Identifying Motion Entities in Natural Language and A Case Study for Named Entity Recognition (2020)

Abstract: Motion recognition is one of the basic cognitive capabilities of many life forms, however, detecting 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 understanding. 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 motion 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.

## introduction in semantic approaches such as semantic role labeling (srl) and abstract meaning representation (amr), semantic features can have peculiarities of a particular language @xcite . in contrast, the irreducible semantic core considered by the natural semantic metalanguage (nsm) approach, builds a kind of universal mini-language where its core components, the semantic primes 1 , are universal to all languages @xcite . semantic primes, are defined as universal 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 @xcite . 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 @xcite @xcite , 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 spatial features in natural language has considered and investigated semantics related with motion events, their elements, including encoding schemes @xcite @xcite , to the best of our knowledge, there has not been an investigation of how motion detection in natural language, and motion features, might impact nlp tasks. to start this investigation, we selected named-entity recognition (ner), a tagging task that identifies and classifies named entities in a text into predefined semantic types such as person and organization, among others. ner is a core task of many nlp systems, especially information extraction and question answering. this paper explores and shows results of a motion entity tagging model and how leveraging motion entities positively impact models for the ner task. this paper is organized as follows, section 2 presents characteristics of motion in natural language and related work; section 3 presents the limit dataset, created as a resource to build motion in text classification and motion entity tagger models, which is publicly released to the community. a model for motion entity tagging is presented in section 4; in section 5, we present the results of our investigation about the impact of the entity in motion feature in the ner task; conclusions and future work are presented in section 6. ## background in natural language, motion instances can be present as literal or figurative @xcite @xcite . motion describing the movement of a physical object is considered literal motion (e.g. "william lifted the weight"), while figurative motion is when there is no direct occurrence of motion (e.g. "a gentle slope brought us in one hour to the main summit"), or not occurrence of physical motion at all (e.g. "the range runs east and west"). although figurative motion can sometimes indirectly indicate motion of physical objects, this is not always the case. therefore, we decided to focus our analysis on literal motion which directly indicates motion events related with physical entities in motion. four elements of a motion event are identified by contemporary linguistics: (a) an object, also called the 'figure ', 'theme', or 'participant', (b) moving along (c) a 'path' (with 'source' and 'goal') (d) with respect to another reference-object, also called 'ground' @xcite . nevertheless, not all of motion elements are present in all instances of motion in natural language. for instance, verbs such as 'wiggle' or 'wave' describe the manner of motion of an object but do not require traversal of a path. similarly, other verbs such as 'dance' may sometimes not specify a motion path. for this reason, we decided to identify, as one core element of the motion event, the entities in motion (i.e. figure or participant), and start our analysis of motion in natural language by focusing on the occurrence of physical motion in text. to the best of our knowledge, we are the first to investigate how motion features impact an nlp task. however, there have been related work around motion data sets and motion classification tasks in natural language. in the context of capturing spatial information in text, motionbank @xcite was a corpus created with fictive and literal motion sentences, including information about different features related with location and non-location, one of which is motion. then the entityin-motion annotation was proposed as part of iso-space, as the mover entity associated with the movelink relation annotation @xcite . recently, and with the aim to better understand the semantics of spatial information in text, @xcite presented a fiction motion data set and classification model for fictive motion sentences. although previous motion data sets could have been considered for our study, we were not able to find any of the mentioned data sets publicly available online yet. therefore, we decided to use the recently released literal-motion-in-text (limit) dataset @xcite of literal motion sentences. ## the literal motion in text dataset the literal-motion-in-text (limit) dataset @xcite was created as a resource to identify both literal motion in text and the entities in motion from a natural language expression describing the motion of physical entities. with models built with limit dataset, it is also possible to investigate the impact of motion features in natural language tasks. limit was publicly released foot_0 to promote further research in this area. the size of the limit dataset is 24, 559 sentences: 15, 346 sentences describing literal motion, and 9, 213 sentences describing figurative or no motion events. in this section we briefly describe the characteristics of the limit dataset, which was used for training the model to identify physical entities in motion in a sentence. ## motion in text baseline model to investigate how motion features might possibly impact nlp tasks we need a model to identify motion entities in natural language text. in this section we first describe the baseline model we trained to identify entities in motion in a sentence. then we describe an algorithm designed to extract short sentences from complex long sentences to improve the performance of the model output. ## case study: impact of motion entity features on named entity recognition task in this section we present a preliminary study about the proportion of motion sentences in well-known nlp datasets. then, we present the case study showing the impact of entity in motion features on ner models. ## conclusions and future research we presented a motion entity tagger model to identify entities in motion in text, and presented a method to improve entity in motion predictions for complex, long motion sentences. furthermore, we present our initial results towards analyzing how features of literal motion, specifically motion entities, can impact nlp tasks. specifically, we investigated the impact of motion features on the ner task and proved our hypothesis that motion features can improve the performance of the ner model. finally, we show the study of co-occurrence between category [per] in ner and animate motion entity, which indicates how the motion feature benefits the classification results for the [per] category. for future research, we plan to continue investigating how motion features can benefit other more complex nlp tasks such as textual inference. we also study approaches for integrating motion features to obtain better performance in different tasks which helps with better understanding of natural language overall.