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However, we are always keen to speak with potential candidates for various roles here at AYLIEN. If you’re interested in joining the team, we would love to hear from you. Please email your CV to

At AYLIEN we are using recent advances in Artificial Intelligence to try to understand natural language. Part of what we do is building products such as our Text Analysis API and News API to help people extract meaning and insight from text. We are also a research lab, conducting research that we believe will make valuable contributions to the field of Artificial Intelligence, as well as driving further product development (see this post about a recent publication on aspect-based sentiment analysis by one of our research scientists for example).

We are excited to announce that we are currently accepting applications from students and researchers for funded PhD and Masters opportunities, as part of the Irish Research Council Employment Based Programme.

The Employment Based Programme (EBP) enables students to complete their PhD or Masters degree while working with us here at AYLIEN.

For students and researchers, we feel that this is a great opportunity to work in industry with a team of talented scientists and engineers, and with the resources and infrastructure to support your work.

About us

We’re an award-winning VC-backed text analysis company specialising in cutting-edge AI, deep learning and natural language processing research to offer developers and solution builders a package of APIs that bring intelligent analysis to a wide range of apps and processes, helping them make sense of large volumes of unstructured data and content.

With thousands of users worldwide and a growing customer base that includes great companies such as Sony, Complex Media, Getty Images, and McKinsey, we’re growing fast and enjoy working as part of a diverse and super smart team here at our office in Dublin, Ireland.

You can learn more about AYLIEN, who we are and what we do, by checking out our blog and two of our core offerings – our Text Analysis API and News API.

About the IRC Employment Based Programme

The Irish Research Council’s Employment Based Programme (EBP) is a unique national initiative, providing students with an opportunity to work in a co-educational environment involving a higher education institution and an employment partner.

The EBP provides a co-educational opportunity for researchers as they will be employed directly by AYLIEN, while also being full time students working on their research degree. One of the key benefits of such an arrangement is that you will be given a chance to see your academic outputs being transferred into a practical setting. This immersive aspect of the programme will enable you to work with some really bright minds who can help you generate research ideas and bring benefits to your work that may otherwise not have come to light under a traditional academic Masters of PhD route.


The Scholarship funding consists of €24,000pa towards salary and a maximum of €8,000pa for tuition, travel and equipment expenses. Depending on candidates’ level of seniority and expertise, the salary amount may be increased.

Our experience with the EBP

AYLIEN is proud to host and work with two successful programme awardees under the EBP, Sebastian Ruder and Peiman Barnaghi. Both Sebastian and Peiman have been working under the supervision of Dr. John Breslin, who is an AYLIEN advisor and lecturer at NUI Galway and Insight Center. We also have academic ties with University College Dublin (UCD) through Barry Smyth. Barry is a Full Professor and Digital Chair of Computer Science at UCD, and recently joined the team at AYLIEN as an advisor.

Screen Shot 2016-11-02 at 14.59.33Back row, left to right: Peiman and Sebastian with Parsa Ghaffari, AYLIEN Founder & CEO

Sebastian Ruder

Throughout his research, Sebastian has developed language and domain-agnostic Deep Learning-based models for sentiment analysis and aspect-based sentiment analysis that have been published at conferences and are used in production. His main research focus is to develop efficient methods to enable models to learn from each other and to equip them with the capability to adapt to new domains and languages.

The Employment Based Programme for me brings academia and industry together in the best possible way: It enables me to immerse myself and get to the bottom of hard problems; at the same time, I am able to collaborate with driven and inspiring individuals at AYLIEN. I find this immersion of research-oriented people like myself sitting next to people that are hands-on with diverse technical backgrounds very compelling. This stimulating and fast-paced working environment provides me with direction and focus for my research, while the ‘get stuff done’ mentality allows me to concentrate and accomplish meaningful things” – Sebastian Ruder, Research Scientist at AYLIEN

Here are some of Sebastian’s recent publications:

  • INSIGHT-1 at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and Quantification (arXiv)
  • INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis (arXiv)
  • A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis (arXiv)
  • Towards a continuous modeling of natural language domains (arXiv

Peiman Barnaghi

Peiman’s research, in collaboration with the Insight Centre for Data Analytics, NUI Galway, focuses on Scalable Topic-level Sentiment Analysis on Streaming Feeds. His main focus is working on Twitter data for Sentiment Analysis using Machine Learning and Deep Learning methods for detecting polarity trends toward a topic on a large set of tweets and determining the degree of polarity.

Here are some of Peiman’s recent publications:

  • Opinion Mining and Sentiment Polarity on Twitter and Correlation between Events and Sentiment (link)
  • Text Analysis and Sentiment Polarity on FIFA World Cup 2014 Tweets (PDF)

You can read more about our experience with the EBP in the Irish Research Council’s Annual Report (pages 29 & 31)

Details & requirements

First and foremost, your thesis topic must be something you are passionate about. While prior experience with the topic is important, it is not crucial. We can work with you to establish a suitable topic that overlaps with both the supervisor’s general area of interest/research and our own research and product directions.

Suggested read: Survival Guide to a PhD by Andrej Karpathy

We are particularly interested in applicants with interests in the following areas (but are open to other suggestions):

  • Representation Learning
  • Domain Adaptation and Transfer Learning
  • Sentiment Analysis
  • Question Answering
  • Dialogue Systems
  • Entity and Relation Extraction
  • Topic Modeling
  • Document Classification
  • Taxonomy Inference
  • Document Summarization
  • Machine Translation

You have the option to complete a Masters (1 year, or 2 years if structured) or a PhD (3 years, or 4 years if structured) degree.

AYLIEN will co-fund your scholarship and provide you with professional guidance and mentoring throughout the programme. It is a prerequisite that you spend 50-70% of your time based on site with us and the remainder of the time at your higher educational institute (HEI).

Open to students with a bachelor’s degree or higher (worldwide) and you will ideally be based within a commutable distance of our office in Dublin City Centre.


It would be ideal if you have already identified or engaged with a potential supervisor at a university in Ireland. However, if not, we will help you with finding a suitable supervisor.

Important dates and deadlines

Please note: all times stated are Ireland time and are estimates based on last years programme. Full details will be released in December.

Call open: 6 December 2016

FAQ Deadline: 8 February 2017 (16:00)

Applicant Deadline: 15 February 2017 (16:00)

Supervisor, Employment Mentor and Referee Deadline: 22 February 2017 (16:00)

Research Office Endorsement Deadline: 1 March 2017 (16:00)

Outcome of Scheme: 26 May 2017

Scholarship Start Date: 1 October 2017

How to apply

To express your interest, please forward your CV and accompanying note with topic suggestions to


August 2014 will forever be a significant month in the lifetime of AYLIEN. We’ve doubled our team, upgraded our home and pimped our office swag – see new shiny “AYLIEN” tees!

The good people of Regus House on Harcourt Street gave us shelter for little over a year when we first came to Dublin and now we’ve decided to flee the serviced office nest to fend for ourselves.

Our new home is the 4th floor in Equity House, on Ormond Quay, Dublin 7. We’re delighted with our new space. We have a wrap around balcony overlooking the Liffey and the Four Courts, loads of natural light thanks to our huge Georgian windows, and our porter ‘Jerry’ is a bit of an auld legend.



The coolest thing about our new space is the size of it. This is of paramount importance to us. We’ve 3 new team members on board and plenty of room to hire more.

Our first weekend as “AYLIEN of Equity House” was spent in our new den, giving it some TLC. Our office space is awesome but we wanted it to become “AYLIEN awesome”. So paint brushes in hand and 5 litres of green Dulux at the ready, we got to work.



Fast forward 48 hours and 2 coats of paint and we have ourselves an “AYLIEN awesome” office.

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One of the coolest things about analysing text is, it’s everywhere! Irrespective of industry, companies & individuals want to make better informed business decisions based off trackable and measurable insight. With advancements in Text Analysis, companies can now mine text to uncover insights and improve their service or offering to prosper in their market.

So far at AYLIEN, our Text Analysis API has had great success in the news and media space. But this is just the tip of the Text Analytics iceberg. There are countless numbers of other industries that can gain the same value from such insights. As we don’t have a countless amount of time, let’s stick with a Top 10 list of use cases for Text Analytics.

1. Sports trading – One of the most popular sports to bet on, particularly in Europe, is football (soccer). The top sports traders gather data from the mainstream media and have a deep understanding of the game and it’s politics at a local level. If you live in England and you bet on English football, irrespective of the division, it’s relatively easy to understand your market. You can successfully bet on a local second division English team because you speak the language, read the local newspapers and may even follow some of the team members on Twitter. But what if you’d like to do the same for a similar team in Spain and you don’t speak a word of Spanish? A Text Analysis API capable of understanding Spanish would allow you to extract meaning from local Twitter feeds, giving you insights into what the local fans are saying about their team. These people understand the squad dynamics at a local level. If, for example, the star striker of Real Club Deportivo Mallorca has an argument with his wife the night before his cup game, is he as likely to be the top scorer on match day?

2. Financial Trading – As with sports trading, having an insight into what is happening at a local level can be very valuable to a financial trader. Domain-specific sentiment analysis/classification can add real value here. The same way in which fans have their own distinct vocab based on the sport, so too do traders in particular markets. Intent recognition and Spoken Language Understanding services for detecting user intents (e.g. “buy”, “sell”, etc) from short utterances can help to guide traders in deciding what to trade, how much and how quickly.

3. Voice of the customer (VOC) – VOC applications are primarily used by companies to determine what a customer is saying about a product or service. Sources of such data include emails, surveys, call center logs and social media streams like blogs, tweets, forum posts, newsfeeds, and so on. For example, a telecom company could use voice of customer text analysis to scan Twitter for customer gripes about their broadband internet services. This would would give them an early warning when customers were annoyed with the performance of the service and allow them to intercept the issue before it involved the customer calling to officially complain or request contract cancellation.

4. Fraud – Whether it’s workers claiming false compensation or a motorist disclosing a false home address, fraudulent activity can be discovered much more quickly when those investigating can join the dots together, faster. In the latter case, for example, the guilty party may give an address that has many claims associated with it or the driven vehicle may have been involved in other claims. Having the ability to capture this information saves the insurer time and gives them greater insight into the case.

5. Manufacturing or warranty analysis – In this use case, companies examine the text that comes from warranty claims, dealer technician lines, report orders, customer relations text, and other potential information using text analytics to extract certain entities or concepts (like the engine or a certain part). They can then analyze this information, looking at how the entities cluster and to see if the clusters are increasing in size and whether they are a cause for concern, for example.

6. Customer service routing – In this use case, companies can use text analytics to route requests to customer service representatives. For example, say you’ve sent an email to a company while on hold to one of their reps. You might have a question or a complaint about one of their products. The company can use text analytics for intelligent routing of that email to the appropriate person at the company. This could also be possible in a call center situation, provided you have sufficiently accurate speech-to-text software.

7. Lead generation – As was the case with the VOC application, taking timely action on a piece of Social Media information can be used to both retain and gain new customers. For example, if a person tweets that they are interested in a certain product or service, text analytics can discover this & feed this info to a sales rep who can then pursue this prospect and convert them into a customer.

8. TV advertising & audience analysis – TV shows or live televised events are some of the most talked-about topics on Twitter. Marketers and TV producers can both benefit from using Text Analytics in two distinct ways. If producers can get an understanding of how their audience ‘feels’ about certain characters, settings, storylines, featured music etc they can make adjustments in a bid to appease their viewers and therefore increase the audience size and viewers ratings. Marketers can dig in to social media streams to analyse the effectiveness of product placement and commercials aired during the breaks. For example, the TV character ‘Cersei’ from Game of Thrones is becoming a fashion icon amongst fans, who regularly Tweet about her latest frock. High street retailers that want to take advantage of this trend could release a line of ‘Queen of Westeros’ style clothing and align their commercials with shows like Game of Thrones. Text Analytics could also be used by TV Executives looking to sell to advertisers. For example, a TV company could mine viewers tweets & forum activity to profile their audience more accurately. So instead of merely pitching the size of their audience to advertisers, they could wow them by identifying their gender, location, age etc and their feelings towards certain products.

9. Recruitment – Text Analysis could be used in both the search and selection phases of recruitment. The most basic application would be identifying the skills of a potential hire. In the recruitment industry, the real value comes from identifying prospects before they become active on the job market. For example, it would be very powerful to know if somebody tweets about disliking their job or expresses an interest in working in a different field, larger/smaller company, different location etc. Once you have identified such a prospect, you could use Text Analytics to analyse the suitability of this person based on what others say about them. Mining news and blog articles, forum postings and other sources could help to evaluate potential hires.

10. Review Sites – Companies like Expedia have millions of reviews on their website, from travellers all over the world. Given the nature of the site and the fact that their users are looking for a stress free experience, having to sift through hundreds of reviews to find a place to stay can be a real turn off. Text Analysis can be used here to build tools that can summarize multiple properties in 2-3 word phrases. Instead of scrolling through a list of hotel features like heated pool, massage therapy, buffet breakfast etc, you could simply say “Luxurious Hotel and Spa”.

Did you like our top 10 use cases? If you work in an industry that’s not mentioned above and have an idea of how Text Analytics could help you, please let us know!

Subscribe to our blog and keep an eye out for our next post on how Text Analytics can add value to your business.

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Position: advisor

We are delighted to announce that Dr. John Breslin has joined AYLIEN as an advisor. John has a unique blend of academia and industry in his background and he’s currently a lecturer at NUI Galway and Insight Centre.

He previously co-founded the very successful and websites as well as StreamGlider, Technology Voice and Startup Galway. He has also lead Eurapp and SIOC.

Happy to have you on board, John and looking forward to making great things happen with your help and advice!