The Namkin company and Loria - Université de Lorraine invites applications for a postdoctoral position on business event extraction.
Location: Troyes, France and Nancy, France
Application Deadline: 31st January 2024
Starting Date: March 2024
Contract Duration: 1 year (with possible extension)
The industry faces numerous challenges that necessitate the evolution of BtoB
marketing tools, in order to develop a valuable offer and provide an enhanced
customer experience. Namkin's BrainLab develops industrial marketing tools for
digitalizing customer relations, evolving business models, and exploiting
business and economic data for business development. One of the key challenges
of marketing intelligence is to identify risks and opportunities so as to guide
marketing strategies. Among the sources of information useful to detect risks
and opportunities, Namkin has identified Business Events, that is, “textually
reported real-world occurrences, actions, relations, and situations involving
companies and firms” (Jacobs et al., 2018).
The Loria Semagram team specialises in modelling natural language semantics to
represent discourse. While modern semantic representations may contain vast
quantities of information, they do not always (or necessarily) contain the
information that is useful for the concrete application. For instance,
significant challenges still persist in dealing with temporal relations and
finely-grained negation interpretation.
A number of studies at the crossroads of business intelligence and NLP have
focused on the detection or extraction of Business Events (e.g., Arendarenko &
Kakkonen, 2012; Han et al., 2018; Jacobs et al., 2018; Jacobs & Hoste, 2020;
Jacobs & Hoste, 2022). Despite the richness of the event extraction literature,
many challenges still remain. Some of these challenges are concerned with the
modelling of the task itself, such as the necessity / benefit of trigger
identification for event extraction (see Zhu et al. 2021), some with the scope
of the task, such as sentence level vs document level extraction (e.g., Zheng
et al. 2019), some with the information necessary to the integration of events
in a coherent knowledge base, like factuality detection (e.g., Zhang et al.,
2022) and event disambiguation (e.g., Barhom et al., 2019).
Recent research has looked into the benefits of exploiting semantic
representations, and in particular Abstract Meaning Representation (AMR;
Banarescu et al. 2013), for low-resources scenarios (Huang et al., 2018) and
document level event argument extraction (e.g., Xu et al., 2022). However, it
appears that AMR has to be adapted in order to optimally support event
extraction related tasks (Yang et al., 2023). One major limitation of AMR for
document-level event extraction is that AMR works at the sentence level, and
thus requires the aggregation of sentence-level representations. AMR is also
limited in terms of negation and universal quantification expressive power.
To overcome these issues, we seek to appoint a Postdoctoral Researcher to work
on semantic modelling. Some promising new lead was recently provided by Bos
(2023) who proposes a new meaning representation system that overcomes
expressive power limitations, supports discourse relations and inter-sentential
coreferences, and reduces the annotation load. The appointed Postdoctoral
Researcher will explore semantic modelling solutions and their application to
event extraction in the field of business.
The topic covers various subjects, including:
- Computational semantics,
- Machine learning with neural networks,
- Cross-domain model transfer,
- Learning from small data,
- Combining top-down (expert-driven) and bottom-up (dataset-driven) models,
- Design of meaning representations
- Shallow and deep semantic processing and reasoning
- Hybrid symbolic and statistical approaches to semantics
- Neural semantic parsing
- Semantics and ontologies
The successful candidate will be part of Namkin's Data & IA team and the
Sémagramme Team at Loria, with co-supervision provided by Agata Marcante and
Professor Maxime Amblard.
As part of the role, you will have the opportunity to...
- Design, develop and test semantic representation algorithms for text-mining
with the aim of identifying significant information in unstructured text.
- Collaborate with Namkin’s experts to evaluate the algorithms on real-world
use cases.
You will be responsible for writing academic papers, technical reports and
project deliverables. You will also attend academic conferences or project
meetings to present your findings and act as a representative for the team.
Requirements include expertise in semantic representation algorithms, excellent
technical writing skills and the ability to work well in a team.
* Applicants must hold a PhD in Computer Science, related to Data Systems,
Natural Language Processing, or Artificial Intelligence.
* They should have proven fluency in at least one programming language, such as
Python, R, Java or C++.
* Candidates must possess a curious and passionate attitude towards research
and learning in general.
* Proficiency in French language would be considered a bonus.
* Previous experience in the NLP field would be considered advantageous.
How to apply:
send an email to:
applicati...@namkin.fr <mailto:applicati...@namkin.fr>
- with the subject starting with ''Namkin-Loria Postdoc''
- with a single PDF attached containing:
* Cover letter detailing motivation and qualifications for this position.
* Curriculum vitae, with a list of publications and contact details for
references.
Interested parties are encouraged to contact us for further information
regarding the position before applying.
References
Arendarenko, E., & Kakkonen, T. (2012). Ontology-based information and event
extraction for business intelligence. In Artificial Intelligence: Methodology,
Systems, and Applications: 15th International Conference, AIMSA 2012, Varna,
Bulgaria, September 12-15, 2012. Proceedings 15 (pp. 89-102). Springer Berlin
Heidelberg.
Barhom, S., Shwartz, V., Eirew, A., Bugert, M., Reimers, N., & Dagan, I.
(2019). Revisiting joint modeling of cross-document entity and event
coreference resolution. arXiv preprint arXiv:1906.01753.
Banarescu, L., Bonial, C., Cai, S., Georgescu, M., Griffitt, K., Hermjakob, U.,
... & Schneider, N. (2013, August). Abstract meaning representation for
sembanking. In Proceedings of the 7th linguistic annotation workshop and
interoperability with discourse (pp. 178-186).
Jacobs, G., & Hoste, V. (2020). Extracting fine-grained economic events from
business news. In COLING 2020 (pp. 235-245). COLING.
Jacobs, G., & Hoste, V. (2022). SENTiVENT: enabling supervised information
extraction of company-specific events in economic and financial news. Language
Resources and Evaluation, 56(1), 225-257.
Jacobs, G., Lefever, E., & Hoste, V. (2018). Economic event detection in
company-specific news text. In 1st Workshop on Economics and Natural Language
Processing (ECONLP) at Meeting of the Association-for-Computational-Linguistics
(ACL) (pp. 1-10). Association for Computational Linguistics (ACL).
Han, S., Hao, X., & Huang, H. (2018). An event-extraction approach for business
analysis from online Chinese news. Electronic Commerce Research and
Applications, 28, 244-260.
Huang, L., Ji, H., Cho, K., Dagan, I., Riedel, S., & Voss, C. (2018, July).
Zero-Shot Transfer Learning for Event Extraction. In Proceedings of the 56th
Annual Meeting of the Association for Computational Linguistics (Volume 1: Long
Papers) (pp. 2160-2170).
Xu, R., Wang, P., Liu, T., Zeng, S., Chang, B., & Sui, Z. (2022). A two-stream
AMR-enhanced model for document-level event argument extraction. arXiv preprint
arXiv:2205.00241.
Yang, Y., Guo, Q., Hu, X., Zhang, Y., Qiu, X., & Zhang, Z. (2023). An AMR-based
link prediction approach for document-level event argument extraction. arXiv
preprint arXiv:2305.19162.
Zhang, H., Qian, Z., Li, P., & Zhu, X. (2022, November). Evidence-Based
Document-Level Event Factuality Identification. In PRICAI 2022: Trends in
Artificial Intelligence: 19th Pacific Rim International Conference on
Artificial Intelligence, PRICAI 2022, Shanghai, China, November 10–13, 2022,
Proceedings, Part II (pp. 240-254). Cham: Springer Nature Switzerland.
Zheng, S., Cao, W., Xu, W., & Bian, J. (2019). Doc2EDAG: An end-to-end
document-level framework for Chinese financial event extraction. arXiv preprint
arXiv:1904.07535.
Zhu, T., Qu, X., Chen, W., Wang, Z., Huai, B., Yuan, N. J., & Zhang, M. (2021).
Efficient document-level event extraction via pseudo-trigger-aware pruned
complete graph. arXiv preprint arXiv:2112.06013.
----------------------
Maxime Amblard
Université de Lorraine
https://members.loria.fr/mamblard <https://members.loria.fr/mamblard>
http://espoir-ul.fr <http://espoir-ul.fr/>
smime.p7s
Description: S/MIME cryptographic signature
_______________________________________________ Corpora mailing list -- corpora@list.elra.info https://list.elra.info/mailman3/postorius/lists/corpora.list.elra.info/ To unsubscribe send an email to corpora-le...@list.elra.info
