However, since last year, the field of Natural Language Processing NLP has experienced a fast evolution thanks to the development in Deep Learning research and the advent of Transfer Learning techniques. With such progress, several improved systems and applications to NLP tasks are expected to come out. One of such systems is the cdQA-suitea package developed by some colleagues and me in a partnership between Telecom ParisTech, a French engineering school, and BNP Paribas Personal Finance, a European leader in financing for individuals.
Open-domain systems deal with questions about nearly anything, and can only rely on general ontologies and world knowledge. As these documents are related to several different topics and subjects we can understand why this system is considered an ODQA. On the other hand, closed-domain systems deal with questions under a specific domain for example, medicine or automotive maintenanceand can exploit domain-specific knowledge by using a model that is fitted to a unique-domain database.
The cdQA-suite was built to enable anyone who wants to build a closed-domain QA system easily. The cdQA-suite is comprised of three blocks:. I will explain how each module works and how you can use it to build your QA system on your own data. The cdQA architecture is based on two main components: the Retriever and the Reader. You can see below a schema of the system mechanism. When a question is sent to the system, the Retriever selects a list of documents in the database that are the most likely to contain the answer.
It is based on the same retriever of DrQAwhich creates TF-IDF features based on uni-grams and bi-grams and compute the cosine similarity between the question sentence and each document of the database.
After selecting the most probable documents, the system divides each document into paragraphs and send them with the question to the Reader, which is basically a pre-trained Deep Learning model.
Then, the Reader outputs the most probable answer it can find in each paragraph. After the Reader, there is a final layer in the system that compares the answers by using an internal score function and outputs the most likely one according to the scores. Before starting using the package, let's install it. You can install it using pip or clone the repository from source. Now, you can open a jupyter notebook and follow the steps below to see how cdQA works:. You should have something like the following as output:.
If you use your own dataset, please be sure that your dataframe has such structure. When using the CPU version of the model, each prediction takes between 10 and 20 seconds to be done. If you have an annotated dataset that can be generated by the help of the cdQA-annotator in the same format as SQuAD dataset you can fine-tune the reader on it:. In order to facilitate the data annotation, the team has built a web-based application, the cdQA-annotator.
Entity extraction: How does it work?
Now you can install the annotator and run it:. To start annotating question-answer pairs you just need to write a question, highlight the answer with the mouse cursor the answer will be written automaticallyand then click on Add annotation :. After the annotation, you can download it and use it to fine-tune the BERT Reader on your own data as explained in the previous section. The team also has provided a web-based user interface to couple with cdQA.
Second, you should proceed to the installation of the cdQA-ui package:. Then, you start the develpoment server:. You will see something like the figure below:. As the application is well connected to the back-end, via the REST API, you can ask a question and the application will display an answer, the passage context where the answer was found and the title of the article:.
If you want to couple the interface on your website you just need do the following imports in your Vue app:. Then you insert the cdQA interface component:. Do not hesitate to star and to follow the repositories if you liked the project and consider it valuable for you and your applications.
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How to create your own Question-Answering system easily with python
We have been using the software to run our psychology practice for over ten years. The system enables you to record your clients, appointments, services, organisations and referring doctors, and has the aim of making operations like generating standard letters, claiming from the government and insurance companies, and reporting income, much easier. You can download all files from the Files Tab Look This, if used correctly, can improve your productivity greatly. Store all of your pharmacy and hospital dispensary data for easy retrieval and tracking including your Prescriptions, Drugs, Employees, Clients and Users.
Manage all from one simple yet powerful admin system. Get it today! This is medical store software with POS. In software all requirement will be fullfill of pharmacy store. This come up with accounting ,POS,inventory,Suggestiong list stock list ,bills,purchase,profit loss.
Software esp. Care software Medical store software Miracle software Pharmaceutical store software Medicine software Medical software Retail store software Calibre has the ability to view, convert, edit, and catalog e-books of almost any e-book format. This is a fleet management program developed according to market requirements. Warns upon the expiration of revisions, contracts, and insurance.
It provides a clear, global and real-time view of the processes required to carry out the transport activity. This program generates email alerts when it expires: vignette, vehicle inspection, vehicle insurance, casco insurance, medical kit, extinguisher, car warranty, leasing contract. The data is recorded in the cloud on a PostgreSql server It uses the Safe Harbor deidentification method. Then sqlite database file is generated with a unique key to store the encrypted files and folders in binary blobs for later decryption.
Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am looking to design a system that will essentially need to make decisions based on input. The input will be a person. Women from the UK between should go to class B. Men over 75 should go to class A. Women over 6ft should go to class C. We will have approximately different rules and the first rule that is met should be applied - we need to maintain the order of the rules.
Obviously, you could just have a veeeery long if, elif, elif statement but this isn't efficient. Another option would be storing the rules in a database and maybe having an in memory table. I would like to be able to edit the rules without doing a release - possibly having a front end to allow non tech people to add, remove and reorder rules.
Everything is on the table here - the only certain requirement is the actually programming language must be Python.
I suppose my question is how to store the rules. At the moment it is one huge long if elif elif statement so anytime there is a change to the business logic the PM does up the new rules and I then convert them to the if statement. All inputs to the system will be sent through the same list of rules and the first rule that matches will be applied. Multiple rules can apply to each input but it's always the first that is applied. Input will always contain the same format input - haven't decided where it will be an object or a dict but some of the values may be None.
Some Persons may not have a weight associated with them. Rather than re-inventing the wheel, I'd suggest you to use some readily available solution. There are several expert systems out there and I'll focus on those which are either in Python or can be used via Python. It's considered state of the art and used in university courses when teaching basics of AI.
It's a great starting point due to its excellent documentation. Its syntax is definitely not Python, it rather reminds of Lisp. The advantage of CLIPS is that is a solid C engine which can be fully integrated with any other Python system via its bindings: the older pyclips and the newer clipspy. Rules can be loaded at runtime without the need of restarting the engine which should better suit your need. The Python Knowledge Engine it's a fairly powerful logic programming framework.
Rules can be activated and deactivated on demand. This should allow you to support releaseless updates. Durable Rules is a fairly new project with the ambition of supporting multiple programming languages Python, Node. Durable Rules allow you to write the whole knowledge base facts and rules in Python. The syntax might look a bit weird though, a note in this regards at the end of the post. Apart from the multiple syntax support, what interest me of this project is the fact the core is a C based implementation of RETE built on top of Redis DB.Unlike Prolog, Pyke integrates with Python allowing you to invoke Pyke from Python and intermingle Python statements and expressions within your expert system rules.
In this way, Pyke provides a way to radically customize and adapt your Python code for a specific purpose or use case. Doing this essentially makes Pyke a very high-level compiler. And taking this approach also produces dramatic increases in performance.
Pyke does not replace Python, nor is meant to compete with Python. Python is an excellent general purpose programming language, that allows you to "program in the small". Pyke builds upon Python by also giving you tools to directly program in the large. Oh, and Pyke uses Logic Programming to do all of this. Please join Pyke on Google Groups for questions and discussion! There is also an FAQ list on the sourceforge wikito make it easy to contribute.
A tutorial on logic programming in Pyke, including statementspattern matching and rules. Knowledge is made up of both facts and rules. These are gathered into named repositories called knowledge bases.
Pyke Project Page. Welcome to Pyke Release 1. Pyke was developed to significantly raise the bar on code reuse. Here's how it works: You write a set of Python functions, and a set of Pyke rules to direct the configuration and combination of these functions.
These functions refer to Pyke pattern variables within the function body. Pyke may instantiate each of your functions multiple times, providing a different set of constant values for each of the pattern variables used within the function body.
Each of these instances appears as a different function. Pyke then automatically assembles these customized functions into a complete program function call graph to meet a specific need or use case.
Pyke calls this function call graph a plan.Skip to content. Instantly share code, notes, and snippets. Code Revisions 1. Embed What would you like to do? Embed Embed this gist in your website. Share Copy sharable link for this gist.
Learn more about clone URLs. Download ZIP. Voice Based Expert System. It involves voice input, voice output and database from which every next converstaional result is queried.
It has three components ears. It uses a linux'espeak' command ears. And uses this inbuilt json database to answer future queries. One can can also train a bot by hardcoding by using. The present databse is exported as export. Further implementation: Make bot understand context between immediate conversations.
Give the bot, a sense of humour. And also embbed a expert system logic into it if necessary and maintain the normal converation database and expert system databse seperately.
Recognizer with sr. Microphone as source : print "Say something!
Let's do more of those! The cake is delicious. What about you? Where did you hear that? But tell me, do you like music? But I'll tell you a secret. All the best people are.
You too. Kennedy assassinated?Entity extraction, also known as entity name extraction or named entity recognition, is an information extraction technique that refers to the process of i dentifying and classifying key elements from text into pre-defined categories.
In this way, it helps transform unstructured data to data that is structuredand therefore machine readable and available for standard processing that can be applied for retrieving information, extracting facts and question answering. In certain formats such as document files, spreadsheets, web pages and social media, text appears as unstructured data.
Being able to identify entities —people, places, organizations and concepts, numerical expressions dates, times, currency amounts, phone numbers, etc. Entity extraction can provide a useful view of unknown data sets by immediately revealing at a minimum, who, and what, the information contains. As a result, an analyst would be able to see a structured representation of all of the the names of people, companies, brands, cities or countrieseven phone numbers in a corpus that could serve as a point of departure for further analysis and investigation.
Entity extraction technologies must address a number of language issues to be able to correctly identify and classify entities. Try our solution for entity extraction with our online demo at www. Extraction rules are what fuel the extraction of entities in text and may be based on pattern matching, linguistics, syntax, semantics or a combination of approaches.
These include:. Expert System Team. Entity Extraction at work Entity extraction technologies must address a number of language issues to be able to correctly identify and classify entities.
Share On. Post Categories.Post a Comment. Thursday, May 25, Open Source: C implementation of a simple expert system shell. Net Core 1. Run the following command to install:. Install-Package cs-expert-system-shell. The sample code below shows how to create a rule engine and initialize it with a set of rules:. AddAntecedent new IsClause " vehicleType "" cycle " ; rule.
AddAntecedent new IsClause " motor "" no " ; rule. AddAntecedent new IsClause " motor "" yes " ; rule. AddAntecedent new IsClause " vehicleType "" automobile " ; rule.
AddAntecedent new IsClause " size "" medium " ; rule. AddAntecedent new IsClause " size "" large " ; rule. The sample code below shows how to use forward chaining in the rule engine to derive more facts from the known facts using rules:. AddFact new IsClause " motor "" yes " ; rie. AddFact new IsClause " size "" medium " ; console.
WriteLine "before inference" ; console. Facts ; console. WriteLine " " ; rie. WriteLine " after inference" ; console. WriteLine " ". The sample code below shows how to use the backward chaining to reach conclusion for a target variable given a set of known facts:.