The main aim of SenticNet is to make the conceptual and affective information conveyed by natural language (meant for human consumption) more easily-accessible to machines.
I recently created a demo for some prospective clients of mine, demonstrating how to use Large Language Models (LLMs) together with graph databases like Neo4J.
The two have a lot of interesting interactions, namely that you can now create knowledge graphs easier than ever before, by having AI find the graph entities and relationships from your unstructured data, rather than having to do all that manually.
On top of that, graph databases also have some advantages for Retrieval Augmented Generation (RAG) applications compared to vector search, which is currently the prevailing approach to RAG.
I'm not sure what these vectors are, since BERT does not generate meaningful sentence vectors. It seems that this is is doing average pooling over the word tokens to get a sentence vector, but we never suggested that this will generate meaningful sentence representations. And even if they are decent representations when fed into a DNN trained for a downstream task, it doesn't mean that they will be meaningful in terms of cosine distance. (Since cosine distance is a linear space where all dimensions are weighted equally).
In statistics, the Bhattacharyya distance measures the similarity of two probability distributions. It is closely related to the Bhattacharyya coefficient which is a measure of the amount of overlap between two statistical samples or populations. Both measures are named after Anil Kumar Bhattacharya, a statistician who worked in the 1930s at the Indian Statistical Institute.[1]
The coefficient can be used to determine the relative closeness of the two samples being considered. It is used to measure the separability of classes in classification and it is considered to be more reliable than the Mahalanobis distance, as the Mahalanobis distance is a particular case of the Bhattacharyya distance when the standard deviations of the two classes are the same. Consequently, when two classes have similar means but different standard deviations, the Mahalanobis distance would tend to zero, whereas the Bhattacharyya distance grows depending on the difference between the standard deviations.
Build document-based question-answering systems using LangChain, Pinecone, LLMs like GPT-4, and semantic search for precise, context-aware AI solutions.
The ultimate guide to chatbot analytics. Find out what bot metrics and KPIs you should measure and discover easy ways to optimize your chatbot performance.
In natural language processing (NLP) field, it is hard to augmenting text due to high complexity of language. Not every word we can replace it by others such as a, an, the. Also, not every word has synonym. Even changing a word, the context will be totally difference. On the other hand, generating augmented image in computer vision area is relative easier. Even introducing noise or cropping out portion of image, model can still classify the image.
As data engineers, you might have heard the terms functional data pipeline, factory pattern, singleton pattern, etc. One can quickly look up the implementation, but it can be tricky to understand what they are precisely and when to (& when not to) use them. Blindly following a pattern can help in some cases, but not knowing the caveats of a design will lead to hard-to-maintain and brittle code! While writing clean and easy-to-read code takes years of experience, you can accelerate that by understanding the nuances and reasoning behind each pattern. Imagine being able to design an implementation that provides the best extensibility and maintainability! Your colleagues (& future self) will be extremely grateful, your feature delivery speed will increase, and your boss will highly value your opinion. In this post, we will go over the specific code design patterns used for data pipelines, when and why to use them, and when not to use them, and we will also go over a few python specific techniques to help you write better pipelines. By the end of this post, you will be able to identify patterns in your data pipelines and apply the appropriate code design patterns. You will also be able to take advantage of pythonic features to write bug-free, maintainable code that is a joy to work on!
Recent explosion in the popularity of large language models like ChatGPT has led to their increased usage in classical NLP tasks like language classification. This involves providing a context…
Large language models (LLMs) have proven to be valuable tools, but they often lack reliability. Many instances have surfaced where LLM-generated responses included false information. Specifically…
TLDR — Extractive question answering is an important task for providing a good user experience in many applications. The popular Retriever-Reader framework for QA using BERT can be difficult to scale…
D-Tale is an interactive web-based library that consists of a Flask backend and a React front-end serving as an easy way to view & analyze Pandas data structures. It integrates seamlessly with ipython notebooks & python/ipython terminals. Currently, this tool supports such Pandas objects as DataFrame, Series, MultiIndex, DatetimeIndex & RangeIndex.
Facebook Research open sourced a great project recently – fastText, a fast (no surprise) and effective method to learn word representations and perform text classification. I was curious about comparing these embeddings to other commonly used embeddings, so word2vec seemed like the obvious choice, especially considering fastText embeddings are an extension of word2vec.
Eversince Nov 2022, as Microsoft and OpenAI accounted ChatGTP the LLM space has been revolutionized and democratized. The demand to adopt the technology and apply it to the diverse use cases across…
You want to discern how many clusters we have (or, if you prefer, how many gaussians components generated the data), and you don’t have information about the “ground truth”. A real case, where data do not have the nicety of behaving good as the simulated ones.
Pandas AI is a Python library that integrates generative artificial intelligence capabilities into Pandas, making dataframes conversational - GitHub - gventuri/pandas-ai: Pandas AI is a Python library that integrates generative artificial intelligence capabilities into Pandas, making dataframes conversational
We’ve done a lot of looking over our shoulders at OpenAI. Who will cross the next milestone? What will the next move be?
But the uncomfortable truth is, we aren’t positioned to win this arms race and neither is OpenAI. While we’ve been squabbling, a third faction has been quietly eating our lunch.
I’m talking, of course, about open source. Plainly put, they are lapping us. Things we consider “major open problems” are solved and in people’s hands today.
In the ever-evolving world of technology, natural language processing (NLP) and artificial intelligence (AI) have been turning heads with their jaw-dropping advancements. One of the standout players…
In this article, we’ll look at Weakly Supervised Learning (WSL), which provides a solution by leveraging “weak” annotations to learn the task. But before we dive deeper into the techniques, it is worth exploring the various types of WSL techniques and the sections we intend to cover in this article.
OpenChat is a series of open-source language models fine-tuned on a diverse and high-quality dataset of multi-round conversations. With only ~6K GPT-4 conversations filtered from the ~90K ShareGPT conversations, OpenChat is designed to achieve high performance with limited data.
In today’s rapidly evolving tech landscape, the integration of advanced language models with robust data management systems is opening new horizons for data processing and analytics. One of the most…
Topic modelling refers to the task of identifying topics that best describes a set of documents. These topics will only emerge during the topic modelling process (therefore called latent). And one…
In probability theory and statistics, the Jensen–Shannon divergence is a method of measuring the similarity between two probability distributions. It is also known as information radius (IRad)[1] or total divergence to the average.[2] It is based on the Kullback–Leibler divergence, with some notable (and useful) differences, including that it is symmetric and it always has a finite value. The square root of the Jensen–Shannon divergence is a metric often referred to as Jensen-Shannon distance.[3][4][5]
Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text.
LSA is an information retrieval technique which analyzes and identifies the pattern in unstructured collection of text and the relationship between them.
LSA itself is an unsupervised way of uncovering synonyms in a collection of documents.
To start, we take a look how Latent Semantic Analysis is used in Natural Language Processing to analyze relationships between a set of documents and the terms that they contain. Then we go steps further to analyze and classify sentiment. We will review Chi Squared for feature selection along the way.
Learn Prompting is the largest and most comprehensive course in prompt engineering available on the internet, with over 60 content modules, translated into 9 languages, and a thriving community.
The recent release of this open-source project, LlamaFS, addresses the challenges associated with traditional file management systems, particularly in the context of overstuffed download folders, inefficient file organization, and the limitations of knowledge-based organization. These issues arise due to the manual nature of file sorting, which often leads to inconsistent structures and difficulty finding specific files. The disorganization in the file system hampers productivity and makes it challenging to locate important files quickly.
This talk explores the integration of Knowledge Graphs (KGs) and Large Language Models (LLM) to harness their combined power for improved natural language understanding. By leveraging KGs' structured knowledge and language models' text comprehension abilities, we can leverage the domain-specific–and potentially sensitive–data together with the general knowledge of LLMs.
We also examine how language models can enhance KGs through knowledge extraction and refinement. The integration of these technologies presents opportunities in various domains, from question-answering to chatbots, fostering more intelligent and context-aware applications.
MACE (Multi-Annotator Competence Estimation) is an implementation of an item-response model that let's you evaluate redundant annotations of categorical data. It provides competence estimates of the individual annotators and the most likely answer to each item.
If we have 10 annotators answer a question, and five answer with 'yes' and five with 'no' (a surprisingly frequent event), we would normaly have to flip a coin to decide what the right answer is. If we knew, however, that one of the people who answered 'yes' is an expert on the question, while one of the others just alwas selects 'no', we would take this information into account to weight their answers. MACE does exactly that. It tries to find out which annotators are more trustworthy and upweighs their answers. All you need to provide is a CSV file with one item per line.
In tests, MACE's trust estimates correlated highly wth the annotators' true competence, and it achieved accuracies of over 0.9 on several test sets. MACE can take annotated items into account, if they are available. This helps to guide the training and improves accuracy.
These measurements are indispensable for tracking the results of your chatbot, identifying any stumbling blocks and continuously improving its performance. But which metrics should you choose?
The pulearn Python package provide a collection of scikit-learn wrappers to several positive-unlabled learning (PU-learning) methods.
Features
Scikit-learn compliant wrappers to prominent PU-learning methods.
Fully tested on Linux, macOS and Windows systems.
Compatible with Python 3.5+.
This page is a distribution site for movie-review data for use in sentiment-analysis experiments. Available are collections of movie-review documents labeled with respect to their overall sentiment polarity (positive or negative) or subjective rating (e.g., "two and a half stars") and sentences labeled with respect to their subjectivity status (subjective or objective) or polarity. These data sets were introduced in the following papers:
In this article, I am going to show you how to choose the number of principal components when using principal component analysis for dimensionality reduction.
In the first section, I am going to give you a short answer for those of you who are in a hurry and want to get something working. Later, I am going to provide a more extended explanation for those of you who are interested in understanding PCA.
Perplexity is a useful metric to evaluate models in Natural Language Processing (NLP). This article will cover the two ways in which it is normally defined and the intuitions behind them. A language…
At the very beginning of the tutorial, I’ll explain the dimensionality of a dataset, what dimensionality reduction means, the main approaches to dimensionality reduction, the reasons for dimensionality reduction and what PCA means. Then, I will go deeper into the topic of PCA by implementing the PCA algorithm with the Scikit-learn machine learning library. This will help you to easily apply PCA to a real-world dataset and get results very fast.
As data scientists, we spend a lot of our time doing exploratory data analysis (EDA), cleaning data and making sure the data we use to generate insights is of good quality. Have you ever found…
When the downstream applications only care about the direction of the word vectors (e.g. they only pay attention to the cosine similarity of two words), then normalize, and forget about length.
However, if the downstream applications are able to (or need to) consider more sensible aspects, such as word significance, or consistency in word usage (see below), then normalization might not be such a good idea.
B. Pang, and L. Lee. Proceedings of the Association for Computational Linguistics (ACL), page 271--278. Association for Computational Linguistics, (2004)
S. Basu, A. Banerjee, and R. Mooney. Proceedings of the 2004 SIAM International Conference on Data Mining, page 333--344. Lake Buena Vista, FL, Society for Industrial and Applied Mathematics, (April 2004)
N. Hossain, J. Krumm, and M. Gamon. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), page 133--142. (2019)