Don't know where to start on this one so hopefully you guys can clear up my question. I have project where email will be searched for specific words/patterns and stored in a structured manner. Something that is done with Trip it.
The article states that they developed a DataMapper
The DataMapper is responsible for taking inbound email messages
addressed to plans [at] tripit.com and transforming them from the
semi-structured format you see in your mail reader into a highly
structured XML document.
There is a comment that also states
If you're looking to build this yourself, reading a little bit about
Wrappers and Wrapper Induction might be helpful
I Googled and read about wrapper induction but it was just too broad of a definition and didn't help me understand how one would go about solving such problem.
Is there some open source project out there that does similar things?
There are a couple of different ways and things you can do to accomplish this.
The first part, which involves getting access to the email content I'll not answer here. Basically, I'll assume that you have access to the text of emails, and if you don't there are some libraries that allow you to connect java to an email box like camel (http://camel.apache.org/mail.html).
So now you've got the email so then what?
A handy thing that could help is that lingpipe (http://alias-i.com/lingpipe/) has an entity recognizer that you can populate with your own terms. Specifically, look at some of their extraction tutorials and their dictionary extractor (http://alias-i.com/lingpipe/demos/tutorial/ne/read-me.html) So inside of the lingpipe dictionary extractor (http://alias-i.com/lingpipe/docs/api/com/aliasi/dict/ExactDictionaryChunker.html) you'd simply import the terms you're interested in and use that to associate labels with an email.
You might also find the following question helpful: Dictionary-Based Named Entity Recognition with zero edit distance: LingPipe, Lucene or what?
Really a very broad question, but I can try to give you some general ideas, which might be enough to get started. Basically, it sounds like you're talking about an elaborate parsing problem - scanning through the text and looking to apply meaning to specific chunks. Depending on what exactly you're looking for, you might get some good mileage out of a few regular expressions to start - things like phone numbers, email addresses, and dates have fairly standard structures that should be matchable. Other data points might benefit from some indicator words - the phrase "departing from" might indicate that what follows is an address. The natural language processing community also has a large tool set available for text processing - check out things like parts of speech taggers and semantic analyzers if they're appropriate to what you're trying to do.
Armed with those techniques, you can follow a basic iterative development process: For each data point in your expected output structure, define some simple rules for how to capture it. Then, run the application over a batch of test data and see which samples didn't capture that datum. Look at the samples and revise your rules to catch those samples. Repeat until the extractor reaches an acceptable level of accuracy.
Depending on the specifics of your problem, there may be machine learning techniques that can automate much of that process for you.
Related
I don't know if this is the right place to ask this but, i am trying to build a bot in Python that will read incoming messages on a Slack channel where customer post their issues such as 'unable to connect to VPN', 'can someone reply to my ticket' etc…
The bot will analyze the message, determine if the customer is angry or not, and then propose a solution until an agent is free to actually check the issue.
Now, I was experimenting with TextBlob for the sentiment analysis part, but I don't know which technologies to actually use to determine the issue based on specific keywords and provide a solution to the user. Can someone propose me some python libraries/technologies that I could use to achieve this ?
To be honest your question is to generic to answer in one go.
Nontheless, you first have to clearly define the scope of your project. In doing so, you might want to first do a quick literaty review (Google Scholar) to familiarize with the state of the art technologies and methods.
From my little experience, a common (maybe simple) technique (lexicon-based approach) used to determine the sentiment of a word, is to use a pre-compiled dictionary (you can create your own though) that contains words - sentiment mappings. For example:
word:tired, sentiment:negative, score:5
So each time the bot finds the keyword "tired" in a sentence it will assign its corresponding negative value (polarity) to the sentence.
You might want to consider applying POS tags in the input text, as sometimes nouns or ``verbs carry significant meaning, compared to adjectives for example.
Keep in mind though, that negative comments can be written in the form of sarcasm. Sarcasm detectioin is a more difficult task though.
Alternatively, you could try using a pre-trained model such as bert-base-multilingual-uncased-sentiment that can be found here in Hugging Face.
For more information on the matter you have a look at this post.
Again as I mentioned, you have to clearly define your goals. This will enable you to specify the libraries or methodology available to solve your problem. Hope my answer helps.
I'm trying to ascertain the "right tool for the job" here, and I believe Cognitive Services can do this but without disappearing down an R&D rabbit-hole I thought I'd make sure I was tunnelling in the right direction first.
So, here is the brief:
I have a collection of known existing phrases which I want to look for, but these might be written in slightly different ways, be that grammar or language.
I want to be able to parse a (potentially large) volume of text to scan and look for those phrases so that I can identify them.
For example, my phrase could be "the event will be in person" but that also needs to identify different uses of language; for example "in-person event", "face to face event", or "on-site event" - as well as the various synonyms and variations you can get with such things.
LUIS initially appeared to be the go-to tool for this kind of thing, and includes the ability to write your own Features (aka Phrase Lists) to augment the model, but it isn't clear whether that would hit the brief - LUIS appears to be much more about "intent" and user interaction (for example building a chat Bot, or understanding intent from emails).
Text Analytics also seems a likely candidate, but again seems more focused about identifying "entities" (such as people / places / organisations) rather than a natural language "phrase" - would this tool work if I was defining my own "Topics" or is that really just barking up the wrong tree?
.. or ... is there actually something else I should be looking at completely different?
At this point - I'm really looking for a "which tool should I spend lots of time learning about".
Thanks all in advance - I appreciate this is a fairly open-ended requirement.
It seems your scenario aligns more with our text analytics service. I was going to recommend Key Phrase Extraction API which evaluates unstructured text and returns a list of key phrases. However, since you require to use known (custom) phrase list, it may not be the solution you're looking for. We currently don't support custom key phrase extraction today, however it's on our roadmap. If interested, we can connect you with the product team to learn more about your scenario.
Updated:
Please try custom NER capability.
My Question
What are the best practices for creating a customized report based on a user form input? Specifically, how do I create an easy to maintain system which takes user input which is collected in a form and generate multiple paragraphs that explains the results of analysis.
Background
I am working on a very large multiyear project with a startup (who is my client). My job is to program analysis and generate reports to users. The pipeline for data looks like this:
Users enter information into a form -> results are calculated based on user input -> reports are displayed to users that share analysis.
It is really important to my client that some of the analysis results are displayed in paragraphs in a non-formal user friendly tone. The challenge is that the form and analysis are quite complex and will only get more complex over time. An example of the type of template for the paragraphs looks something like this:
resultsParagraphText=`Hi ${userName}. We found that the best ice cream flavour for you is ${bestIceCreamFlavor}. These other flavors ${otherFlavors} might be good for you. Here are the reasons why you might enjoy these flavors: ${reasonsWhyGoodFlavors}.
However we would not recommend these other flavors ${badFlavors}. Here are the reasons you should avoid this bad flavors: ${reasonsWhyBadFlavors}.`
These results paragraphs, of which there of many, have several minor problems which combined are significant:
If there is a bug in the code, minor visual errors would be visible to end users (capitalization errors, missing/extra commas, and so on).
A lot of string comparisons (e.g. if answers.previousFlavors.includes("Vanilla")) are required to generate the results paragraphs. Minor errors in the forms (e.g. vanilla in the form is not capitalized so answers.previousFlavors.includes("Vanilla") returns false even when user enters vanilla.) can cause errors in the results paragraph.
Changes in different parts of the project (form, analysis) directly effect how the results paragraph is made. Bad types, differences in string values, null or undefined values not being caught directly have an impact on how the results paragraph is made.
There are many edge cases (e.g. What if the user has no other suitable good flavors for them? The the sentence These other flavors ${otherFlavors} might be good for you. needs to be excluded).
It is hard to write paragraphs that use templates and have a non-formal tone.
and so on.
I have charts and other types of ways to display results and have explained to the client the challenges of sharing the information in paragraph form.
What I am looking for
I need examples, how tos, best practices on how to build a maintainable system for generating customized paragraphs based on user input. I know how to solve each of the individual issues (as they are fairly simple) but in a large project this will become very hard to maintain.
Notes
I have no clue what tags to use for the post. Feel free to edit/add tags if you know more appropriate ones.
The project is planning to use machine learning in the future other parts of the project. If there is a ML/AI solution that is useful please tell me.
I am working primarily in JavaScript, Python, C, and R, but if there is a library or tool in any other language please tell me. Finding a solution is very important to me and I would be willing to learn a lot find a best solution.
To avoid this question being removed because I have rephrased it to avoid asking for personal opinion, instead asking for existing examples or how tos. I can also imagine that others might find a solution fairly useful. If you can edit it to make the question less subjective please do so.
If you have any questions or need clarification feel free to ask. Any help is appreciated.
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.
We (my company) runs a website which have lots of data recorded like user registration, visits, clicks, what the stuff they post etc etc but so far we don't have a tool to find out how to monitor entire thing or how to find patterns in it so that we can understand what kind of information we can get from it? So that Mgmt can take decisions based on it. In short, the people do at Amazon or Google based on data they retrieve, we want a similar thing.
Now, after the intro, I would like to know what technology could it be called;is it Data Mining,Machine Learning or what? Where should we start to convert meaningless data into useful Information?
I think what you need enters in the "realm" of: parsing data, creating graphs, showing statistics about some elements, etc.
There is no "easy" answer, I can only answer parts of your question.
There are no premade magical analytical tools, big companies have their own backend tools tunned to parse the large amounts of data and spit out data summaries that are then used to build graphs or for statistical analysis.
I think the domain you are searching for is statistical data analysis. But there are many parts that go together here.
Best advice I can give you is to set up specific goals for you analysis and then try to see what is the best solution, you question is too open.
ie. if you are interested in visits/clicks/website related statistics Google Analytics is a great tool, and very easy to use.