Matt Shearer

Data Language AI for News offers a new market-leading automated news classification SaaS. It beats other SaaS because it uses your own tags.

Data Language AI for News - Automated News Content Classification

Automatically Tag your text with News Tags
We’ve trained our Data Language Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] using a News
training corpus, and are making it available for you to try out.

Simply add your text, and get News tags back. Get the latest News tags,
consistently, and improve your user journeys and SEO.

Industry-leading Auto-tagging AI
Our sophisticated Text AI automated classification service
[https://datalanguage.com/products/datalanguageai/text-ai-services] is designed
with years of experience in Data Science. It beats popular benchmarks, and
tackles all the normal data science issues faced with News auto-tagging.

Get access
Simply use the contact form to request access.

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Data Language AI - News and AI Product Updates

Data Language AI

Data Language AI for News - Automated News Classification

Fabio Colasanti

(Update @ 2020: "Tagmatic" has been rebranded as the Text Classification service
in our "Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services]" product
suite).

It may be argued that customer satisfaction is the only and ultimate KPI that
really matters when judging the success of a product. In which case Tagmatic
would pass with flying colors, given the fantastic feedback we are receiving
from clients. 

Remarkably enough, we found that sheer inferencing performances are often at the
edges of our clients' considerations: this is partly due to the intrinsically
tailored nature of Tagmatic's models, but mostly attributable to a plethora of
invaluable qualities that go beyond the naked F1 scores: being able to quickly
spin up a model on your own content and vocabulary, and then interact with it
via a minimalistic HTTP interface, zero maintenance, on-the-fly learning of new
classes, low latency, security, scalability and high availability out of the
box, are crucial factors to enterprise-scale deliveries. 

Your Intellectual Property lies in your content, your data, your data models,
and how you use the classifications. It is not in commoditized compute
solutions.
Your resources are thus more effectively spent developing and exploiting your
market differentiating IP, rather than spending way too much time and money on
trying to replicate the engineering complexities of off-the-shelf MLaaS
solutions which may not be your technical team's core competence.

This is where Tagmatic comes in. The hard work and complex engineering has been
done, just bring your content!

The datasets
RCV1 is arguably the most popular dataset for multi-label classification,
comprising 800,000 newswires by Reuters tagged with 103 classes. Oddly enough
for such a totemic dataset, the breadth of the vocabulary is anachronistic when
compared to some of our clients, modern global publishers who routinely maintain
taxonomies with thousands of topics.
The dataset is available here
[http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/lyrl2004_rcv1v2_README.htm] 
although unfortunately not in its original XML format with full HTML markup,
which we would normally leverage as part of the classification task. Also the
corpus comes already tokenised and train/test datasets pre-canned. Besides
taking away part of the fun, this has also negative implications on the
vectorisation logic Tagmatic implements, making it largely ineffective
unfortunately. For the purpose of this benchmark, we have used the full train
set (23,000) and a reduced test set (100,000).

AAPD (Arxiv Academic Paper Dataset) is a collection of research papers covering
a number of subjects organised in several classification systems. The dataset 
here [https://www.kaggle.com/neelshah18/arxivdataset] is annotated with a number
of classification schemes, of varying level of quality. We have carved out a
subset corpus in the Computer Science domain, comprising of 32,000 papers and 39
subjects in total.

The benchmark
We have considered a number of models amongst the most widely employed for
multi-label classification tasks. The reference evaluation metric is
weighted-F1.

RCV1 AAPD One-vs-Rest - LinearSVC 0.78 0.71 One-vs-Rest - SGD 0.74 0.62 
One-vs-Rest - XGBoost 0.75 0.64 Classifier Chain - LinearSVC 0.79 0.71 Label
Powerset - MultinomialNB 0.55 0.55 Label Powerset - RandomForest 0.60 0.55 
Tagmatic 0.80 0.74 
Tagmatic offers out-of-the-box improvement over a number of basic
implementations. Granted, a team of Data Scientists might possibly come up with
better optimised flavours of such models, pushing performances up. But at what
cost? And by how much? And what then? There is still the much bigger task of
bringing that model to fruition in a real world environment, requiring
non-trivial development effort. Is that really worth it? 
Unless text classification is either your USP or a strong market differentiator,
then the answer ought to be a resounding no. 

Tagmatic is production-ready, enterprise-grade, and available to you right now -
see our Text AI Services
[https://datalanguage.com/products/datalanguageai/text-ai-services] product.

Lastly - Using Tagmatic (Text AI Services) on Hansard
We have also benchmarked Tagmatic against Hansard data: a corpus of ~200,000
parliamentary interventions for the 2005/6 period , classified with ~7200
categories, scoring a whopping 0.83. More on this on the next blog post. 
Make sure you sign up for our newsletter [ https://datalanguage.com/contact-us] 
and follow @datalanguage [https://twitter.com/datalanguage] on Twitter for all
the latest.

News and product updates about our Text AI Services. Read about how our Text AI products are being used in innovation projects, and by our clients.

Read the latest on our Text AI Services. Projects, Innovation initiatives, and new developments for AI features in specialized use cases.

News and Product Updates about our Text AI Services

Text AI Services

Benchmarking Tagmatic

Tolga Ergin

Our Text AI Services solve the common AI SaaS pain points: they are specialized to you, language agnostic, self-optimizing, and immediately scalable.

Text AI Services - Data Language AI

Our Text AI services use machine learning to make tagging, entity recognition,
auto classification and content recommendations both easy and efficient. 

They dramatically reduce the time, cost and complexity of organising your text
content. The data science, difficult engineering and scaling are all taken care
of.

Our Text AI classifiers and entity extractors are enterprise grade, state of the
art machine learning, delivering best-in-class capability.

SaaS Services
These SaaS services come with powerful APIs for immediate integration:

 1. AI Text Classification - classify your content using your taxonomies
 2. AI Entity Extraction - document level and inline
 3. AI Entity Relation Extraction - find relationships between entities inline
 4. AI Recommendations - fully automated content recommendations

Advantages
The real advantage with our Text AI services, is that they dynamically spin up
new models as they are needed, without the need for any data scientist. 

As fully managed SaaS products, our Text AI services are:

 1. Easy to integrate
 2. Language agnostic
 3. Use your own vocabularies
 4. Self-optimise on the fly, independent from your workflow
 5. Highly scalable, highly performant
 6. Automated on-the-fly training 
 7. Minimum setup time

Data Language AI for News - Automated News Classification

Automatically Tag your text with News Tags

Industry-leading Auto-tagging AI

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