Towards Learning Terminological Concept Systems from Multilingual Natural Language Text

Reference

Wachowiak, L., Lang, C., Heinisch, B., & Gromann, D. (2021). Towards Learning Terminological Concept Systems from Multilingual Natural Language Text. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Schloss Dagstuhl-Leibniz-Zentrum für Informatik. Chicago

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Try it Out

If you just want to try out the service without using the code provided here you can use our implementation made available on the European Language Grid. However, the implemenation on the European Language Grid utilizes a slightly improved architecture as well as a different dataset for model-training; an updated description will be made available soon.

Architecture

Architecture for extracting terminological concepts systems from natural language.

Example Output

The resulting terminological concept system is returned in a TBX format as well as connected graph (see below).

Term Extraction Scores

Dataset	Precicion	Recall	F1
TermEval2020 EN	54.9	62.2	58.3
TermEval2020 FR	65.4	51.4	57.6
TermEval2020 NL	67.9	71.7	69.8
ACL RD-TEC Annotator 1	74.4	77.2	75.8
ACL RD-TEC Annotator 2	80.1	79.3	80.0

Relation Extraction Scores

Relation Type	Precicion	Recall	F1
synonymy	0.85	0.76	0.80
activityRelation (e1,e2)	0.93	0.97	0.95
activityRelation (e2,e1)	0.00	0.00	0.00
associativeRelation	0.90	0.92	0.91
causalRelation (e1,e2)	0.90	0.95	0.92
causalRelation (e2,e1)	0.92	0.91	0.91
genericRelation (e1,e2)	0.90	0.93	0.92
genericRelation (e2,e1)	0.46	0.41	0.43
instrumentalRelation (e1,e2)	0.72	0.68	0.70
instrumentalRelation (e2,e1)	0.85	0.88	0.86
none	0.69	0.44	0.54
originationRelation (e1,e2)	0.83	0.89	0.86
originationRelation (e2,e1)	0.84	0.83	0.83
partitiveRelation (e1,e2)	0.90	0.85	0.87
partitiveRelation (e2,e1)	0.77	0.77	0.77
spatialRelation (e1,e2)	0.90	0.91	0.91
spatialRelation (e2,e1)	0.90	0.82	0.86