I have data of phone numbers and village names collected from the villagers via forms. Because of various reasons the data is inaccurate or incomplete.
The idea is to validate these two data points before adding them to the data base/store.
The phone numbers are being formatted programmatically and validated via an external API. (That gives me the service provider and province information).
The problem is with the addresses.
No standardized address line. Tons of ambiguity.
Numeric street names and door numbers exist.
Input string will sometimes contain an addressee.
Possible solutions I can think of
Reverse geocoding helps. But not very accurate when it comes to Indian context. The Google TOS also prohibits automated queries. (correct me if I'm wrong here)
Soundexing. Again not very accurate with Indian data.
I understand it's difficult to such highly unstructured data, but I'm looking for a ways to achieve atleast enough accuracy to map addresses to the nearest point of interest.
Queries
Given a village name from the villager who might spell it wrong or incorrectly or abbreviate it how do I get the correct official name of the village and location?
Any possible ways to sanitize bad location/addresses or decode complex/poorly formed addresses?
Are there any machine learning solutions that can help so I can learn from every computation?(I have 0 knowledge on ML, do correct me if I'm wrong here.)
What you want is a geolocation system that works with informal text input. I have a previously used a Text-based geolocation model trained on Twitter data.
To solve your problem, you need training data in the form of:
informal_text village_name
If you have access to such data (e.g. using the addresses which can be geolocated) then you can train a text-based classifier that given a new informal address can predict where on the map it points to. In your case every village becomes a class label. You can use scikit-learn to train the classifier.
Related
So, a little bit on my problem.
TL;DR
Can I use machine-learning instead of Elastic Search to find results depending on the user's text input? Is it a good idea?
I am working on a car spare parts project, and we have split the car into 300 parts that we store on the database, with some data for each part (weight, availability, etc).
When the customer inputs the text of his part, we need to be able to classify the part, and map it to one in our database.
The current way it's being done is by people on our team manually mapping the customer text with the parts on our database, we want to automate that process.
We tried using MongoDB text search, but it was often inaccurate since parts have different names in different parts of the country.
So we wanted something that got more accurate results, and improved by the more data we have, we immediately considered TensorFlow, after some research and taking part of Google's Machine Learning Crash Course, I got to that point where it specified:
Models can't learn from string values, so you'll have to perform some feature engineering to convert those values to something numeric
That would be useful in the case we have limited number of features as strings, but we don't know what the user will input as a text.
So, my questions are:
1- Can we use Machine Learning to map text input by the user with some documents on our database?
2- If we can do that, is it a good idea to favor it over other search tools like ElasticSearch?
3- Can ElasticSearch improve its results the more data we have? How?
4- How would you go about this problem?
Note: I'd be doing that in Node.js, and since TensorFlow.js is new, I am inclining to go for other solutions, but if push comes to shove, and the results are much better, I would definitely go there.
TL;DR: Yes and yes.
TS;WM:
This is a perfectly suited problem for machine learning. Especially so, if you have a database of past customer texts that have already been mapped to parts. Ideally, you have hundreds of texts mapped to each part. If that is present, you can design and train a network. And models can learn from string values with some engineering, and it's not that bad.
I'm not sure ElasticSearch would improve much on the network. I don't know much about auto parts trading, but as a wild guess, "the large round thingy that helps change direction" would never be mapped to "steering wheel" by ES but could be learned easily by a network - provided there are at least some examples of people using that text to specify steering wheel.
You can but don't have to necessarily use tensorflow.js for your network. The AI could run on your server as a webservice, and you'd just send over the customer's text to it and it would send back it's recommendations of part SKUs and names.
I have 20,000 messages (combination of email and live chat) between my customer and my support staff. I also have a knowledge base for my product.
Often times, the questions customers ask are quite simple and my support staff simply point them to the right knowledge base article.
What I would like to do, in order to save my support staff time, is to show my staff a list of articles that may likely be relevant based on the initial user's support request. This way they can just copy and paste the link to the help article instead of loading up the knowledge base and searching for the article manually.
I'm wondering what solutions I should investigate.
My current line of thinking is to run analysis on existing data and use a text classification approach:
For each message, see if there is a response with a link to a how-to article
If Yes, extract key phrases (microsoft cognitive services)
TF-IDF?
Treat each how-to as a 'classification' that belongs to sets of key phrases
Use some supervised machine learning, support vector machines maybe to predict which 'classification, aka how-to article' belongs to key phrase determined from a new support ticket.
Feed new responses back into the set to make the system smarter.
Not sure if I'm over complicating things. Any advice on how this is done would be appreciated.
PS: naive approach of just dumping 'key phrases' into search query of our knowledge base yielded poor results since the content of the help article is often different than how a person phrases their question in an email or live chat.
A simple classifier along the lines of a "spam" classifier might work, except that each FAQ would be a feature as opposed to a single feature classifier of spam, not-spam.
Most spam-classifiers start-off with a dictionary of words/phrases. You already have a start on this with your naive approach. However, unlike your approach a spam classifier does much more than a text search. Essentially, in a spam classifier, each word in the customer's email is given a weight and the sum of weights indicates if the message is spam or not-spam. Now, extend this to as many features as FAQs. That is, features like: FAQ1 or not-FAQ1, FAQ2 or not-FAQ2, etc.
Since your support people can easily identify which of the FAQs an e-mail requires then using a supervised learning algorithm would be appropriate. To reduce the impact of any miss-classification errors, then consider the application presenting a support person with the customer's email followed by the computer generated response and all the support person would have to-do is approve the response or modify it. Modifying a response should result in a new entry in the training set.
Support Vector Machines are one method to implement machine learning. However, you are probably suggesting this solution way too early in the process of first identifying the problem and then getting a simple method to work, as well as possible, before using more sophisticated methods. After all, if a multi-feature spam classifier works why invest more time and money in something else that also works?
Finally, depending on your system this is something I would like to work-on.
I'm embarking on a project for a non-profit organization to help process and classify 1000's of reports annually from their field workers / contractors the world over. I'm relatively new to NLP and as such wanted to seek the group's guidance on the approach to solve our problem.
I'll highlight the current process, and our challenges and would love your help on the best way to solve our problem.
Current process: Field officers submit reports from locally run projects in the form of best practices. These reports are then processed by a full-time team of curators who (i) ensure they adhere to a best-practice template and (ii) edit the documents to improve language/style/grammar.
Challenge: As the number of field workers increased the volume of reports being generated has grown and our editors are now becoming the bottle-neck.
Solution: We would like to automate the 1st step of our process i.e., checking the document for compliance to the organizational best practice template
Basically, we need to ensure every report has 3 components namely:
1. States its purpose: What topic / problem does this best practice address?
2. Identifies Audience: Who is this for?
3. Highlights Relevance: What can the reader do after reading it?
Here's an example of a good report submission.
"This document introduces techniques for successfully applying best practices across developing countries. This study is intended to help low-income farmers identify a set of best practices for pricing agricultural products in places where there is no price transparency. By implementing these processes, farmers will be able to get better prices for their produce and raise their household incomes."
As of now, our approach has been to use RegEx and check for keywords. i.e., to check for compliance we use the following logic:
1 To check "states purpose" = we do a regex to match 'purpose', 'intent'
2 To check "identifies audience" = we do a regex to match with 'identifies', 'is for'
3 To check "highlights relevance" = we do a regex to match with 'able to', 'allows', 'enables'
The current approach of RegEx seems very primitive and limited so I wanted to ask the community if there is a better way to solving this problem using something like NLTK, CoreNLP.
Thanks in advance.
Interesting problem, i believe its a thorough research problem! In natural language processing, there are few techniques that learn and extract template from text and then can use them as gold annotation to identify whether a document follows the template structure. Researchers used this kind of system for automatic question answering (extract templates from question and then answer them). But in your case its more difficult as you need to learn the structure from a report. In the light of Natural Language Processing, this is more hard to address your problem (no simple NLP task matches with your problem definition) and you may not need any fancy model (complex) to resolve your problem.
You can start by simple document matching and computing a similarity score. If you have large collection of positive examples (well formatted and specified reports), you can construct a dictionary based on tf-idf weights. Then you can check the presence of the dictionary tokens. You can also think of this problem as a binary classification problem. There are good machine learning classifiers such as svm, logistic regression which works good for text data. You can use python and scikit-learn to build programs quickly and they are pretty easy to use. For text pre-processing, you can use NLTK.
Since the reports will be generated by field workers and there are few questions that will be answered by the reports (you mentioned about 3 specific components), i guess simple keyword matching techniques will be a good start for your research. You can gradually move to different directions based on your observations.
This seems like a perfect scenario to apply some machine learning to your process.
First of all, the data annotation problem is covered. This is usually the most annoying problem. Thankfully, you can rely on the curators. The curators can mark the specific sentences that specify: audience, relevance, purpose.
Train some models to identify these types of clauses. If all the classifiers fire for a certain document, it means that the document is properly formatted.
If errors are encountered, make sure to retrain the models with the specific examples.
If you don't provide yourself hints about the format of the document this is an open problem.
What you can do thought, is ask people writing report to conform to some format for the document like having 3 parts each of which have a pre-defined title like so
1. Purpose
Explains the purpose of the document in several paragraph.
2. Topic / Problem
This address the foobar problem also known as lorem ipsum feeling text.
3. Take away
What can the reader do after reading it?
You parse this document from .doc format for instance and extract the three parts. Then you can go through spell checking, grammar and text complexity algorithm. And finally you can extract for instance Named Entities (cf. Named Entity Recognition) and low TF-IDF words.
I've been trying to do something very similar with clinical trials, where most of the data is again written in natural language.
If you do not care about past data, and have control over what the field officers write, maybe you can have them provide these 3 extra fields in their reports, and you would be done.
Otherwise; CoreNLP and OpenNLP, the libraries that I'm most familiar with, have some tools that can help you with part of the task. For example; if your Regex pattern matches a word that starts with the prefix "inten", the actual word could be "intention", "intended", "intent", "intentionally" etc., and you wouldn't necessarily know if the word is a verb, a noun, an adjective or an adverb. POS taggers and the parsers in these libraries would be able to tell you the type (POS) of the word and maybe you only care about the verbs that start with "inten", or more strictly, the verbs spoken by the 3rd person singular.
CoreNLP has another tool called OpenIE, which attempts to extract relations in a sentence. For example, given the following sentence
Born in a small town, she took the midnight train going anywhere
CoreNLP can extract the triple
she, took, midnight train
Combined with the POS tagger for example; you would also know that "she" is a personal pronoun and "took" is a past tense verb.
These libraries can accomplish many other tasks such as tokenization, sentence splitting, and named entity recognition and it would be up to you to combine all of these tools with your domain knowledge and creativity to come up with a solution that works for your case.
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.
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.