Adversarial machine learning techniques in text and audio data involve generating manipulated samples to mislead models. Text attacks often involve word replacements or additions to alter the meaning while maintaining human readability. Various strategies are used to create adversarial text examples

1 views • 57 slides

Autoencoders (AEs) are neural networks trained using unsupervised learning to copy input to output, learning an embedding. This article discusses various types of autoencoders, topics in autoencoders, applications such as dimensionality reduction and image compression, and related concepts like embe

2 views • 86 slides

Translation involves transferring written text from one language to another, while interpreting deals with oral communication. Etymologically, the term "translation" comes from Latin meaning "to carry over." It is a process of replacing an original text with another in a different language. Translat

11 views • 76 slides

Graph-based data science offers a powerful approach to analyzing data by leveraging graph structures. This involves using graph representation, analysis algorithms, ML/AI techniques, kernels, embeddings, and neural networks. Real-world examples show the utility of data graphs in various domains like

3 views • 37 slides

Text features are essential components of nonfiction texts that authors use to enhance reader comprehension. They include elements such as tables of contents, indexes, glossaries, and titles, each serving a unique purpose in aiding readers to navigate and understand the content. By utilizing these t

1 views • 15 slides

Defense Personnel Analytics Center (DPAC) is enhancing their NLP capabilities by implementing a transformer-based platform on the Department of Defense's cloud system ADVANA. The platform focuses on topic modeling and sentiment analysis of open-ended survey responses from various DoD populations. Le

0 views • 13 slides

Create captivating visuals with this diverse collection of sample text images. From customizable text layouts to percentage displays, this set offers a range of design elements to elevate your creative projects. Explore different styles, colors, and compositions to enhance your presentations, websit

7 views • 10 slides

The similarity metric method for binary basic blocks is crucial in various applications like malware classification, vulnerability detection, and authorship analysis. This method involves two steps: sub-ldr operations and similarity score calculation. Different methods, both manual and automatic, ha

0 views • 20 slides

This presentation delves into the significance and methods of implementing positional encoding in Transformers for natural language processing tasks. It discusses the challenges faced by recurrent networks, introduces approaches like linear position assignment and sinusoidal/cosinusoidal positional

0 views • 15 slides

Explore the concept of word embeddings in natural language processing (NLP), which involves learning vectors that encode words. Discover the properties and relationships between words captured by these embeddings, along with questions around embedding space size and finding the right function. Delve

0 views • 28 slides

Autoencoders play a crucial role in supervised and unsupervised learning, with applications ranging from image classification to denoising and watermark removal. They compress input data into a latent space and reconstruct it to produce valuable embeddings. Autoencoders are data-specific, lossy, and

0 views • 18 slides

Significant advancements have been made in cross-lingual spoken language understanding (SLU) to overcome barriers related to labeled data availability in different languages. The development of a SLU model for a new language with minimal supervision and achieving reasonable performance has been a ke

0 views • 20 slides

Structured text is a high-level text language used in PLC programming to implement complex procedures not easily expressed with graphical languages. It involves logical operations, ladder diagrams, and efficient control logic for industrial automation. Concepts such as sensor input, logic operation

5 views • 23 slides

Delve into the basics of graph theory with topics like graph embeddings, graph plotting, Kuratowski's theorem, planar graphs, Euler characteristic, trees, and more. Explore the principles behind graphs, their properties, and key theorems that define their structure and connectivity.

0 views • 17 slides

This instructional text guides learners through the purpose of functional skills in analyzing different types of text, such as skimming and scanning, and understanding the features of various text genres. It includes activities to practice skimming, scanning, and detailed reading, with a focus on de

0 views • 13 slides

Explore the importance of alternate text in Microsoft documents for accessibility. Learn what alternate text is, why and when you should use it, and how to add it effectively. Discover the benefits of incorporating alternate text and the legal aspects related to accessibility under Section 508. Enha

0 views • 23 slides

Open question answering over tables and text is a challenging area in natural language processing. Various paradigms such as text-based QA, table/KB-only QA, and combined text and table QA have been explored. Incompleteness in answering specific questions like identifying the runner-up song on Billb

0 views • 24 slides

Tf-idf and PPMI are sparse representations, while alternative dense vectors offer shorter lengths with non-zero elements. Dense vectors may generalize better and capture synonymy effectively compared to sparse ones. Learn about dense embeddings like Word2vec, Fasttext, and Glove, which provide effic

0 views • 44 slides

When analyzing written texts, identifying the purpose and audience is crucial. The purpose reflects the reason behind the text, while the audience indicates who the text is intended for. By recognizing these aspects, one can better understand the content, language, and overall impact of the text. Va

1 views • 50 slides

Explore the evolution of word embeddings with a focus on dependency-based methods, showcasing innovations like Skip-Gram with Negative Sampling. Learn about Generalizing Skip-Gram and the shift towards analyzing linguistically rich embeddings using various contexts such as bag-of-words and syntactic

0 views • 39 slides

FCC is moving towards implementing the 988 National Suicide Hotline by July 16, 2022. The proposal includes requiring text providers to support text messaging to 988, defining text messages covered, and setting a nationwide implementation deadline. Technical considerations like text routing and boun

1 views • 8 slides

Explore key details about the text-to-911 system, including capturing text conversations, handling abandoned calls, transferring text calls to queues, and managing text conversations effectively. Learn about system configurations, call release timings, and dispatcher capabilities in handling text me

0 views • 12 slides

Test your knowledge on Text-to-911 system operations with this quiz. Learn about capturing text conversations, handling abandoned calls, transferring calls to queues, text conversation timelines, and more. Enhance your understanding of the protocols and procedures involved in managing text-based eme

1 views • 12 slides

This study introduces WEB-SOBA, a method for semi-automatically building ontologies using word embeddings for aspect-based sentiment analysis. With the growing importance of online reviews, the focus is on sentiment mining to extract insights from consumer feedback. The motivation behind the researc

2 views • 35 slides

Explore the importance of text similarity in NLP, how it aids in understanding related concepts and processing language, human judgments of similarity, automatic similarity computation using word embeddings like word2vec, and various types of text similarity such as semantic, morphological, and sent

2 views • 8 slides

Utilizing sketching in algorithmic design, Alex Andoni from Columbia University explores methodologies such as succinct efficient algorithms, dimension reduction, sampling, metric embeddings, and more. The approach involves numerical linear algebra, similarity search, and geometric min-cost matching

0 views • 18 slides

Growing importance of online reviews highlights the need for automation in sentiment mining. Aspect-Based Sentiment Analysis (ABSA) focuses on detecting sentiments expressed in product reviews, with a specific emphasis on sentence-level analysis. The proposed approach, Deep Contextual Word Embedding

0 views • 34 slides

Data augmentation plays a crucial role in knowledge distillation processes, enhancing model performance by generating diverse training data. Techniques such as token replacement, representation interpolation, and rich semantics are explored in the context of improving image classifier performance. T

0 views • 13 slides

Word embeddings play a crucial role in representing words as compact vectors. This comprehensive overview delves into the concept of word embeddings, discussing approaches like one-hot encoding, histograms of co-occurring words, and more advanced techniques like word2vec. The exploration covers topi

0 views • 20 slides

Jeff Edmonds is a researcher with interests in various theoretical and mathematical topics, including scheduling algorithms, cake cutting, and topological embeddings. He also provides support for mathematical and theoretical topics. Additionally, Jeff has ventured into machine learning to explore ne

0 views • 17 slides

The evolution of sentiment analysis on tweets from SemEval competitions in 2013 to 2017 is discussed, showcasing advancements in technology and the shift from SVM and sentiment lexicons to CNN with word embeddings. Aspect-Based Sentiment Analysis, as explored in SemEval2014, involves determining asp

0 views • 23 slides

Text representation and mining play a crucial role in Business Intelligence and Analytics. Dealing with text data, understanding why text is difficult, and the importance of text preprocessing are key aspects covered in this session. Learn about the goals of text representation, the concept of Bag o

0 views • 27 slides

Motivated by the increasing number of online product reviews, this research explores automation in sentiment mining through Aspect-Based Sentiment Analysis (ABSA). The focus is on sentiment detection for aspects at the review level, using a hybrid approach that combines ontology-based reasoning and

0 views • 26 slides

Word embeddings involve learning an encoding for words into vectors to capture relationships between them. Functions like W(word) return vector encodings for specific words, aiding in tasks like prediction and classification. Techniques such as word2vec offer methods like CBOW and Skip-gram to predi

0 views • 27 slides

Discover the power of JMP tools for text exploration with examples of data curation steps, quantifying text comments, and modeling ratings data. Learn about data requirements, overall processing steps, key definitions, and the bag of words approach in text analysis using Amazon gourmet food review d

0 views • 23 slides

Today's lecture in the Computational Techniques for Linguists course covered the concept of bigrams using NLTK. Bigrams are pairs of words found in text, which are essential for tasks like random text generation. The lecture demonstrated how to work with bigrams, including examples from the NLTK boo

0 views • 19 slides

Explore how word vectors enable easy computation of similarity and relatedness, along with approaches for representing words using distributional semantics. Discover the contributions of word embeddings through novel algorithms and hyperparameters for improved performance.

0 views • 69 slides

Word vector models play a crucial role in representing words as vectors in NLP tasks. Subrata Chattopadhyay's Word Vector Model introduces concepts like word representation, one-hot encoding, limitations, and Word2Vec models. It explains the shift from one-hot encoding to distributed representations

0 views • 25 slides

In the domain of neural networks, the Transformer architecture has revolutionized sequence-to-sequence translation tasks. This involves attention mechanisms, multi-head attention, transformer encoder layers, and positional embeddings to enhance the translation process. Additionally, Encoder-Decoder

0 views • 24 slides

This resource delves into the significance of text-dependent questions in improving students' reading comprehension skills by emphasizing the importance of evidence from the text, building knowledge through nonfiction, and developing critical thinking abilities. It highlights key advances in educati

0 views • 16 slides