learning platform. After analyzing approx. 100 documents, I get results classified as Organizations. In most cases, they are really organizations. This is great, but my problem is that the fund name is also categorized as an organization. I am not able to distinguish between a company name and a fund name.
From some readings on Internet, I could discover that Gazette system could help so that we can match the recognized organizations against a list of funds, and therefore make sure that we have a fund name.
Do you think this would be a good approach? Or is there any other algorithm that I should try to improve the results?
Thanks for any suggestion!
NER has its origins in identifying text identifying broad semantic categories, like the names of people or organizations (companies) in your case. Reading the description of question, I don't think this is the problem you really want to solve. Specifically you mention:
that Gazette system could help so that we can match the recognized organizations against a list of funds
I suspect the problem you really want to solve is one of semantic interoperability - you want text from your NLP program to match a list you have that is part of another system. In that case, the only accepted way you are going to solve your problem is to map all of the input text to a list/common standard - ie) use the gazetteer. So you are on the right path.
The only caveat is that if you only need to distinguish between funds and other types of organizations - without the need to match the results against a list. If that is the case, you write a classifier to distinguish funds from everything else and you can avoid mapping to your list entirely. Otherwise use a gazetteer.
Related
In Salesforce's Service Cloud one can enable the out of the box search function where the user enters a term and the system searches all parts of the database for a match. I would like to enable smart searching of acronyms so that if I spell an organizations name the search functionality will also search for associated acronyms in the database. For example, if I search type in American Automobile Association, I would also get results that contain both "American Automobile Association" and "AAA".
I imagine such a script would involve declaring that if the term being searched contains one or more spaces or periods, take the first letter of the first word and concatenate it with the letters that follow subsequent spaces or periods.
I have unsuccessfully tried to find scripts for this or articles on enabling this functionality in Salesforce. Any guidance would be appreciated.
Interesting question! I don't think there's a straightforward answer but as it's standard search functionality, not 100% programming related - you might want to cross-post it to salesforce.stackexchange.com
Let's start with searchable fields list: https://help.salesforce.com/articleView?id=search_fields_business_accounts.htm&type=0
In Setup there's standard functionality for Synonyms, quite easy to use. It's not a silver bullet though, applies only to certain objects like Knowledge Base (if you use it). Still - it claims to work on Cases too so if there's "AAA" in Case description it should still be good enough?
You could also check out the trick with marking a text field as indexed and/or external ID and adding there all your variations / acronyms: https://success.salesforce.com/ideaView?id=08730000000H6m2 This is more work, to prepare / sanitize your data upfront but it's not a bad idea.
Similar idea would be to use Tags although that could explode in size very quickly. It's ridiculous to create a tag for every single company.
You can do some really smart things in data deduplication rules. Too much to write it all here, check out the trailhead: https://trailhead.salesforce.com/en/modules/sales_admin_duplicate_management/units/sales_admin_duplicate_management_unit_2 No idea if it impacts search though.
If you suffer from bad address data there are State & Country picklists, no more mess with CA / California / SoCal... https://resources.docs.salesforce.com/204/latest/en-us/sfdc/pdf/state_country_picklists_impl_guide.pdf Might not help with Name problem...
Data.com cleanup might help. Paid service I think, no idea if it affects search too. But if enabling it can bring these common abbreviations into your org - might be better than reinventing the wheel.
I have a collection of bills and Invoices, so there is no context in the text (i mean they don't tell a story).
I want to extract people names from those bills.
I tried OpenNLP but the quality of trained model is not good because i don't have context.
so the first question is: can I train model contains only people names without context? and if that possible can you give me good article for how i build that new model (most of the article that i read didn't explain the steps that i should made to build new model).
I have database name with more than 100,000 person name (first name, last name), so if the NER systems don't work in my case (because there is no context), what is the best way to search for those candidates (I mean searching for every first name with all other last names?)
thanks.
Regarding "context", I guess you mean that you don't have entire sentences, i.e. no previous / next tokens, and in this case you face quite a non-standard NER. I am not aware of available software or training data for this particular problem, if you found none you'll have to build your own corpus for training and/or evaluation purposes.
Your database of names will probably greatly help, depending indeed on what proportion of bill names are actually present in the database. You'll also probably have to rely on character-level morphology of names, as patterns (see for instance patterns in [1]). Once you have a training set with features (presence in database, morphology, other information of bill) and solutions (actual names of annotated bills), using standard machine-learning as SVM will be quite straightforward (if you are not familiar with this, just ask).
Some other suggestions:
You may most probably also use other bill's information: company name, positions, tax mentions, etc.
You may also proceed in a a selective manner - if all bills should mention (exactly?) one person name, you may exclude all other texts (e.g. amounts, tax names, positions etc.) or assume in a dedicated model that among all text in a bill, only one should be guessed as a name.
[1] Ranking algorithms for named-entity extraction: Boosting and the voted perceptron (Michael Collins, 2002)
I'd start with some regular expressions, then possibly augment that with a dictionary-based approach (i.e., big list of names).
No matter what you do, it won't be perfect, so be sure to keep that in mind.
My friend has a small business where customers order services using email. He receives several emails a day and sorting thru it is becoming cumbersome.
There are about 10 different kind of tasks the customer can request, and for each there are one or two words that specify it. The other info present in the emails is the place where the service is to be delivered, the time, and the involved people's names. The email also contains an ID, a long number with a fairly standard format.
The emails are very unstructured, but all contain the key info above. My question is: what is the best method to sweep thru these emails and extract the key info (such as type of service, place, people's names, the ID etc)?
I thought about some kind of pre-processing, then pass it thru AlchemyAPI and then test the Alchemy output using Neural Networks for each feature (key info). This can be supervised learning as I can do a feedback loop all the time, as once the info is inputted, I can have someone to validate.
Any ideas? Thanks
I guess some parts (ID, task, time) can be captured by a regular expression and dictionary matching. Have a look at GATE's JAPE tool.
It should be fairly easy to assemble a dictionary and then use the lookups for the "task", also you can reuse the available jape rules for date/time and write a new one for the ID (also, a simple regex could be fine).
For matching the location and people's names you should be careful, openCalais and alchemyAPI can give you good results if names and places are used in well defined sentences and will probably make more mistakes with some tabular or weird format. Also you can never be sure you captured the place and person correctly so don't rely on that for processing orders directly.
If you have more information about mails' structure or expected names and places (i.e. you have a "clients" table with all possible names), you would probably want to do your own tagging, otherwise I'd stick to openCalais or alchemyAPI + some regular expressions.
P.S. I assume all mails are in English.
I have no clue of where to start on this. I've never done any NLP and only programmed in Python 3.1, which I have to use. I'm looking at the site http://www.linkedin.com and I have to gather all of the public profiles and some of them have very fake names, like 'aaaaaa k dudujjek' and I've been told I can use NLP to find the real names, where would I even start?
This is a difficult problem to solve, and one which starts with acquiring valid given name & surname lists.
How large is the set of names that you're evaluating, and where do they come from? These are both important things for you to consider. If you're evaluating a small set of "American" names, your valid name lists will differ greatly from lists of Japanese or Indian names, for instance.
Your idea of scraping LinkedIn is on the right track, but you were right to catch the fake profile/name flaw. A better website would probably be something like IMDB (perhaps scraping names by iterating over different birth years), or Wikipedia's lists of most popular given names and most common surnames.
When it comes down to it, this is a precision vs. recall problem: in order to miss fewer fakes, you're inevitably going to throw out some real names. If you loosen up your restrictions, you'll get more fakes, but you'll also throw out fewer real names.
Several possibilities here, but the most obvious seems to be with HMMs, i.e. Hidden Markov Models. The NLTK kit includes [at least] one module for HMMs, although I must admit I never used it.
Another possible snag is that AFAIK, NTLK is not yet ported to Python 3.0
This said, and while I'm quite keen on using NLP techniques where applicable, I think that a process which would use several paradigms, including some NLP tricks may be a better solution for this particular problem. For example, storing even a reduced dictionary of common family names (and first names) in a traditional database may offer both a more reliable and more computationally efficient way of filtering a significant portion of the input data, leaving precious CPU resources to be spent on less obvious cases.
i am afraid this problem is not solveable if your list is even only minimally ‘open’ — if the names are eg customers from a small traditionally acting population, you might end up with a few hundred names for thousands of people. but generally you can hardly predict what is a real name and what is not, however unusual an arabic, chinese, or bantu name may look in a sample of, say, south english rural neighborhood names. i mean, ‘Ng’ is a common cantonese surname, and ‘O’ is common in korea, so assumptions may fail. there is this place in austria called ‘fucking’, so even looking out for four letter words is no guarantee for success.
what you could do is work through a sufficiently big sample of such names and sort them out manually. then, use all kinds of textprocessing tools and collect metrics. maybe you can derive a certain likelyhood for a name to be recognized as fake, maybe it will not be viable. you will never go beyond likelyhoods here, though.
as an aside, we used to use google maps and the telephone directory for validating customer data years ago. if google maps could find the place, we called the address validated. it is clear that under stricter requirements, true validation must go much further. let’s not forget the validation of such data is much more a social question than a linguistic one.
I am currently working on project where I have to match up a large quantity of user-generated names with a separate list of the same names in a canonical format. The problem is that the user-generated names contains numerous misspellings, abbreviations, as well as simply invalid data, making it hard to do a cross-reference with the canonical data. Any suggestions on methods to do this?
This does not have to be done in real-time and in this case accuracy is more important than speed.
Current ideas for this are:
Do a fuzzy search for the user entered name in the canonical database using an existing search implementation like Lucene or Sphinx, which I presume use something like the Levenshtein distance for this.
Cross-reference on the SOUNDEX hash (which is supposedly computed on the sound of the name rather than spelling) instead of using the actual name.
Some combination of the above
Anyone have any feedback on any of these or ideas of their own?
One of my concerns is that none of the above methods will handle abbreviations very well. Can anyone point me in a direction for some machine learning methods to actually search on expanded abbreviations (or tell me I'm crazy)? Thanks in advance.
First, I'd add to your list the techniques discussed at Peter Norvig's post on spelling correction.
Second, I'd ask what kind of "user-generated names" you're talking about. Having dealt with both, I believe that the heuristics you'd use for street names are somewhat different from the heuristics for person names. (As a simple example, does "Dr" expand to "Drive" or "Doctor"?)
Third, I'd look at a combination using testing to establish the set of coefficients for combining the results of the various techniques.