I'm new to NLP. I am looking for recommendations for an Annotation tool to create a labeled NER dataset from raw texts.
In details:
I'm trying to create a labeled data set for specific types of Entities in order to develop my own NER project (rule based at first).
I assumed there will be some friendly frameworks that allows create tagging projects, tag text data, create a labeled dataset, and even share projects so several people could work on the same project, but I'm struggling to find one (I admit "friendly" or "intuitive" are subjective, yet this is my experience).
So far I've tried several Frameworks:
I tried LightTag. It makes the tagging itself fast and easy (i.e. marking the words and giving them labels) but the entire process of creating a useful dataset is not as intuitive as I expected (i.e. uploading the text files, split to different tagging objects, save the tags, etc.)
I've installed and tried LabelStudio and found it less mature then LightTag (don't mean to judge here :))
I've also read about spaCy's Prodigy, which offers a paid annotation tool. I would consider purchasing it, but their website only offers a live demo of the the tagging phase and I can't access if their product is superior to the other two products above.
Even in StackOverflow the latest question I found on that matter is over 5 years ago.
Do you have any recommendation for a tool to create a labeled NER dataset from raw text?
⚠️ Disclaimer
I am the author of Acharya. I would limit my answers to the points raised in the question.
Based on your question, Acharya would help you in creating the project and upload your raw text data and annotate them to create a labeled dataset.
It would allow you to mark records individually for train or test in the dataset and would give data-centric reports to identify and fix annotation/labeling errors.
It allows you to add different algorithms (bring your own algorithm) to the project and train the model regularly. Once trained, it can give annotation suggestions from the trained models on untagged data to make the labeling process faster.
If you want to train in a different setup, it allows you to export the labeled dataset in multiple supported formats.
Currently, it does not support sharing of projects.
Acharya community edition is in alpha release.
github page (https://github.com/astutic/Acharya)
website (https://acharya.astutic.com/)
Doccano is another open-source annotation tool that you can check out https://github.com/doccano/doccano
I have used both DOCCANO (https://github.com/doccano/doccano) and BRAT (https://brat.nlplab.org/).
Find the latter very good and it supports more functions. Both are free to use.
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'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.
In NLP there is a concept of Gazetteer which can be quite useful for creating annotations. As far as i understand,
A gazetteer consists of a set of lists containing names of entities such as cities, organisations, days of the week, etc. These lists are used to find occurrences of these names in text, e.g. for the task of named entity recognition.
So it is essentially a lookup. Isn't this kind of a cheat? If we use a Gazetteer for detecting named entities, then there is not much Natural Language Processing going on. Ideally, i would want to detect named entities using NLP techniques. Otherwise how is it any better than a regex pattern matcher.
Does that make sense?
Depends on how you built/use your gazetteer. If you are presenting experiments in a closed domain and you custom picked your gazetteer, then yes, you are cheating.
If you are using some openly available gazetteer and performing experiments on a large dataset or using it in an application in the wild where you don't control the input then you are fine.
We found ourselves in a similar situation. We partition our dataset and use the training data to automatically build our gazetteers. As long as you report your methodology you should not feel like cheating (let the reviewers complain).
I want to recognize named entities in a specific field (e.g. baseball). I know there are tools available like StanfordNER, LingPipe, AlchemyAPI and I have done a little testing with them. But what I want them to be is field specific as I mentioned earlier. How this is possible?
One approach may be to
Use a general (non-domain specific) tool to detect people's names
Use a subject classifier to filter out texts that are not in the domain
If the total size of the data set is sufficient and the accuracy of the extractor and classifier good enough, you can use the result to obtain a list of people's names that are closely related to the domain in question (e.g. by restricting the results to those that are mentioned significantly more often in domain-specific texts than in other texts).
In the case of baseball, this should be a fairly good way of getting a list of people related to baseball. It would, however, not be a good way to obtain a list of baseball players only. For the latter it would be necessary to analyse the precise context in which the names are mentioned and the things said about them; but perhaps that is not required.
Edit: By subject classifier I mean the same as what other people might refer to simply as categorization, document classification, domain classification, or similar. Examples of ready-to-use tools include the classifier in Python-NLTK (see here for examples) and the one in LingPipe (see here).
Have a look at smile-ner.appspot.com which covers 250+ categories. In particaul, it covers a lot of persons/teams/clubs on sports. May be useful for your purpose.
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/