I am experimenting with elasticsearch as a search server and my task is to build a "semantic" search functionality. From a short text phrase like "I have a burst pipe" the system should infer that the user is searching for a plumber and return all plumbers indexed in elasticsearch.
Can that be done directly in a search server like elasticsearch or do I have to use a natural language processing (NLP) tool like e.g. Maui Indexer. What is the exact terminology for my task at hand, text classification? Though the given text is very short as it is a search phrase.
There may be several approaches with different implementation complexity.
The easiest one is to create list of topics (like plumbing), attach bag of words (like "pipe"), identify search request by majority of keywords and search only in specified topic (you can add field topic to your elastic search documents and set it as mandatory with + during search).
Of course, if you have lots of documents, manual creation of topic list and bag of words is very time expensive. You can use machine learning to automate some of tasks. Basically, it is enough to have distance measure between words and/or documents to automatically discover topics (e.g. by data clustering) and classify query to one of these topics. Mix of these techniques may also be a good choice (for example, you can manually create topics and assign initial documents to them, but use classification for query assignment). Take a look at Wikipedia's article on latent semantic analysis to better understand the idea. Also pay attention to the 2 linked articles on data clustering and document classification. And yes, Maui Indexer may become good helper tool this way.
Finally, you can try to build an engine that "understands" meaning of the phrase (not just uses terms frequency) and searches appropriate topics. Most probably, this will involve natural language processing and ontology-based knowledgebases. But in fact, this field is still in active research and without previous experience it will be very hard for you to implement something like this.
You may want to explore https://blog.conceptnet.io/2016/11/03/conceptnet-5-5-and-conceptnet-io/.
It combines semantic networks and distributional semantics.
When most developers need word embeddings, the first and possibly only place they look is word2vec, a neural net algorithm from Google that computes word embeddings from distributional semantics. That is, it learns to predict words in a sentence from the other words around them, and the embeddings are the representation of words that make the best predictions. But even after terabytes of text, there are aspects of word meanings that you just won’t learn from distributional semantics alone.
Some results
The ConceptNet Numberbatch word embeddings, built into ConceptNet 5.5, solve these SAT analogies better than any previous system. It gets 56.4% of the questions correct. The best comparable previous system, Turney’s SuperSim (2013), got 54.8%. And we’re getting ever closer to “human-level” performance on SAT analogies — while particularly smart humans can of course get a lot more questions right, the average college applicant gets 57.0%.
Semantic search is basically search with meaning. Elasticsearch uses JSON serialization by default, to apply search with meaning to JSON you would need to extend it to support edge relations via JSON-LD. You can then apply your semantic analysis over the JSON-LD schema to word disambiguate plumber entity and burst pipe contexts as a subject, predicate, object relationships. Elasticsearch has a very weak semantic search support but you can go around it using faceted searching and bag of words. You can index a thesaurus schema for plumbing terms, then do a semantic matching over the text phrases in your sentences.
"Elasticsearch 7.3 introduced introduced text similarity search with vector fields".
They describe the application of using text embeddings (e.g., word embeddings and sentence embeddings) to implement this sort of semantic similarity measure.
A bit late to the party, but part II of this blog seems to address this through "contextual searches". It basically makes a two-part query to Elasticsearch in order to build a list of "seed" documents and then an expanded query via the more-like-this API. The result is a set of documents most contextually similar to the search query.
it's possible. This GitHub repo shows how to integrate Elasticsearch with the current state-of-the-art on NLP for semantic representation of language: BERT (Bidirectional Encoder Representations from Transformers) https://github.com/Hironsan/bertsearch
Good luck.
My suggestion is to use BERT embedding for your sentences and add an embedding field to your ElasticSearch, as it is described in https://www.elastic.co/blog/text-similarity-search-with-vectors-in-elasticsearch
For BERT embedding I suggest to use sentence-transformers from Huggingface library. You can find sample codes in https://towardsdatascience.com/how-to-build-a-semantic-search-engine-with-transformers-and-faiss-dcbea307a0e8
There are several options for that:
You can perform it in elasticsearch itself. Elasticsearch supports the indexing of Dense Embedding of docs. From there, you can write your own pipeline for search and use your preferred relevancy score formula ie. cosine similarity or something else.
Use Haystack pipeline, refer to my blog which describes setting up a semantic search pipeline (end-to-end).
You can use Meta's Faiss
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.
We have a system that allows users to answer a question as free text and we want to check whether their answer contains any of our predefined topics. These topics will be defined prior to answers being submitted.
We tried to use a method similar to spam detection, but this is only good for determining whether something is true/false, incorrect/correct. We need the response to say which of the predefined topics a piece of text contains. Is there an algorithm that would solve this problem?
Maybe you will try to use "bag of words" for feature extraction and "naive Bayes classifier with multinomial model" for classification.
In this page this described more detail link.
You could also try explicit semantic analysis (ESA)[1][2]. Given a set of documents that represent concepts (in your case your topics) you can train a model and given any new sentence as input you can get a ranked list of the closest concepts "evoked" by that sentence. Of course this assume you have a document with some text describing every concept you want to identify (that's why the most common thing to do is to use Wikipedia pages as concepts), but if this is the case you could give it a try.
[1] https://en.wikipedia.org/wiki/Explicit_semantic_analysis
[2] http://www.cs.technion.ac.il/~gabr/papers/ijcai-2007-sim.pdf
I am working on a natural language processing application. I have a text describing 30 domains. Each domain is defined with a short paragraph that explains it. My aim is to build a thesaurus from this text so I can determine from an input string which domains are concerned. The text is about 5000 words and each domains is described by 150 words. My questions are :
Do I have a long enough text to create a thesaurus from ?
Is my idea of building a thesaurus legit or should I just use NLP libraries to analyse my corpus and the input string ?
At the moment, I have calculated the number total of occurrence of each words grouped by domains because I first thought of a indexed approach. But I am really not sure which method is the best. Does someone have experience in both NLP and thesaurus building ?
I think what you are looking for is topic modeling. Given a word, you want to get the probability of which domain the word belongs to. I would recommend using off the shelf algorithms that implement LDA (Latent Dirichlet Algorithm).
Alternatively, you can visit David Blei's website. He has written some great software that implements LDA, and topic modeling in general. He also has presented several tutorials for topic modeling for beginners.
If your goal is to build a thesaurus then build a thesaurus; if your goal is not to build a thesaurus, then you better use stuff available out there.
More generally, for any task in NLP - from data acquisition to machine translation - you're gonna face numerous problems (both technical and theoretical), and it is very easy to stray from the path, as these problems are - most of the time - fascinating.
Whatever the task is, build a system using existing resources. Then you get the big picture; then you can start thinking about improving component A or B.
Good luck.
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/
In an app that i'm creating, I want to add functionality that groups news stories together. I want to group news stories about the same topic from different sources into the same group. For example, an article on XYZ from CNN and MSNBC would be in the same group. I am guessing its some sort of fuzzy logic comparison. How would I go about doing this from a technical standpoint? What are my options? We haven't even started the app yet, so we aren't limited in the technologies we can use.
Thanks, in advance for the help!
This problem breaks down into a few subproblems from a machine learning standpoint.
First, you are going to want to figure out what properties of the news stories you want to group based on. A common technique is to use 'word bags': just a list of the words that appear in the body of the story or in the title. You can do some additional processing such as removing common English "stop words" that provide no meaning, such as "the", "because". You can even do porter stemming to remove redundancies with plural words and word endings such as "-ion". This list of words is the feature vector of each document and will be used to measure similarity. You may have to do some preprocessing to remove html markup.
Second, you have to define a similarity metric: similar stories score high in similarity. Going along with the bag of words approach, two stories are similar if they have similar words in them (I'm being vague here, because there are tons of things you can try, and you'll have to see which works best).
Finally, you can use a classic clustering algorithm, such as k-means clustering, which groups the stories together, based on the similarity metric.
In summary: convert news story into a feature vector -> define a similarity metric based on this feature vector -> unsupervised clustering.
Check out Google scholar, there probably have been some papers on this specific topic in the recent literature. A lot of these things that I just discussed are implemented in natural language processing and machine learning modules for most major languages.
The problem can be broken down to:
How to represent articles (features, usually a bag of words with TF-IDF)
How to calculate similarity between two articles (cosine similarity is the most popular)
How to cluster articles together based on the above
There are two broad groups of clustering algorithms: batch and incremental. Batch is great if you've got all your articles ahead of time. Since you're clustering news, you've probably got your articles coming in incrementally, so you can't cluster them all at once. You'll need an incremental (aka sequential) algorithm, and these tend to be complicated.
You can also try http://www.similetrix.com, a quick Google search popped them up and they claim to offer this service via API.
One approach would be to add tags to the articles when they are listed. One tag would be XYZ. Other tags might describe the article subject.
You can do that in a database. You can have an unlimited number of tags for each article. Then, the "groups" could be identified by one or more tags.
This approach is heavily dependent upon human beings assigning appropriate tags, so that the right articles are returned from the search, but not too many articles. It isn't easy to do really well.