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An intent classifier which can classifies a query into one of the 21 given intents.

This project demonstrates building a synthetic mental health support chatbot. It generates a labeled dataset, trains an intent classifier, and implements a safety-first conversational engine with empathetic responses, crisis detection, and resource routing, strictly for educational purposes, not as a substitute for professional mental health care.

A project that enables identification and classification of an intent of a message with dynamic labels

A Tiny Intent Classifier model for short customer-support style text. Given an input text (e.g., "Hi, I need help with my bill"), the model returns one of: `greeting` - `question` - `complaint` - `praise` - `other`.

Minimal zero-shot intent classifier for arbitrary intent slot filling, via LLM prompting w LangChain.

Dual Intent Entity Classifier Pytorch version

A flexible and pragmatic chatbot intent classifier for chatbots

A Natural Language Intent classifier in Python using NLTK and Scikit-Learn

A WhatsApp chatbot for real estate inquiries, built using Twilio API, Flask, and a BERT intent classifier. It uses LangChain and RAG for managing context and delivering accurate property information, automating client interactions while easing the workload for agents.

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MSc project: design, develop and evaluate a conversational story-teller dialogue system. The system is developed in python and makes use of a Telegram web interface. The connection between the system and the web interface is done by using a SSL connection provided by Ngrok. The system includes a BERT model with a span classification head on top to handle user questions. A naive bayes classifier is used for the sentiment analysis task and a Rasa intent classifier is used to recognise user intents.

An Intent Classifier For Chatbot

A NLP program that predicts the intent of a customer query email.

To extract intent from question using RNN.

In a task oriented domain, recognizing the intention of a speaker is important so that the conversation can proceed in the correct direction. This is possible only if there is a way to label the utterance with its proper intent. One such labeling technique is Dialog Act (DA) tagging. The main goal of this thesis is to build a Dialog Act tagger for the Telugu English Code Mixed corpus. Dialogue Act (DA) classification plays a key role in dialogue interpretation, especially in spontaneous conversation analysis. Dialogue acts are defined as the meaning of each utterance at the illocutionary force level. Code-Mixing (CM) is a very commonly observed mode of communication in a multilingual configuration. The trends of using this newly emerging language have its effect as a culling option especially in platforms like social media. This becomes particularly important in the context of technology and health, where expressing the upcoming advancements is difficult in native language. Despite the change of such language dynamics, current dialog systems cannot handle a switch between languages across sentences and mixing within a sentence. Everyday conversations are fabricated in this mixed language and analyzing dialog acts in this language is very essential in further advancements of making interaction with personal assistants more natural. Almost all standard traditional supervised machine learning approaches to classification have been applied in DA classification, from Support Vector Machines (SVM), Naïve Bayes, NLTK Classifiers, Max Entropy Classifier, Multilayer Perceptron, Conditional Random Field Classifier and Hidden Markov Model (HMM).

Post classification Experiment using Scikit learn Date 20/02/18 Dylan Butler Task The overall task of this experiment is to create a trained classifier to correctly classify whether or not a post is useful for quizes and knowledge testing of Java core concepts. Data The data for this experiment consists of a manually labelled dataset of 1500 stackoverflow posts. These posts have been filtered according to the following characteristics: They posses the structure of either a "how-to"(procedural intent) or a "why"(casual intent) type of question They have a minimum score of 7 (post score) They have not been deleted They have not been closed They have an accepted answer After extracting this data I conducted an analysis on the resulting dataset to gain a deeper understanding of the data: Extracted Data insights Group 1 (useful for quizzes): How to split a string in Java? Read and convert an input stream to a string? How to read all files in a folder in Java? How to round a number to n decimal places in Java? How to parse JSON in Java? How do I declare and initialize an array in Java? Why is it faster to process an unsorted array vs a sorted array How do I compare strings in Java? Group 2 (not useful fr quizzes): How do I fix android.os.NetworkOnMainThreadException? How do you assert that a certain exception is thrown in JUnit 4 tests? How to fix java.lang.UnsupportedClassVersionError: Unsupported major.minor version How to add local jar files to a Maven project? How do I set up IntelliJ IDEA for Android applications? How does autowiring work in Spring? How do I tell Maven to use the latest version of a dependency? Unfortunately MyApp has stopped. How can I solve this? Why is subtracting these two times (in 1927) giving a strange result? Key Findings Useless Q's A key difference I can spot is that most of the questions that pose no use are environment, framework, related and focus on a technology that uses Java. Verbs like; set-up, fix, stopped ... i.e. less java specific and more generic - used in everyday language. Useful Q's The useful questions seem to be following a pattern in which the main words in the questions (split, string, read, java, JSON, declare, initialize) are all words closely related to Java and programming concepts in general. The verbs/action words used in the useful q's are closely associated with java itself. Experiment Process Chunk tags and titles and bodies into a single body eliminate code snippets remove stop words lemmatise each body Extract the core features from the text that the algorithm can learn from Train a classifier Evaluate Improve results

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Natural language intent classifier

Created using React, parses CSV files into classified data, tokenise data and train models using tensorflow js

A simple API that allows chatbot users to train intent classification models as a service using personal data.

Open Deep Learning and Reinforcement Learning lectures from top Universities like Stanford University, MIT, UC Berkeley. And an intent classifier which can classifies a query into one of the 21 given intents.

Intent classifier in a Livebook, for the Elixir Amsterdam meetup, nov 2021

A multi-label intent classifier for natural language queries. Given a user query, DDM predicts one or more intent labels (e.g. general, content, vision, reminder) using a TF-IDF + LinearSVC pipeline.

Intent classifier of the VIRA chatbot

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Question Embeddings Based on Shannon Entropy. Solving intent classification task in goal-oriented dialogue system

Intent Classifier using BERT and TF2

DIET (Dual Intent and Entity Transformer) classifier implementation in Pytorch