Hi I would like to know if we can have something like the following example on Doccano:
So let's say that we have a sentence like this : "MS is an IT company". I want to label some words in this sentence, for example MS (Microsoft). MS should be labelled as a Company (so imagine that I have an entity named Company) but I also want to say that MS stands for Microsoft.
Is there a way to do that with Doccano?
Thanks
Doccano supports
Sequence Labelling good for Named Entity Recognition (NER)
Text Classification good e.g. for Sentiment Analysis
Sequence To Sequence good for Machine Translation
What you're describing sounds a little like Entity Linking.
You can see from Doccano's roadmap in its docs that Entity Linking is part of the plans, but not yet available.
For now, I suggest to frame this as a NER problem, and to have different entities for MS (Microsoft) and MS (other). If you have too many entities to choose from, the labelling could become complicated, but then you could break up the dataset in smaller entity-focussed datasets. For example, you could get only documents with MS in them and label the mentions as one of the few synonyms.
Related
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 need to categorize a text or word to a particular category. For example, the text 'Pink Floyd' should be categorized as 'music' or 'Wikimedia' as 'technology' or 'Einstein' as 'science'.
How can this be done? Is there a way I can use the DBpedia for the same? If not, the database has to be trained from time to time, right?
This is a text classification problem. Manning, Raghavan and Schütze's Information Retrieval book chapter is a nice introduction. I think you do not need DBPedia nor NER for this, just a small labeled training data set with enough labeled examples for all of your classes.
Yes, DBpedia may be a good choice for this kind of problem. You'll have to
squash the DBpedia category structure so you get the right granularity (e.g., Pink Floyd is listed under Capitol Records artists and a host of other categories, but not directly under Music). Maybe pick a few large categories and try to find whether your concepts are listed indirectly in them;
normalize text; Einstein is listed as Albert Einstein, not einstein
deal with ambiguity due to terms describing multiple concepts and concepts belonging to multiple top-level categories.
These problems may be solvable using machine learning, but I only see how it can be done if you extract these terms, along with relevant features, from running text. But in that case, you might just as well classify the entire text into one of the categories you choose in step 1.
This is the well-studied named entity recognition problem. Unless you have a particular need to roll your own technology (hint: it's a hard problem in general), using Gate, or perhaps one of the online services that builds on it (e.g. TSO's Data Enrichment Service), would be a good option. An alternative online service is OpenCalais.
Mapping your categries to DBPedia.
Index with lucene selected DBPedia categories and label data with your category names.
Do search for your data - tokenization, normalization will be done by Lucene.
This approach is somehow related to KNN classification.
Yes DBpedia is a good choice for text classification, as you can use its predicates/ relations to query and to extract the meaningful information for the particular category.
You can look into the endpoint for querying Dbpedia:
http://dbpedia.org/sparql
Further, learn the basic syntax of SPARQL to query on the endpoint from the following link:
http://www.w3.org/TR/rdf-sparql-query/
I am developing a program but stuck on a particular hurdle. I need to find words associated with other words. EG "green" might be associated with "environment", "leaf", "earth", "wind", "electric", "hybrid", etc. All I can find is Google Sets. Is there any other resource that is better?
If you have a large text collection (say Wikipedia, Project Gutenberg) you can use co-occurrence scores extract this kind of data. See e.g. Padó and Lapata and the references therein.
I recently built a tool that mines this kind of associations from Wikipedia database dumps by another method. It requires a lot of memory though; other folks have tried to do the same using randomized methods.
If you're still looking for a resource of semantically related words, I've just recently developed an API that takes a query and returns semantically related words. It offers parts of speech, relationships to the query word, and a word similarity measurement.
https://kiingo.co/rapid-associations-api
Disclaimer: I'm the developer of this API.
I am new to Natural Language Processing and I want to learn more by creating a simple project. NLTK was suggested to be popular in NLP so I will use it in my project.
Here is what I would like to do:
I want to scan our company's intranet pages; approximately 3K pages
I would like to parse and categorize the content of these pages based on certain criteria such as: HR, Engineering, Corporate Pages, etc...
From what I have read so far, I can do this with Named Entity Recognition. I can describe entities for each category of pages, train the NLTK solution and run each page through to determine the category.
Is this the right approach? I appreciate any direction and ideas...
Thanks
It looks like you want to do text/document classification, which is not quite the same as Named Entity Recognition, where the goal is to recognize any named entities (proper names, places, institutions etc) in text. However, proper names might be very good features when doing text classification in a limited domain, it is for example likely that a page with the name of the head engineer could be classified as Engineering.
The NLTK book has a chapter on basic text classification.
We have a client who is looking for a means to import and categorize a large amount of textual data. This data has to be categorized and it's been suggested that the easiest way to to do this would be to look at the description field and try to match the words held there to see if a category can be derived for that particular record.
It was thought the best way to do this would be matching the words to key words held against each category and if that was unsuccessful then to use some kind of synonym look up to see if this could be used instead. So for example, if a particular record had the word "automobile" in it then a synonym look up could match that word to the word "car" which would be held against the category "vehicle".
Does anyone know of a web service or other means of looking up a dictionary to find synonyms for a particular word? The project manager has suggested buying a Google Enterprise Search license for this but from what I can make out that doesn't offer what these guys are looking for.
Any suggestions of other getting the client what they are looking for would be gratefully accepted.
Thanks! I'll look into Wordnet.
Do you know of any other types of textual classification software products out there. I see there's some discussion of using Bayasian algorithms for this but I can't see any real world examples of it.
The first thing that comes to mind is Wordnet. Wordnet is a human-generated database of words and related words, including synonyms. The Wikipedia Wordnet entry lists several interfaces to Wordnet. I believe some of them are web services.
You can also roll your own. Manning and Schutze's chapter 5 (free PDF) shows ways to do this.
Having said that, are you solving the right problem? How do you build the category list?
Is it a hierarchy? a tag cloud? See Clay Shirky's Ontology is Overrated for a critique of hierarchical categories. I believe that synonyms are less important if you base your classification on sets of words (Naive Bayes, for example) rather than on single words.
You should look at using WordNet. You can visit their website http://wordnet.princeton.edu/ to get more information, but there are libraries available for integrating against them in lots of languages.
Go to their online tool to see the use of it in action here: http://wordnetweb.princeton.edu/perl/webwn. If you look up a word, then click on "S" next to each definition, you'll get a list of semantically related words to that definition.
I also think you should check out software that will allow you to perform "document clustering." Here is an example: http://glaros.dtc.umn.edu/gkhome/cluto/cluto/overview. That should help you bootstrap the category creation process.
I think this will help get you a long way toward what you want!
For text classification you can take a look at Apache Mahout.