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Lexalytics Celebrates Its Anniversary: 20 Years of NLP Innovation

Lexalytics

We’ve pioneered a number of industry firsts, including the first commercial sentiment analysis engine, the first Twitter/microblog-specific text analytics in 2010, the first semantic understanding based on Wikipedia in 2011, and the first unsupervised machine learning model for syntax analysis in 2014.

NLP 98
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Pre-processing temporal data made easier with TensorFlow Decision Forests and Temporian

TensorFlow

The dataset is stored in a single CSV file, with one transaction per line: $ head -n 5 sales.csv timestamp,client,product,price 2010 - 10 -05 11 :09: 56 ,c64,p35, 405.35 2010 -09- 27 15 : 00 : 49 ,c87,p29, 605.35 2010 -09-09 12 : 58 : 33 ,c97,p10, 108.99 2010 -09-06 12 : 43 : 45 ,c60,p85, 443.35

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SPECTER2: Adapting Scientific Document Embeddings to Multiple Fields and Task Formats

Allen AI

Transformer models like BERT , which are pre-trained on large quantities of text, are the go-to approach these days for embedding text in a semantic space. These vectors are then used either to find similar documents or as features in a computationally cheap model. A variety of such embedding models are available for users to choose from.

BERT 98
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NLP-Powered Data Extraction for SLRs and Meta-Analyses

Towards AI

BioBERT and similar BERT-based NER models are trained and fine-tuned using a biomedical corpus (or dataset) such as NCBI Disease, BC5CDR, or Species-800. Data formats for inputting data into NER models typically include Pandas DataFrame or text files in CoNLL format (ie. a text file with one word per line). This study by Bui et al.

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Dude, Where’s My Neural Net? An Informal and Slightly Personal History

Lexalytics

Ignore the plateau around 2010: this is probably an artifact of the incompleteness of the MAG dump.) The base model of BERT [ 103 ] had 12 (!) And what’s more, Google made BERT publicly available, so that everyone could have access to contextual word vectors. BERT is just too good not to use.

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Rising Tide Rents and Robber Baron Rents

O'Reilly Media

Google and Amazon were still atop their respective hills of web search and ecommerce in 2010, and Meta’s growth was still accelerating, but it was hard to miss that internet growth had begun to slow. It was certainly obvious to outsiders how disruptive BERT could be to Google Search. The market was maturing.

BERT 109
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Multi-domain Multilingual Question Answering

Sebastian Ruder

Reading Comprehension assumes a gold paragraph is provided Standard approaches for reading comprehension build on pre-trained models such as BERT. Using BERT for reading comprehension involves fine-tuning it to predict a) whether a question is answerable and b) whether each token is the start and end of an answer span.

BERT 52