JHU/APL only attempted the NER and Entity Matching subtasks. We employed a statistical tagger called SVMLattice [1], with NER labels inferred by projecting English tags across bitext. The Illinois tagger [2] was used for English. A rule-based entity clusterer called "kripke" was used for Entity Matching [3].

[1] James Mayfield, Paul McNamee, Christine Piatko, and Claudia Pearce, Lattice-based Tagging Using Support Vector Machines. Proceedings of the Twelfth International ACM Conference on Information and Knowledge Management (CIKM 2003), pp. 303-308, November 2003.

[2] Lev Ratinov and Dan Roth. 2009. Design challenges and misconceptions in named entity recognition. In Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL '09). Association for Computational Linguistics, Stroudsburg, PA, USA, 147-155.

[3] Paul McNamee, Tim Finin, Dawn Lawrie, and James Mayfield, HLTCOE Participation at TAC 2013. Proceedings of the Text Analysis Conference, Gaithersburg, Maryland, 18-19 November, 2013.

Liner2 is a generic framework which can be used to solve various tasks based on sequence labeling, i.e. recognition of named entities, temporal expressions, mentions of events. It provides a set of modules (based on statistical models, dictionaries, rules and heuristics) which recognize and annotate certain types of phrases. The framework was already used for recognition of named entities (different levels of granularity), temporal expressions and event mentions for Polish.

Runs only for Polish at the moment.

LexiFlexi applies 3 lexico-semantic resources on input text in the following order: (a) match names from JRC Variant Names database [1] (circa 4,05 mln entries) and use the cross-lingual entity IDs therefrom, (b) match names from a huge collection (circa 6,82 mln entries) of multi-word named entities semi-automatically derrived from BabelNet on uncomsumed text using the method described in [2], and (c) match toponyms from the GeoNames gazetteer (circa 1,36 mln entries - only populated places) in unconsumed part of the texts and exploit cross-lingual IDs therefrom. Finally some language-independent heuristics are applied to match variants (abbreviated forms) of the named mentions of entities that were recognised using the the aforementioned lexical resources.

[1] Maud Ehrmann, Guillaume Jacquet and Ralf Steinberger, JRC-Names: Multilingual entity name variants and titles as Linked Data.. In Semantic Web Journal, Volume 8(2), pages 283-295, 2017.

[2] Sophie Chesney, Guillaume Jacquet, Ralf Steinberger and Jakub Piskorski, Multi-word Entity Classification in a Highly Multilingual Environment.. Proceedings of the 13-th Workshop on Multiword Expressions (MWE 2017). Held at EACL 2017, Valencia, Spain, 4 April 2017.

This is an almost “out-of-the-box” baseline.

Serge Sharoff's system is an example of the Language Adaptation method [1] applied to the NER detection subtask. A multilingual word embedding space for all Slavonic languages in the task has been created using the model by Dinu et al [2] with the addition of Weighted-Levenshtein distance [3]. This space was used for training a Neural Network NER tagger based on the architecture presented in [4] using a Slovenian NER corpus [5] and applying the model to other languages in the shared task.

[1] Serge Sharoff, 2017. Toward Pan-Slavic NLP: Some Experiments with Language Adaptation. Proc. BSNLP 2017.
[2] Georgiana Dinu, Angeliki Lazaridou and Marco Baroni, 2015 Improving Zero-shot Learning by Mitigating the Hubness Problem. Proc. ICLR 2015.
[3] Miguel Rios, Serge Sharoff, 2015. Obtaining SMT dictionaries for related languages. Proc. BUCC 2015.
[4] Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, Chris Dyer, 2016. Neural Architectures for Named Entity Recognition, Proc NAACL 2016.
[5] Simon Krek, Tomaž Erjavec, Kaja Dobrovoljc, Nanika Holz, Nina Ledinek, Sara Može. 2012. Učni korpus ssj500k kot podatkovna zbirka.

These experiments were presented as a part of Serge Sharoff's keynote talk at BSNLP 2017