SAS' Ali Dixon and Mary Osborne reveal why a BERT-based classifier is now part of our natural language processing capabilities of SAS Viya.
Tag: SAS Visual Text Analytics
To find exact duplicates, matching all string pairs is the simplest approach, but it is not a very efficient or sufficient technique. Using the MD5 or SHA-1 hash algorithms can get us a correct outcome with a faster speed, yet near-duplicates would still not be on the radar. Text similarity is useful for finding files that look alike. There are various approaches to this and each of them has its own way to define documents that are considered duplicates. Furthermore, the definition of duplicate documents has implications for the type of processing and the results produced. Below are some of the options. Using SAS Visual Text Analytics, you can customize and accomplish this task during your corpus analysis journey either with Python SWAT package or with PROC SQL in SAS.
Word embeddings are the learned representations of words within a set of documents. Each word or term is represented as a real-valued vector within a vector space. Terms or words that reside closer to each other within that vector space are expected to share similar meanings. Thus, embeddings try to capture the meaning of each word or term through its relationships with the other words in the corpus.
In Part I of this blog post, I provided an overview of the approach my team and I took tackling the problem of classifying diverse, messy documents at scale. I shared the details of how we chose to preprocess the data and how we created features from documents of interest
Unstructured text data is ubiquitous in both business and government and extracting value from it at scale is a common challenge. Organizations that have been around for a while often have vast paper archives. Digitizing these archives does not necessarily make them usable for search and analysis, since documents are
SAS Conversation Designer is available with every offering that also includes SAS Visual Analytics. Users can easily access Visual Text Analytics capabilities from SAS Conversation Designer with minimum additional configuration.
지난 텍스트 분석 시리즈 2편에서는 보험사의 데이터를 이용하여 예측 모델을 개발하고, 모델의 성능을 개선하여 고객 행동에 대한 예측도를 높이는 방법을 살펴봤습니다. 이번에는 영화 리뷰 데이터를 사용하여 분류 규칙을 개발하는 과정을 SAS Visual Text Analytics를 중심으로 알아보겠습니다. SAS Visual Text Analytics(이하, VTA)는 대용량의 비정형 데이터로부터 쉽게 인사이트를 추출할 수 있도록 설계된
지난 10월 21일부터 23일까지 이탈리아 밀라노에서 열린 'SAS 애널리틱스 익스피리언스 2019(SAS Analytics Experience 2019)'에서는 SAS의 머신러닝, 컴퓨터 비전, 자연어처리 등 AI 기술을 기반으로 기업들이 어떻게 실제(real) 가치를 실현할 수 있는지 보여주는 다양한 사례들이 소개되었습니다. 특히 행사 둘째 날에는 짐 굿나잇 SAS CEO, 올리버 샤벤버거 SAS 수석부회장 겸 최고운영책임자(COO) & 최고기술책임자(CTO)의