Abstract
Identifying the specific semantic relations between words is crucial for IR and NLP systems. Our goal in this paper is twofold. First, we want to understand whether learning a classifier for one semantic relation (e.g. hypernymy) can gain from concurrently learning another classifier for a cognitively-linked semantic relation (e.g. co-hyponymy). Second, we evaluate how these systems perform where only few labeled examples exist. To answer the first question, we rely on a multi-task neural network architecture, while for the second we use self-learning to evaluate whether semi-supervision improves performance. Our results on two popular datasets as well as a novel dataset proposed in this paper show that concurrent learning of semantic relations consistently benefits performance. On the other hand, we find that semi-supervised learning can be useful depending on the semantic relation. The code and the datasets are available at https://bit.ly/2Qitasd.
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Notes
- 1.
This issue is out of the scope of this paper.
- 2.
We are aware that this architecture can further be improved by additional task-specific inputs, but as a great deal of possible models can be proposed, which deserve intensive research, this issue remains out of the scope of this paper.
- 3.
- 4.
Available at https://bit.ly/2Qitasd.
- 5.
- 6.
This value was set experimentally.
- 7.
A large number of hypernym pairs contain the root synset “entity”, i.e. path length equals to 0.
- 8.
All datasets are available at https://bit.ly/2Qitasd.
- 9.
A multi-class model learns to separate between several classes and direct comparison with binary models is not fair. Nevertheless, we report its performance as it highlights the potential of multi-class learning for problems that are cognitively similar.
- 10.
The code is available at https://bit.ly/2Qitasd.
- 11.
Column 3 of Table 4.
- 12.
Note that due to the lexical split process, results can not directly be compared to the ones obtained over ROOT9 or RUMEN.
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Balikas, G., Dias, G., Moraliyski, R., Akhmouch, H., Amini, MR. (2019). Learning Lexical-Semantic Relations Using Intuitive Cognitive Links. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11437. Springer, Cham. https://doi.org/10.1007/978-3-030-15712-8_1
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