I readily admit that I'm biased toward Python. This isn't intentional -- such is the case with many biases -- but coming from a computer science background and having been programming since a very young age, I have naturally tended towards general purpose programming languages (Java, C, C, Python, etc.). This is the major reason that Python books and resources are at the forefront of my radar, recommendations, and reviews. Obviously, however, not all data scientists are in this same position, given that there are innumerable paths to data science. Given that, and since R is powerful and popular programming language for a large swath of data scientists, today let's take a look at a book which uses R as a tool to implement solutions to data science problems.
MetLife processes over 260,000 life insurance applications a year. Underwriting of these applications is labor intensive. Automation is difficult because the applications include many free-form text fields. MetLife's intelligent text analyzer (MITA) uses the information-extraction technique of natural language processing to structure the extensive textual fields on a life insurance application. Knowledge engineering, with the help of underwriters as domain experts, was performed to elicit significant concepts for both medical and occupational textual fields.
Question Aware Open Information Extraction (Question aware Open IE) takes question and passage as inputs, outputting an answer tuple which contains a subject, a predicate, and one or more arguments. Each field of answer is a natural language word sequence and is extracted from the passage. The semi-structured answer has two advantages which are more readable and falsifiable compared to span answer. There are two approaches to solve this problem. One is an extractive method which extracts candidate answers from the passage with the Open IE model, and ranks them by matching with questions. It fully uses the passage information at the extraction step, but the extraction is independent to the question. The other one is the generative method which uses a sequence to sequence model to generate answers directly. It combines the question and passage as input at the same time, but it generates the answer from scratch, which does not use the facts that most of the answer words come from in the passage. To guide the generation by passage, we present a two-stage decoding model which contains a tagging decoder and a correction decoder. At the first stage, the tagging decoder will tag keywords from the passage. At the second stage, the correction decoder will generate answers based on tagged keywords. Our model could be trained end-to-end although it has two stages. Compared to previous generative models, we generate better answers by generating coarse to fine. We evaluate our model on WebAssertions (Yan et al., 2018) which is a Question aware Open IE dataset. Our model achieves a BLEU score of 59.32, which is better than previous generative methods.
In this project we are extracting data from different types of templates, it may be fixed template, semi-fixed template, non-fixed template or a receipt. Here we go through ROI and OCR concepts. A region of interest includes samples within a data set identified for a particular purpose. The concept of a ROI is commonly used in many application areas. For example, in medical imaging, the boundaries of a tumor may be defined on an image or in a volume, for the purpose of measuring its size.
Learn how to pre-process your text data and build topic modeling, text summarization and sentiment analysis applications New Created by Dr. Ali Feizollah English English [Auto] PREVIEW THIS COURSE - GET COUPON CODE Description Text mining and Natural Language Processing (NLP) are among the most active research areas. Pre-processing your text data before feeding it to an algorithm is a crucial part of NLP. In this course, you will learn NLP using natural language toolkit (NLTK), which is part of the Python. You will learn pre-processing of data to make it ready for any NLP application. We go through text cleaning, stemming, lemmatization, part of speech tagging, and stop words removal.
Clearwater Analytics, a Software-as-a-service (Saas) buy-side data aggregation and portfolio reporting specialist, this week launches a new machine learning-based information extraction service. The data extraction solution focuses on data aggregation and normalization, drilling into transactional data to create a service that automates the ingestion of many types of data which are traditionally manually entered. The solution uses advanced AI techniques including natural language processing (NLP) and deep learning to identify key data elements in a variety of document types, then extracts the data and feeds it into Clearwater's data aggregation engine to be reconciled. "We are committed to providing our clients with the most accurate data possible for their reporting needs," says Warren Barkley, Chief Technology Officer at Clearwater Analytics. "Machine learning-backed data extraction eliminates the need for manual intervention with unstructured data and allows our clients faster access to more accurate information."
Retrieving information from documents and forms has long been a challenge, and even now at the time of writing, organisations are still handling significant amounts of paper forms that need to be scanned, classified and mined for specific information to enable downstream automation and efficiencies. Automating this extraction and applying intelligence is in fact a fundamental step toward digital transformation that organisations are still struggling to solve in an efficient and scalable manner. An example could be a bank that receives hundreds of kilograms of very diverse remittance forms a day that need to be processed manually by people in order to extract a few key fields. Or medicinal prescriptions need to be automated to extract the prescribed medication and quantity. Typically organisations will have built text mining and search solutions which are often tailored for a scenario, with baked in application logic, resulting in an often brittle solution that is difficult and expensive to maintain.
We offer topics that are full Stuck future. Cool products and a team that wants to make a difference together. We are looking for bright minds full of commitment and enthusiasm! Averbis develops intelligent software solutions that understand texts and make meaningful predictions. In this way we help people to gain new insights, automate processes and make the right decisions.
Time is deeply woven into how people perceive, and communicate about the world. Almost unconsciously, we provide our language utterances with temporal cues, like verb tenses, and we can hardly produce sentences without such cues. Extracting temporal cues from text, and constructing a global temporal view about the order of described events is a major challenge of automatic natural language understanding. Temporal reasoning, the process of combining different temporal cues into a coherent temporal view, plays a central role in temporal information extraction. This article presents a comprehensive survey of the research from the past decades on temporal reasoning for automatic temporal information extraction from text, providing a case study on the integration of symbolic reasoning with machine learning-based information extraction systems.