Identifying Motion Entities in Natural Language and A Case Study for Named Entity Recognition

Proceedings of the 28th International Conference on Computational Linguistics, pages 5250–5258 Barcelona, Spain (Online), December 8-13, 2020 5250 Identifying Motion Entities in Natural Language and A Case Study for Named Entity Recognition Ngoc Phuoc An Vo, Irene Manotas, Vadim Sheinin, Octavian Popescu IBM Research Yorktown Heights, NY, US {ngoc.phuoc.an.vo, irene.manotas}@ibm.com {vadims, o.popescu}@us.ibm.com Abstract Motion recognition is one of the basic cognitive capabilities of many life forms, however, detect- ing and understanding motion in text is not a trivial task. In addition, identifying motion entities in natural language is not only challenging but also beneficial for a better natural language under- standing. In this paper, we present a Motion Entity Tagging (MET) model to identify entities in motion in a text using the Literal-Motion-in-Text (LiMiT) dataset for training and evaluating the model. Then we propose a new method to split clauses and phrases from complex and long mo- tion sentences to improve the performance of our MET model. We also present results showing that motion features, in particular, entity in motion benefits the Named-Entity Recognition (NER) task. Finally, we present an analysis for the special co-occurrence relation between the person category in NER and animate entities in motion, which significantly improves the classification performance for the person category in NER. 1 Introduction In semantic approaches such as Semantic Role Labeling (SRL) and Abstract Meaning Representa- tion (AMR), semantic features can have peculiarities of a particular language (Zhu et al., 2019). In contrast, the irreducible semantic core considered by the Natural Semantic Metalanguage (NSM) ap- proach, builds a kind of universal mini-language where its core components, the semantic primes1, are universal to all languages (Goddard, 1997; Wierzbicka, 1980). Semantic primes, are defined as univer- sal core meanings. The hypothesis in NSM is that meaning could be reconstructed into basic elements or semantic primes. Then, any complex meaning can be decomposed without circularity and without residue into a combination of discrete other meanings, the semantic primes, among which is the meaning of motion represented by the MOVE semantic prime. In natural language, motion can describe different movement types (e.g. rotational, transactional, internal, etc), it can relate to changes in a concept or abstract object when it is figurative motion (e.g. ”Her voice twisted from incredulity to astonishment”), or it can describe the movement of physical object(s) when it is literal motion (e.g. ”The player twisted his leg before kicking the ball”). Thus, motion analysis and detection can be challenging because of the different ways in which motion can be used in natural language (Beavers et al., 2010). Overall, motion is a linguistic primitive that allows us to express complicated events more concisely, because of its features motion has been considered extensively in theoretical linguistic analysis. Many linguists have agreed that motion is a semantic fundamental (Talmy, 1985; Goddard, 1997; Beavers et al., 2010), thus identifying motion in text and its features is important, and so it is to investigate how motion features could help improve Natural Language Processing (NLP) tasks. Although analysis of …

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