'Natural concepts' in the spatial topological domain - adpositional meanings in crosslinguistic perspective: An exercise in semantic typology

Most approaches to spatial language have assumed that the simplest spatial notions are (after Piaget) topological and universal (containment, contiguity, proximity, support, represented as semantic primitives suchas IN, ON, UNDER, etc.). These concepts would be coded directly in language, above all in small closed classes suchas adpositions—thus providing a striking example of semantic categories as language-specific projections of universal conceptual notions. This idea, if correct, should have as a consequence that the semantic categories instantiated in spatial adpositions should be essentially uniform crosslinguistically. This article attempts to verify this possibility by comparing the semantics of spatial adpositions in nine unrelated languages, with the help of a standard elicitation procedure, thus producing a preliminary semantic typology of spatial adpositional systems. The differences between the languages turn out to be so significant as to be incompatible withstronger versions of the UNIVERSAL CONCEPTUAL CATEGORIES hypothesis. Rather, the language-specific spatial adposition meanings seem to emerge as compact subsets of an underlying semantic space, withcertain areas being statistical ATTRACTORS or FOCI. Moreover, a comparison of systems withdifferent degrees of complexity suggests the possibility of positing implicational hierarchies for spatial adpositions. But such hierarchies need to be treated as successive divisions of semantic space, as in recent treatments of basic color terms. This type of analysis appears to be a promising approachfor future work in semantic typology

The Hippocampal Cognitive Map: One Space or Many?

New evidence reported by Solomon et al. that hippocampal activity tracks distance in semantic space during recall supports the growing consensus of a domain-general cognitive map. Nevertheless, are all inputs equally processed into a ‘universal map’, or are there input constraints (e.g., space, semantics) that lead to differentiated multiple maps across the hippocampus that have distinct properties

Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space

The trends of open science have enabled several open scholarly datasets which include millions of papers and authors. Managing, exploring, and utilizing such large and complicated datasets effectively are challenging. In recent years, the knowledge graph has emerged as a universal data format for representing knowledge about heterogeneous entities and their relationships. The knowledge graph can be modeled by knowledge graph embedding methods, which represent entities and relations as embedding vectors in semantic space, then model the interactions between these embedding vectors. However, the semantic structures in the knowledge graph embedding space are not well-studied, thus knowledge graph embedding methods are usually only used for knowledge graph completion but not data representation and analysis. In this paper, we propose to analyze these semantic structures based on the well-studied word embedding space and use them to support data exploration. We also define the semantic queries, which are algebraic operations between the embedding vectors in the knowledge graph embedding space, to solve queries such as similarity and analogy between the entities on the original datasets. We then design a general framework for data exploration by semantic queries and discuss the solution to some traditional scholarly data exploration tasks. We also propose some new interesting tasks that can be solved based on the uncanny semantic structures of the embedding space.Comment: TPDL 2019; add appendix for the KG20C scholarly knowledge graph benchmark datase

A Description Of Space Relations In An NLP Model: The ABBYY Compreno Approach

The current paper is devoted to a formal analysis of the space category and, especially, to questions bound with the presentation of space relations in a formal NLP model. The aim is to demonstrate how linguistic and cognitive problems relating to spatial categorization, definition of spatial entities, and the expression of different locative senses in natural languages can be solved in an artificial intelligence system. We offer a description of the locative groups in the ABBYY Compreno formalism – an integral NLP framework applied for machine translation, semantic search, fact extraction, and other tasks based on the semantic analysis of texts. The model is based on a universal semantic hierarchy of the thesaurus type and includes a description of all possible semantic and syntactic links every word can attach. In this work we define the set of semantic locative relations between words, suggest different tools for their syntactic presentation, give formal restrictions for the word classes that can denote spaces, and show different strategies of dealing with locative prepositions, especially as far as the problem of their machine translation is concerned

Universal Dimensions of Meaning Derived from Semantic Relations among Words and Senses: Mereological Completeness vs. Ontological Generality

A key to semantic analysis is a precise and practically useful definition of meaning that is general for all domains of knowledge. We previously introduced the notion of weak semantic map: a metric space allocating concepts along their most general (universal) semantic characteristics while at the same time ignoring other, domain-specific aspects of their meanings. Here we address questions of the number, quality, and mutual independence of the weak semantic dimensions. Specifically, we employ semantic relationships not previously used for weak semantic mapping, such as holonymy/meronymy (“is-part/member-of”), and we compare maps constructed from word senses to those constructed from words. We show that the “completeness” dimension derived from the holonym/meronym relation is independent of, and practically orthogonal to, the “abstractness” dimension derived from the hypernym-hyponym (“is-a”) relation, while both dimensions are orthogonal to the maps derived from synonymy and antonymy. Interestingly, the choice of using relations among words vs. senses implies a non-trivial trade-off between rich and unambiguous information due to homonymy and polysemy. The practical utility of the new and prior dimensions is illustrated by the automated evaluation of different kinds of documents. Residual analysis of available linguistic resources, such as WordNet, suggests that the number of universal semantic dimensions representable in natural language may be finite. Their complete characterization, as well as the extension of results to non-linguistic materials, remains an open challenge

EMMA-X: An EM-like Multilingual Pre-training Algorithm for Cross-lingual Representation Learning

Expressing universal semantics common to all languages is helpful in understanding the meanings of complex and culture-specific sentences. The research theme underlying this scenario focuses on learning universal representations across languages with the usage of massive parallel corpora. However, due to the sparsity and scarcity of parallel data, there is still a big challenge in learning authentic ``universals'' for any two languages. In this paper, we propose EMMA-X: an EM-like Multilingual pre-training Algorithm, to learn (X)Cross-lingual universals with the aid of excessive multilingual non-parallel data. EMMA-X unifies the cross-lingual representation learning task and an extra semantic relation prediction task within an EM framework. Both the extra semantic classifier and the cross-lingual sentence encoder approximate the semantic relation of two sentences, and supervise each other until convergence. To evaluate EMMA-X, we conduct experiments on XRETE, a newly introduced benchmark containing 12 widely studied cross-lingual tasks that fully depend on sentence-level representations. Results reveal that EMMA-X achieves state-of-the-art performance. Further geometric analysis of the built representation space with three requirements demonstrates the superiority of EMMA-X over advanced models.Comment: Accepted by NeurIPS 202