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AART: AI Assisted Review Tool for Marketing Creatives
Data science in marketing has become critical in gaining sustained competitive advantage in a rapidly changing business environment. It involves using advanced analytics and scientific principles to extract valuable information from large volumes of data gathered from multiple sources, such as social media platforms. There are multiple benefits to using data science in marketing, including proper data-based planning, enhanced customization, enhanced forecasting through predictive analytics, effective ROI measuring, and improved pricing models. The research explains how companies can turn the potential and opportunities of these advanced analytics techniques into real company performance in a competitive marketing environment. This research aims to explore how firms can use marketing analytics and big data to improve capabilities and performance. Specifically, the study argues that big data and marketing analytics can be used to extract valuable and meaningful marketing information and insights that can be integrated to improve marketing effectiveness and performance.
As robots become more ubiquitous and capable of performing complex tasks, the importance of enabling untrained users to interact with them has increased. In response, unconstrained natural-language interaction with robots has emerged as a significant research area. We discuss the problem of parsing natural language commands to actions and control structures that can be readily implemented in a robot execution system. Our approach learns a parser based on example pairs of English commands and corresponding control language expressions. We evaluate this approach in the context of following route instructions through an indoor envi-ronment, and demonstrate that our system can learn to translate English commands into sequences of desired actions, while correctly capturing the semantic intent of statements involving complex control structures. The procedural nature of our for-mal representation allows a robot to interpret route instructions online while moving through a previously unknown environment.
Dependency-based methods for syntactic parsing have become increasingly popular in natural language processing in recent years. This book gives a thorough introduction to the methods that are most widely used today. After an introduction to dependency grammar and dependency parsing, followed by a formal characterization of the dependency parsing problem, the book surveys the three major classes of parsing models that are in current use: transition-based, graph-based, and grammar-based models. It continues with a chapter on evaluation and one on the comparison of different methods, and it closes with a few words on current trends and future prospects of dependency parsing. The book presupposes a knowledge of basic concepts in linguistics and computer science, as well as some knowledge of parsing methods for constituency-based representations.
In this paper, we focus on learning structure-aware document representations from data without recourse to a discourse parser or additional annotations. Drawing inspiration from recent efforts to empower neural networks with a structural bias, we propose a model that can encode a document while automatically inducing rich structural dependencies. Specifically, we embed a differentiable non-projective parsing algorithm into a neural model and use attention mechanisms to incorporate the structural biases. Experimental evaluation across different tasks and datasets shows that the proposed model achieves state-of-the-art results on document modeling tasks while inducing intermediate structures which are both interpretable and meaningful.
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The main aim of this chapter is to study the effects of structural representation of text documents when applying a connectionist approach to modelling the domain. While text documents are often processed un-structured, we will show in this chapter that the performance and problem solving capability of machine learning methods can be enhanced through the use of suitable structural representations of text documents. It will be shown that the extraction of structure from text documents does not require a knowledge of the underlying semantic relationships among words used in the text. This chapter describes an extension of the bag of words approach. By incorporating the "relatedness" of word tokens as they are used in the context of a document, this results in a structural representation of text documents which is richer in information than the bag of words approach alone. An application to very large datasets for a classification and a regression problem will show that our approach scales very well. The classification problem will be tackled by the latest in a series of techniques which applied the idea of self organizing map to graph domains. It is shown that with the incorporation of the relatedness information as expressed using the Concept Link Graph, the resulting clusters are tighter when compared them with those obtained using a self organizing map alone using a bag of words representation. The regression problem is to rank a text corpus. In this case, the idea is to include content information in the ranking of documents and compare them with those obtained using PageRank. In this case, the results are inconclusive due possibly to the truncation of the representation of the Concept Link Graph representations. It is conjectured that the ranking of documents will be sped up if we include the Concept Link Graph representation of all documents together with their hyperlinked structure. The methods described in this chapter are capable of solving real world and data mining problems.
Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. The book will open the way for including causal analysis in the standard curricula of statistics, artificial intelligence, business, epidemiology, social sciences, and economics. Students in these fields will find natural models, simple inferential procedures, and precise mathematical definitions of causal concepts that traditional texts have evaded or made unduly complicated. The first edition of Causality has led to a paradigmatic change in the way that causality is treated in statistics, philosophy, computer science, social science, and economics. Cited in more than 5,000 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers’ questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interests to students and professionals in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable.
Unsupervised word representations are very useful in NLP tasks both as inputs to learning algorithms and as extra word features in NLP systems. However, most of these models are built with only local context and one representation per word. This is problematic because words are often polysemous and global context can also provide useful information for learning word meanings. We present a new neural network architecture which 1) learns word embeddings that better capture the semantics of words by incorporating both local and global document context, and 2) accounts for homonymy and polysemy by learning multiple embeddings per word. We introduce a new dataset with human judgments on pairs of words in sentential context, and evaluate our model on it, showing that our model outperforms competitive baselines and other neural language models.
Large scale scanning and document conversion ef- forts have led to a renewed interest in OCR systems and workflows. This paper describes a new format for representing both intermediate and final OCR results, developed in response to the needs of a newly developed OCR system and ground truth data release. The format embeds OCR information invisibly inside the HTML and CSS standards and therefore can represent a wide range of linguistic and typographic phenomena with al- ready well-defined, widely understood markup and can be processed using widely available and known tools. The format is based on a new, multi-level abstraction of OCR results based on logical markup, common typeset- ting models, and OCR engine-specific markup, making it suitable both for the support of existing workflows and the development of future model-based OCR engines.
An algorithm is presented that searches for the location, ″i,″ of the first occurrence of a character string, ″pat,″ in another string, ″string.″ During the search operation, the characters of pat are matched starting with the last character of pat. The information gained by starting the match at the end of the pattern often allows the algorithm to proceed in large jumps through the text being searched. Thus the algorithm has the unusual property that, in most cases, not all of the first i characters of string are inspected. The number of characters actually inspected (on the average) decreases as a function of the length of pat. For a random English pattern of length 5, the algorithm will typically inspect i/4 characters of string before finding a match at i. Furthermore, the algorithm has been implemented so that (on the average) fewer than i plus patlen machine instructions are executed. These conclusions are supported with empirical evidence and a theoretical analysis of the average behavior of the algorithm. The worst case behavior of the algorithm is linear in i plus patlen, assuming the availability of array space for tables linear in patlen plus the size of the alphabet.
Large scale scanning and document conversion efforts have led to a renewed interest in OCR systems and workflows. This paper describes a new format for representing both intermediate and final OCR results, developed in response to the needs of a newly developed OCR system and ground truth data release. The format embeds OCR information invisibly inside the HTML and CSS standards and therefore can represent a wide range of linguistic and typographic phenomena with already well-defined, widely understood markup and can be processed using widely available and known tools. The format is based on a new, multi-level abstraction of OCR results based on logical markup, common typesetting models, and OCR engine-specific markup, making it suitable both for the support of existing workflows and the development of future model-based OCR engines.
The aim of this paper is to give a comprehensive comparison of well-known longest common subsequence algorithms (for two input strings) and study their behaviour in various application environments. The performance of the methods depends heavily on the properties of the problem instance as well as the supporting data structures used in the implementation. We want to make also a clear distinction between methods that determine the actual lcs and those calculating only its length, since the execution time and more importantly, the space demand depends crucially on the type of the task. To our knowledge, this is the first time this kind of survey has been done. Due to the page limits, the paper gives only a coarse overview of the performance of the algorithms; more detailed studies are reported elsewhere
Introduction to Data Mining (First Edition)
- Michael Pang-Ning Tan
- Vipin Steinbach
- Kumar
Jaccard Distance ( Jaccard Index
- John M Hancock
- Hancock M.
Learning to Interpret Natural Language Commands through Human-Robot Dialog
- Jesse Thomason
- Shiqi Zhang
- Raymond Mooney
- Peter Stone
- Thomason Jesse