Category Archives: Content Industry

Posts about the content industry

Books Are a Service

Semantic Publishing and Voice of the Customer understanding for the media&content industry

The reason for publishing being a key industry to take advantage of text analytics is also the reason why the industry finds it so hard to engage with the technology.

Books are a serviceThe reason? Text. And a lot of it. The publishing world has struggled to understand how data relates to text and understand the value of data. This is changing, too slow for many, as the industry moves from seeing themselves as a ‘product’ based company (e.g. making books, e-books or physical) to a ‘service’ based company. In other words smart publishers are starting to see their service to customers as the creator and curator of information. This content is abled to be mixed and mashed-up in dynamic ways across a number of formats. This service is not bound, saddle-stitch or otherwise, to a specific product. This 180-degree perspective change requires publishers to think more directly about customer experience in the same way more traditional service based industries like hospitality or even retail banking.

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The Analysis of Customer Experience, Touchstone in the Evolution of the Market of Language Technologies

The LT-Innovate 2014 Conference has just been held in Brussels. LT-Innovate is a forum and association of European companies in the sector of language technologies. To get an idea of the meaning and the importance of this market, suffice it to say that in Europe some 450 companies (mainly innovative SMEs) are part of it, and are responsible for 0.12% of European GDP. Daedalus is one of the fifteen European companies (and the only one from Spain) formally members of LT-Innovate Ltd. since its formation as an association, with headquarters in the United Kingdom, in 2012.


LT-Innovate Innovation Manifesto 2014

In this 2014 edition, the document “LT-Innovate Innovation Manifesto:” Unleashing the Promise of the Language Technology Industry for a Language-Neutral Digital Single Market” has been published. I had the honor of being part of the round table which opened the conference. The main subject of my speech was the qualitative change experienced in recent times by the role of our technologies in the markets in which we operate. For years we have been incorporating our systems to solve in very limited areas the specific problems of our more or less visionary or innovative customers. This situation has already changed completely: language technologies now play a central role in a growing number of businesses.

Language Technologies in the Media Sector

In a recent post, I referred to this same issue with regard to the media sector. If before we would incorporate a solution to automate the annotation of file contents, now we deploy solutions that affect most aspects of the publishing business: we tag semantically pieces of news to improve the search experience on any channel (web, mobile, tablets), to recommend related content or additional one according to the interest profile of a specific reader, to facilitate findability and indexing by search engines (SEO, Search Engine Optimization), to place advertising related to the news context or the reader’s intention, to help monetize content in new forms, etc.

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Semantic Publishing: a Case Study for the Media Industry

Semantic Publishing at Unidad Editorial: a Client Case Study in the Media Industry 

Last year, the Spanish media group Unidad Editorial deployed a new CMS developed in-house for its integrated newsroom. Unidad Editorial is a subsidiary of the Italian RCS MediaGroup, and publishes some of the newspapers and magazines with highest circulation in Spain, besides owning nation-wide radio stations and a license of DTTV incorporating four TV channels.

Newsroom El Mundo

Newsroom El Mundo

When a journalist adds a piece of news to the system, its content has to be tagged, which constitutes one of the first steps in a workflow that will end with the delivery of this item in different formats, through different channels (print, web, tablet and mobile apps) and for different mastheads. After evaluation of different provider’s solutions in the previous months, the company then decided that semantic tagging would be done through Daedalus’ text analytics technology. Semantic publishing included, in this case, the identification (with disambiguation) of named entities (people, places, organizations, etc.), time and money expressions, concepts, classification according to the IPTC scheme (an international standard for the media industry, with around 1400 classes organized in three levels), sentiment analysis, etc.

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Our new Semantic Publishing API is now available in MeaningCloud

This API allows you to produce and publish more valuable contents, more quickly and at lower costs

At MeaningCloud we keep developing our roadmap and offering new vertical APIs, optimized for different industries and applications. We are pleased to announce that our Semantic Publishing solutions include a new API, designed especially for media, publishers and content providers in general.

It is a logical step for us, since at S|ngular Meaning (our parent company) we have been collaborating for years with the most significant enterprises in these industries (PRISA, Unidad Editorial, Vocento, RTVE,, etc.) and this is one of the markets where we are detecting more demand and where our solutions are gaining more traction.

The Semantic Publishing API incorporates the know-how we have been developing when working with these large companies and packages it in the form of semantic resources, process pipelines and specific configurations for the most common applications and scenarios of this industry: archive management, content generation, customization of information products, etc.

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Recognizing entities in a text: not as easy as you might think!

Entities recognition: the engineering problem

As in every engineering endeavor, when you face the problem of automating the identification of entities (proper names: people, places, organizations, etc.) mentioned in a particular text, you should look for the right balance between quality (in terms of precision and recall) and cost from the perspective of your goals. You may be tempted to compile a simple list of such entities and apply simple but straightforward pattern matching techniques to identify a predefined set of entities appearing “literally” in a particular piece of news, in a tweet or in a (transcribed) phone call. If this solution is enough for your purposes (you can achieve high precision at the cost of a low recall), it is clear that quality was not among your priorities. However… What if you can add a bit of excellence to your solution without technological burden for… free? If you are interested in this proposition, skip the following detailed technological discussion and go directly to the final section by clicking here.

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Semantic Analysis and Big Data to understand Social TV

We recently participated in the Big Data Spain conference with a talk entitled “Real time semantic search engine for social TV streams”. This talk describes our ongoing experiments on social media analytics and combines our most recent developments on using semantic analysis on social networks and dealing with real-time streams of data.

Social TV, which exploded with the use of social networks while watching TV programs is a growing and exciting phenomenon. Twitter reported that more than a third of their firehose in the primetime is discussing TV (at least in the UK) while Facebook claimed 5 times more comments behind his private wall. Recently Facebook also started to offer hashtags and the Keywords Insight API for selected partners as a mean to offer aggregated statistics on Social TV conversations inside the wall.

As more users have turned into social networks to comment with friends and other viewers, broadcasters have looked into ways to be part of the conversation. They use official hashtags, let actors and anchors to tweet live and even start to offer companion apps with social share functionalities.

While the concept of socializing around TV is not new, the possibility to measure and distill the information around these interactions opens up brand new possibilities for users, broadcasters and brands alike.  Interest of users already fueled Social TV as it fulfills their need to start conversations with friends, other viewers and the aired program. Chatter around TV programs may help to recommend other programs or to serve contextually relevant information about actors, characters or whatever appears in TV.  Moreover, better ways to access and organize public conversations will drive new users into a TV program and engage current ones.

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