Job Type : Internship  
Start date : November 2019 or otherwise August2020
Duration : 5-6 months

Description

Aspect-based sentiment analysis (ABSA) is the task of identifying
fine-grained opinion polarity towards specific aspects associated with
a given target.

The goal of this internship is exploring and implementing a new neural
architecture for end-to-end ABSA, where term detection, aspect and
polarity classification are jointly modeled. Special attention will be
given to data-efficient methods in order to cope with situations where
little amount of annotated data is available. Experiments will be
conducted and evaluated on multiple datasets from various domains and
languages and results will be evaluated not only on specific ABSA
subtasks but also on the full chain of annotations.

The expected outcome is, a minima, a multilingual prototype working on
different domains.

The successful candidate should be enrolled in a graduate program, at
the Master or PhD level, with a focus on NLP and Deep Learning.

Publication of results in major conferences/journals will be strongly
encouraged.

Required skills

Good programming skills and proficiency with TensorFlow or PyTorch

References

Task: SemEval2016 Task 5: http://alt.qcri.org/semeval2016/task5/

[1] Pontiki, Maria, Dimitris Galanis, Haris Papageorgiou, Ion
Androutsopoulos, Suresh Manandhar, Mohammed AL-Smadi, Mahmoud
Al-Ayyoub, et al. 2016. "SemEval-2016 Task 5 : Aspect Based Sentiment
Analysis." In Proceedings of the 10th International Workshop on
Semantic Evaluation (SemEval-2016), 19-30. Association for
Computational Linguistics.

[2] Bailin, Wang and Lu, Wei. "Learning Latent Opinions for
Aspect-level Sentiment Classification". Proceedings of AAAI 2018, New
Orleans.

[3] Chi Sun, Luyao Huang, Xipeng Qiu. "Utilizing BERT for Aspect-Based
Sentiment Analysis via Constructing Auxiliary Sentence". Proceedings
of NAACL-HLT 2019, pages 380-385 Minneapolis, Minnesota, June 2 - June
7, 2019.

Application instructions

Please note that applicants must be registered students at a
university or other academic institution and that this establishment
will need to sign an 'Internship Convention' with NAVER LABS Europe
before the student is accepted.

You can apply for this position online. Don't forget to upload your CV
and cover letter before you submit. Incomplete applications will not
be accepted.

About NAVER LABS

NAVER LABS is a world class team of self-motivated and highly engaged
researchers, engineers and interface designers collaborating together
to create next generation ambient intelligence technology and services
that are rich with the organic understanding they have of users, their
contexts and situations.

Since 2013 LABS has led NAVER's innovation in technology through
products such as the AI-based translation app `Papago', the
omni-tasking web browser `Whale', the virtual AI assistant `WAVE',
in-vehicle information entertainment system `AWAY' and M1, the 3D
indoor mapping robot.

The team in Europe is multidisciplinary and extremely multicultural
specializing in artificial intelligence, machine learning, computer
vision, natural language processing, UX and ethnography. We
collaborate with many partners in the European scientific community on
R&D projects.

NAVER LABS Europe is located in the south east of France in
Grenoble. The notoriety of Grenoble comes from its exceptional natural
environment and scientific ecosystem with 21,000 jobs in public and
private research. It is home to 1 of the 4 French national institutes
in AI called MIAI (Multidisciplinary Innovation in Ai) It has a large
student community (over 62,000 students) and is a lively and
cosmopolitan place, offering a host of leisure opportunities. Grenoble
is close to both the Swiss and Italian borders and is the ideal place
for skiing, hiking, climbing, hang gliding and all types of mountain
sports.

Apply Online

https://europe.naverlabs.com/job/end-to-end-aspect-based-sentiment-analysis/