What is Link Prediction?
Link prediction is a task in graph and network analysis that aims to predict missing or future connections in a network. In simpler terms, it is a method used to predict relationships that are likely to exist between objects in a network.
How Does Link Prediction Work?
Link prediction works by analyzing the connections between nodes in a partially observed network. Nodes are any objects, individuals or entities that are connected in the network. By studying the exist
Logical Reasoning Reading Comprehension: Improving Machine Comprehension Skills
Logical reasoning reading comprehension is an important task that measures the level of logical reasoning skills for machine reading comprehension. A dataset named ReClor (ICLR 2020) was proposed to evaluate the logical reasoning ability of the machine reading comprehension models. The dataset helps to improve the comprehension performance of the machine models by evaluating their ability to read and retain informat
Machine translation refers to the process of translating a sentence written in one language to another language using artificial intelligence and computer algorithms.
Approaches to Machine Translation
There are different approaches to machine translation, ranging from rule-based, statistical, to neural-based. In rule-based machine translation, experts create rules on how to translate words and phrases from the source language to the target language. Statistical methods use large datasets to a
Overview of Multilingual Machine Comprehension in English Hindi
As our world becomes increasingly connected, communication across different languages becomes more and more important. Multilingual Machine Comprehension (MMC) is a sub-task of Question-Answering (QA) that involves finding answers to questions in different languages by analyzing text snippets. In this article, we will explore the use of MMC in the English and Hindi languages.
Understanding Multilingual Machine Comprehension
Mult
Multimodal machine translation is an exciting and innovative technology that has made significant strides in the field of machine translation. This technology is capable of doing machine translation with multiple data sources from different modes, such as text, speech, and images. The idea behind multimodal machine translation is to improve the accuracy of machine translation by incorporating additional sources of information beyond simple text input.
What is Multimodal Machine Translation?
M
Natural Language Inference (NLI) is a fascinating task in the world of natural language processing that involves determining the relation between two sentences, namely the premise and the hypothesis. The goal of this task is to determine whether the hypothesis is true, false or neutral based on the given premise.
The NLI task explained
The NLI task involves analyzing the relationship between two sentences, namely the premise and the hypothesis. A premise is a statement that is given as true,
What is Negation Detection?
Negation detection is the process of identifying negation cues in text. Negation cues are words, phrases, or structures that indicate the presence of negation or denial in a sentence. Negation detection plays a critical role in natural language processing, as it helps identify and interpret the meaning of text accurately.
Why is Negation Detection Important?
Negation detection is important in many applications, such as sentiment analysis, question-answering system
Open-domain question answering is a type of task that aims to answer questions on open-domain data sets, such as the vast array of information found on Wikipedia. The goal is to provide accurate and relevant answers to questions in a way that simulates human intelligence, while relying purely on machine learning algorithms to do so.
What is Open-Domain Question Answering?
Open-domain question answering is a part of natural language processing that aims to answer questions posed to it by human
Open Information Extraction - An Overview
Open Information Extraction (OIE) is a method used in Natural Language Processing (NLP) to extract structured and machine-readable representations of the information present in a text. The goal is to extract the meaning of the text in the clearest and simplest way possible to create triples or n-ary propositions.
What is Open Information Extraction?
Open Information Extraction is a type of information extraction that uses a machine-learning algorithm
Optical Character Recognition (OCR) is a technology used to convert typed, handwritten or printed text into machine-encoded text. This conversion can be performed using electronic or mechanical devices. The technology is commonly used for scanning documents and photos to extract text from them.
How Does OCR Work?
OCR works by analyzing the shapes and patterns of text characters in an image. The technology uses complex algorithms to identify the patterns and convert them into machine-readable
Understand Part-of-Speech Tagging
When you read a sentence, you follow a set of rules that your brain automatically knows. You understand that certain words are nouns, verbs, adjectives, and so on. But what if you had to teach a computer to do the same thing? That's where part-of-speech tagging comes in.
What is Part-of-Speech Tagging?
Part-of-speech tagging is a process where a computer program examines each word in a text and determines what part of speech it belongs to. The different part
Question Answering is a type of machine learning task that involves answering questions based on a given context. The task is typically performed on reading comprehension questions, where an AI system is trained to read a passage of text and answer questions related to that passage.
Types of Question Answering
Question answering can be segmented into various types, including domain-specific tasks like community question answering and knowledge-base question answering. In a community question
Relation Classification: Understanding the Semantic Relationships between Two Entities in Text
Relation Classification is a crucial aspect of natural language processing that involves identifying and understanding the semantic relationships between two nominal entities in text. This process allows computers to comprehend the meaning of language in a more human-like manner, which can improve various applications such as information retrieval, question-answering systems, and machine translation.
Relation Extraction is a fundamental task in natural language processing (NLP) that involves predicting attributes and relationships among entities in sentences. This process is essential for building knowledge graphs and is used in various applications such as structured search, sentiment analysis, question answering, and summarization.
In simple terms, Relation Extraction involves identifying how entities in a sentence are related to each other. For instance, consider the sentence "John bough
Overview of Relation Mention Extraction
Relation Mention Extraction is a process that involves the identification of phrases or expressions in a text corpus that represent a specific type of relation between two entities. The extraction of these phrases is crucial for various natural language processing (NLP) tasks such as information retrieval, sentiment analysis, and question-answering systems.
In essence, Relation Mention Extraction seeks to identify the linguistic patterns that reflect rel
Overview of Relational Reasoning
Relational Reasoning is a problem-solving method that aims to understand the relationships between different entities, such as image pixels, words, or even complex human movements. This approach is used in a variety of fields, including computer science and artificial intelligence. By understanding how the different entities are connected, relational reasoning helps in predicting future outcomes, recognizing patterns, and making decisions.
Relational reasoning
Relationship extraction is a process that takes place in the field of Natural Language Processing (NLP). The aim of this process is to identify the connections between different entities in a text. These entities may be people, organizations or locations. The relationships between them can be of various types such as familial or organizational links. This is a very important task as it helps in categorizing and understanding the content of a text.
What is Distant Supervised Relationship Extrac
Sentence Pair Modeling: What it is and why it matters?
Sentence pair modeling is a technique used in natural language processing to evaluate two sentences based on their internal representation. In simple words, it compares two sentences and helps determine their relationship. This technique is widely used in chatbots, search engines, and many other applications that involve natural language processing. Sentence pair modeling is a crucial concept in NLP, and its importance is increasing day by