orbulon,github

100-times-faster-nlp

🚀100 Times Faster Natural Language Processing in Python - iPython notebook

A chatbot (also known as a talkbot, chatterbot, Bot, IM bot, interactive agent, or Artificial Conversational Entity)The classic historic early chatbots are ELIZA (1966) and PARRY (1972).More recent notable programs include A.L.I.C.E., Jabberwacky and D.U.D.E (Agence Nationale de la Recherche and CNRS 2006). While ELIZA and PARRY were used exclusively to simulate typed conversation, many chatbots now include functional features such as games and web searching abilities. In 1984, a book called The Policeman's Beard is Half Constructed was published, allegedly written by the chatbot Racter (though the program as released would not have been capable of doing so). One pertinent field of AI research is natural language processing. Usually, weak AI fields employ specialized software or programming languages created specifically for the narrow function required. For example, A.L.I.C.E. uses a markup language called AIML, which is specific to its function as a conversational agent, and has since been adopted by various other developers of, so called, Alicebots. Nevertheless, A.L.I.C.E. is still purely based on pattern matching techniques without any reasoning capabilities, the same technique ELIZA was using back in 1966. This is not strong AI, which would require sapience and logical reasoning abilities. Jabberwacky learns new responses and context based on real-time user interactions, rather than being driven from a static database. Some more recent chatbots also combine real-time learning with evolutionary algorithms that optimise their ability to communicate based on each conversation held. Still, there is currently no general purpose conversational artificial intelligence, and some software developers focus on the practical aspect, information retrieval. Chatbot competitions focus on the Turing test or more specific goals. Two such annual contests are the Loebner Prize and The Chatterbox Challenge (offline since 2015, materials can still be found from web archives). According to Forrester (2015), AI will replace 16 percent of American jobs by the end of the decade.Chatbots have been used in applications such as customer service, sales and product education. However, a study conducted by Narrative Science in 2015 found that 80 percent of their respondents believe AI improves worker performance and creates jobs.[citation needed] is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods. Such programs are often designed to convincingly simulate how a human would behave as a conversational partner, thereby passing the Turing test. Chatbots are typically used in dialog systems for various practical purposes including customer service or information acquisition. Some chatterbots use sophisticated natural language processing systems, but many simpler systems scan for keywords within the input, then pull a reply with the most matching keywords, or the most similar wording pattern, from a database. The term "ChatterBot" was originally coined by Michael Mauldin (creator of the first Verbot, Julia) in 1994 to describe these conversational programs.Today, most chatbots are either accessed via virtual assistants such as Google Assistant and Amazon Alexa, via messaging apps such as Facebook Messenger or WeChat, or via individual organizations' apps and websites. Chatbots can be classified into usage categories such as conversational commerce (e-commerce via chat), analytics, communication, customer support, design, developer tools, education, entertainment, finance, food, games, health, HR, marketing, news, personal, productivity, shopping, social, sports, travel and utilities. Background

aopjs

More than a billion of the rural merchants in the developing world commonly depend on hiring on-demand transportation services to commute people or goods to markets. The process of selecting the optimal fare involves handling decision-making characterised with multiple alternatives and competing criteria. Decision support systems are commonly used to solve these types of problems. However, most widely used systems are based on object-based approaches which lack high-level abstractions needed to effectively model and scale human-machine communication. This paper reviews previous literature on the field and introduces an improved preliminary agent-based decision-support approach to overcome those challenges. As a proof of concept, we developed a two-agent simulation that, given a request from one of the agents, the other one takes a dataset of a stratified sample of 104 Ethiopian commuter criteria preferences taken from the Dukem region and an exemplary dataset of fare alternatives. The assistant agent computes those datasets using widely used HPA and TOPSIS algorithms to weight, score, rank those alternatives. Once we run the simulation, in a matter of milliseconds the assistant agent effectively returns an optimal prescription to the other agent, storing all interactions in a self-contained memory resulting in an architecture that allows developers to program further customisation as interactions scale.

atom

:atom: The hackable text editor

audio

Data manipulation and transformation for audio signal processing, powered by PyTorch

awd-lstm-lm

blink

Entity Linker solution

cc-tutor-frontend

A compilation process visualization, interaction system and a compiler construction assistant.

craft-pytorch

Official implementation of Character Region Awareness for Text Detection (CRAFT)

deep-learning-keras-tensorflow

Introduction to Deep Neural Networks with Keras and Tensorflow

deepnlp-models-pytorch

Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ)

dialog-act-tagging-for-code-mixed-data-set

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).