AI makes use of a wide range of ideas, methodologies, and technology. Machine learning, deep learning, neural networks, machine vision, cognitive computing, and natural language processing are some of the subfields. Other AI-supporting technologies include graphics processing units GPUs, the Internet of Things (IoT), sophisticated algorithms, and API. Learning theory alone is insufficient. That is why students and professionals are encouraged to try and complete artificial intelligence projects. In this blog, we have jotted down a list of project ideas that includes suggestions for both students/professionals who are already familiar with the industry.

Vector space models are to consider the relationship between data that are represented by vectors. It is popular in information retrieval systems but also useful for other purposes. Generally, this allows us to compare the similarity of two vectors from a geometric perspective. In this tutorial, we will see what is a vector space model and what it can do. A Gentle Introduction to Vector Space Models Photo by liamfletch, some rights reserved.

Thanks to rapid development of artificial intelligence technology in recent years, the current artificial intelligence technology is contributing to many part of society. Education, environment, medical care, military, tourism, economy, politics, etc. are having a very large impact on society as a whole. For example, in the field of education, there is an artificial intelligence tutoring system that automatically assigns tutors based on student's level. In the field of economics, there are quantitative investment methods that automatically analyze large amounts of data to find investment laws to create investment models or predict changes in financial markets. As such, artificial intelligence technology is being used in various fields. So, it is very important to know exactly what factors have an important influence on each field of artificial intelligence technology and how the relationship between each field is connected. Therefore, it is necessary to analyze artificial intelligence technology in each field. In this paper, we analyze patent documents related to artificial intelligence technology. We propose a method for keyword analysis within factors using artificial intelligence patent data sets for artificial intelligence technology analysis. This is a model that relies on feature engineering based on deep learning model named KeyBERT, and using vector space model. A case study of collecting and analyzing artificial intelligence patent data was conducted to show how the proposed model can be applied to real world problems.

Now calculate cosine similarity between each document and each query. For each query sort the cosine similarity scores for all the documents and take top-3 documents having high scores.

Link: Get Udemy Coupon ED NLP Programming Cosine Similarity for Beginners Using cosine similarity technique to perform document similarity in Java Programming Language.New What you'll learn Students will learn concepts about Natural Language Processing using Vector Space Model. One of the techniques to calculate Cosine Similarity and how to program Cosine This course shows how to perform document similarity using an information-based retrieval method such as vector space model by using cosine similarity technique. In the first part of the course, students will learn key concepts related to natural language and semantic information processing such as Binary Text Representation, Bag of Words, Lemmatization, TF, IDF, TF-IDF, Cosine Similarity, CamelCase and Identifiers. In the second part of the course, students will learn how to develop and implement a natural language software to perform document similarity. The course provides the basics to help students understand the theory and practical in Java Programming.

We design a new technique for the distributional semantic modeling with a neural network-based approach to learn distributed term representations (or term embeddings) - term vector space models as a result, inspired by the recent ontology-related approach (using different types of contextual knowledge such as syntactic knowledge, terminological knowledge, semantic knowledge, etc.) to the identification of terms (term extraction) and relations between them (relation extraction) called semantic pre-processing technology - SPT. Our method relies on automatic term extraction from the natural language texts and subsequent formation of the problem-oriented or application-oriented (also deeply annotated) text corpora where the fundamental entity is the term (includes non-compositional and compositional terms). This gives us an opportunity to changeover from distributed word representations (or word embeddings) to distributed term representations (or term embeddings). This transition will allow to generate more accurate semantic maps of different subject domains (also, of relations between input terms - it is useful to explore clusters and oppositions, or to test your hypotheses about them). The semantic map can be represented as a graph using Vec2graph - a Python library for visualizing word embeddings (term embeddings in our case) as dynamic and interactive graphs. The Vec2graph library coupled with term embeddings will not only improve accuracy in solving standard NLP tasks, but also update the conventional concept of automated ontology development. The main practical result of our work is the development kit (set of toolkits represented as web service APIs and web application), which provides all necessary routines for the basic linguistic pre-processing and the semantic pre-processing of the natural language texts in Ukrainian for future training of term vector space models.

Github has become the goto source for all things open-source and contains tons of resource for Machine Learning practitioners. We bring to you a list of 10 Github repositories with most stars. TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code.

The increasing popularity of internet, wireless technologies and mobile devices has led to the birth of mass connectivity and online interaction through Online Social Networks (OSNs) and similar environments. OSN reflects a social structure consist of a set of individuals and different types of ties like connections, relationships, interactions etc among them and helps its users to connect with their friends and common interest groups, share views and to pass information. Now days the u sers choose OSN sites as a most preferred place for sharing their updates, different views, posting photographs and would like to make it available for others for viewing, rating a nd making comments. Th e current paper aims to explore and analyze the association between the objects (like photographs, posts etc) and its viewers (friends, acquaintances etc) for a given user and to find activity relationship amo ng them by using the TF - I DF scheme of Vector Space Model. After vectorization the vector data has been presented through a weighted graph with various properties .

Automatic measurement of semantic text similarity is an important task in natural language processing. In this paper, we evaluate the performance of different vector space models to perform this task. We address the real-world problem of modeling patent-to-patent similarity and compare TFIDF (and related extensions), topic models (e.g., latent semantic indexing), and neural models (e.g., paragraph vectors). Contrary to expectations, the added computational cost of text embedding methods is justified only when: 1) the target text is condensed; and 2) the similarity comparison is trivial. Otherwise, TFIDF performs surprisingly well in other cases: in particular for longer and more technical texts or for making finer-grained distinctions between nearest neighbors. Unexpectedly, extensions to the TFIDF method, such as adding noun phrases or calculating term weights incrementally, were not helpful in our context.

One of my super nerdy interests include approximate algorithms for nearest neighbors in high-dimensional spaces. You have say 1M points in some high-dimensional space. Now given a query point, can you find the nearest points out of the 1M set? Doing this fast turns out to be tricky. I'm the author of Annoy which has more than 3,000 stars on Github.