Semantic Grammar Research Articles

Corpus-driven conceptual analysis of epidemic and coronavirus for the Humanitarian Encyclopedia

Abstract Terminological conceptual analysis can be applied to purposes beyond terminology work. This article presents a Frame-based Terminology approach adapted to analyse concepts and inform the content of entries in the Humanitarian Encyclopedia. It proposes a method for conceptual analysis by systematising the extraction of knowledge rich contexts (KRCs) around corpus querying tasks through semantic sketch grammars (SSGs) and macros with knowledge patterns (KPs). KRCs are curated manually, modelled into conceptual propositions, and combined with corpus metadata into unified datasets. The method was tested on epidemic and coronavirus and their results are presented. This study provides a preliminary model to operationalise the study of conceptual variation. It also identifies the areas of terminological conceptual analysis with the potential to be informed by other research methods towards creating a standalone methodology.

The Association between Music and Language in Children: A State-of-the-Art Review.

Music and language are two complex systems that specifically characterize the human communication toolkit. There has been a heated debate in the literature on whether music was an evolutionary precursor to language or a byproduct of cognitive faculties that developed to support language. The present review of existing literature about the relationship between music and language highlights that music plays a critical role in language development in early life. Our findings revealed that musical properties, such as rhythm and melody, could affect language acquisition in semantic processing and grammar, including syntactic aspects and phonological awareness. Overall, the results of the current review shed further light on the complex mechanisms involving the music-language link, highlighting that music plays a central role in the comprehension of language development from the early stages of life.

Frame semantic grammars: Where frame analysis meets linguistics to study collective action frames

After navigating conceptual and empirical developments in frame analysis research, I reflect on cornerstones and weaknesses in its elaboration of a rigorous analytical prism. In the reflection, I discuss how combining the frame analysis conceptual toolkit with linguistics work on semantic grammars can perhaps help heal some of these weaknesses.

Controllable Text Generation Using Semantic Control Grammar

Controllable text generation is the primary technique for controlling specific attributes such as topic, keywords and obtaining augmented data. This work proposes a novel controllable text generation framework to improve the controllability of generation models. First, we introduce semantic control grammar, a controllable input format to generate sentences that satisfy the constraints. Second, we adopt a generation and rerank method to obtain semantically reranked controlled sentences. Extensive experiments and analyses are conducted on benchmark, natural language understanding, data-to-text generation, and text classification datasets. Through automatic evaluations, we show that our method leads to improvement over strong baselines. The results show that our model can control sentence and word attributes and semantically generate natural sentences. Furthermore, our techniques improve the generation quality of the model.

Computing semantic similarity of texts based on deep graph learning with ability to use semantic role label information

We propose a deep graph learning approach for computing semantic textual similarity (STS) by using semantic role labels generated by a Semantic Role Labeling (SRL) system. SRL system output has significant challenges in dealing with graph-neural networks because it doesn't have a graph structure. To address these challenges, we propose a novel SRL graph by using semantic role labels and dependency grammar. For processing the SRL graph, we proposed a Deep Graph Neural Network (DGNN) based graph-U-Net model that is placed on top of the transformers to use a variety of transformers to process representations obtained from them. We investigate the effect of using the proposed DGNN and SRL graph on the performance of some transformers in computing STS. For the evaluation of our approach, we use STS2017 and SICK datasets. Experimental evaluations show that using the SRL graph accompanied by applying the proposed DGNN increases the performance of the transformers used in the DGNN.

Who Does What to Whom? Making Text Parsers Work for Sociological Inquiry

Over the past decade, sociologists have become increasingly interested in the formal study of semantic relations within text. Most contemporary studies focus either on mapping concept co-occurrences or on measuring semantic associations via word embeddings. Although conducive to many research goals, these approaches share an important limitation: they abstract away what one can call the event structure of texts, that is, the narrative action that takes place in them. I aim to overcome this limitation by introducing a new framework for extracting semantically rich relations from text that involves three components. First, a semantic grammar structured around textual entities that distinguishes six motif classes: actions of an entity, treatments of an entity, agents acting upon an entity, patients acted upon by an entity, characterizations of an entity, and possessions of an entity; second, a comprehensive set of mapping rules, which make it possible to recover motifs from predictions of dependency parsers; third, an R package that allows researchers to extract motifs from their own texts. The framework is demonstrated in empirical analyses on gendered interaction in novels and constructions of collective identity by U.S. presidential candidates.

Repérage automatisé de l’hyponymie dans des corpus spécialisés en français à l’aide de Sketch Engine

AbstractHyponymy is an essential semantic relation in terminology, as it represents the hierarchical organization of concepts. Much has been written about hyponymy extraction. However, terminologists working with French do not currently have user-friendly and freely available tools to automatically extract hyper-hyponymic pairs from their own corpora. This paper presents the most recent version of the ESSG (EcoLexicon Semantic Sketch Grammar) methodology, a knowledge-pattern-based approach that enables Sketch Engine to extract semantic relations. This methodology is applied to the development and evaluation of the ESSG-fr, a semantic sketch grammar for hyponymy extraction in French. The evaluation results show that the ESSG-fr is a reliable domain-independent tool for terminologists wishing to extract simple hyper-hyponymic pairs and the corresponding concordances from specialized corpora.

Design and Develop Amharic Language Interface to Database

Background/Objectives: Information has become part of our existence and to access the information from database we need to be skilled with database query languages such as SQL. Hence in this study we propose Amharic Language Interface to Database (ALIDB). Here, the request is simple like asking a human to do so in a local language (Amharic). Method: So far, different techniques such as pattern matching, syntax based, semantic grammar based and Intermediate Representation Language systems have been used to develop NLIDB. Among these techniques, the study employed pattern matching and similarity checking for developing Amharic language text retrieval from the Database. Findings: The result of the experiment shows 91% accuracy. However, the scheme has no impact on Amharic temporal queries. Further development will be done on the algorithm that includes temporal queries in ALIDB. Novelty: Finally we identified 20 rules and thereby contributed a new pattern / algorithm for this language that converts Amharic sentence into a Structured Query Language (SQL) and fetch results from the Database. Keywords: Amharic Language; Database interface; DBMS; NLIDB; NLP

Mood in Language: A Lexico-syntactic Analysis of Nigeria Centre for Disease Control (NCDC) Covid-19 Sensitisation SMS

Using data from widely circulated SMSes from the Nigeria Centre for Disease Control (NCDC), this paper provides a lexical-syntactic analysis of language use by a government agency during the COVID-19 pandemic in Nigeria. The framework used for the analysis is dyadic, encompassing tenets from semantic theory (which deals extensively with lexical choices as well as the determination of the meanings attached to them) and Systemic Functional Grammar. The analysis reveals that a writer’s selection of lexical items in a particular text is a reflection of his mood. This plays a significant and important role in the writer’s arrangement of the lexical items into peculiar sentence patterns in the text. Mood which is a system at the deep structure of Systemic Functional Grammar determines, to a very large extent, the language a writer employs in a text. Repetition in NCDC COVID-19 sensitisation messages is used to emphasize the importance of such words. It is meant to further convince the general public that only self-efforts or actions can prevent the spread of COVID-19 as majority of the repeated words are action stimulated words (verbs).

A Large-Scale Analysis of the Semantic Password Model and Linguistic Patterns in Passwords

In this article, we present a thorough evaluation of semantic password grammars. We report multifactorial experiments that test the impact of sample size, probability smoothing, and linguistic information on password cracking. The semantic grammars are compared with state-of-the-art probabilistic context-free grammar ( PCFG ) and neural network models, and tested in cross-validation and A vs. B scenarios. We present results that reveal the contributions of part-of-speech (syntactic) and semantic patterns, and suggest that the former are more consequential to the security of passwords. Our results show that in many cases PCFGs are still competitive models compared to their latest neural network counterparts. In addition, we show that there is little performance gain in training PCFGs with more than 1 million passwords. We present qualitative analyses of four password leaks (Mate1, 000webhost, Comcast, and RockYou) based on trained semantic grammars, and derive graphical models that capture high-level dependencies between token classes. Finally, we confirm the similarity inferences from our qualitative analysis by examining the effectiveness of grammars trained and tested on all pairs of leaks.

المعنى النحوی الدلالی فی سورة الشمس دراسة تحلیلیة.

يتناول البحث المعنى الدلالي النحوي لسورة الشمس بإبراز بعض الظواهر النحوية في السورة کتکرار القسم, و(ال) في المقسم به و في باقي السورة والدلالة النحوية لها, والتأنيث و التذکير وتتابع ذلک بشکل مقصود لدلالة معينة, و تتبع وتتبع إعراب مفردات السورة مما يظهر اتساق النص و دلالة الضبط. الکلمات المفتاحية: اللغة العربية, النحو العربي, الظواهر النحوية, المعنى الدلالي. Semantic grammar in the Sun's Sura (al-shams) Analytical study Rehan Abdel Mohsen Mohamad Mansur Professor of linguistics (grammar and declension) King Khalid University, Saudi Arabia Faculty of Science and Literature, Khamis Mushayt Email: Rehammansour123@gmail.com Abstract : The research deals with the semantic meaning of the Sun's orbiting by highlighting some of the Grammatical manifestations in the orbiting, such as the replication of the oth, (the) in its divider and in the rest of its orbiting and Grammatical Semantic significance, and feminism and reminder and follow this intentionally for a particular significance. And follow the words of the word Sura , which shows the consistency of the text and the significance of the adjustment Key words: Arabic, Arabic grammar, Grammatical manifestations, and Semantic meaning

Distributional Semantics and Linguistic Theory

Distributional semantics provides multidimensional, graded, empirically induced word representations that successfully capture many aspects of meaning in natural languages, as shown by a large body of research in computational linguistics; yet, its impact in theoretical linguistics has so far been limited. This review provides a critical discussion of the literature on distributional semantics, with an emphasis on methods and results that are relevant for theoretical linguistics, in three areas: semantic change, polysemy and composition, and the grammar–semantics interface (specifically, the interface of semantics with syntax and with derivational morphology). The goal of this review is to foster greater cross-fertilization of theoretical and computational approaches to language as a means to advance our collective knowledge of how it works.

Polish School of Semantic Grammar (Polish Semantic Grammar Circle)

This paper is an early excerpt from a knowledge base being prepared by the author. It presents basic information about the Polish semantic grammar circle. This school of thought can be traced as far back as the 1970s and remains active till present day. Only major names, works and controversies are briefly discussed. The paper should be treated as an introduction to modern Polish semantics and contrastive (confrontative) studies of Slavic languages.

Robust Spoken Language Understanding for House Service Robots

Service robotics has been growing significantly in the last years, leading to several research results and to a number of consumer products. One of the essential features of these robotic platforms is represented by the ability of interacting with users through natural language. Spoken commands can be processed by a Spoken Language Understanding chain, in order to obtain the desired behavior of the robot. The entry point of such a process is represented by an Automatic Speech Recognition (ASR) module, that provides a list of transcriptions for a given spoken utterance. Although several well-performing ASR engines are available off-the-shelf, they operate in a general purpose setting. Hence, they may be not well suited in the recognition of utterances given to robots in specific domains. In this work, we propose a practical yet robust strategy to re-rank lists of transcriptions. This approach improves the quality of ASR systems in situated scenarios, i.e., the transcription of robotic commands. The proposed method relies upon evidences derived by a semantic grammar with semantic actions, designed to model typical commands expressed in scenarios that are specific to human service robotics. The outcomes obtained through an experimental evaluation show that the approach is able to effectively outperform the ASR baseline, obtained by selecting the first transcription suggested by the ASR.

A semantic grammar for beginning communicators

Beginning communicators are children faced with to the task of language learning. Young, typically developing children are early speakers before the age of 2years, the initial period of communication and language development. When this development is not happening because of disabilities or delays, it is possible to use computer-aided tools in order to help people to palliate or overcome such limitations, at least partially. For example, an Augmentative and Alternative Communication system must manage a vocabulary made up of several hundred concepts, usually without knowledge of the language at a semantic and pragmatic level. Such knowledge would make possible, for example, the implementation of new strategies for word prediction based on meaning, a very precise natural language generation or semantic parsing of the messages so that it would not allow the composition of meaningless messages. We present Simple Upper Ontology, SUpO, a semantic grammar which is made up of detailed knowledge of facts of the everyday life of simple words. In order to build SUpO we developed a procedure designed to give syntactic detail to part of FrameNet, a well-known ontology which encodes knowledge about usages of language at the semantic level. The result of this procedure is a multilingual semantic grammar which has been implemented by using Grammatical Framework. Finally, we propose some examples of tools where the use of SUpO would be suitable.

Robots that Talk and Listen Judith A. Markowitz (editor) (President of J. Markowitz, Consultants, Chicago, IL) Berlin/Boston/Munich: Walter de Gruyter, 2015, hardbound, ISBN 978-1-61451-603-3; PDF, e-ISBN 978-1-61451-440-4 EPUB; e-ISBN 978-1-61451-915-7

<b>Robots that Talk and Listen</b> <b>Judith A. Markowitz (editor)</b> (President of J. Markowitz, Consultants, Chicago, IL) Berlin/Boston/Munich: Walter de Gruyter, 2015, hardbound, ISBN 978-1-61451-603-3; PDF, e-ISBN 978-1-61451-440-4 EPUB; e-ISBN 978-1-61451-915-7

THE MATERIAL AND THE PSYCHOLOGICAL: AN ANALYSIS OF THE CLAUSE STRUCTURE OF TAGALOG USING COGNITIVE GRAMMAR

For this paper, the researcher attempts to describe the clause structure of Tagalog using Cognitive Grammar (CG). Abiding by the said framework, sentences are regarded as grammatical constructions that represent different event schemas, which are then categorised into situations in: (1) the material world, or how the structured world exists, changes, or undergoes processes; and (2) the psychological world, or the internal world of human sensation, emotion, perception and thought. Analysing the semantic grammar of Tagalog, that is, looking at linguistic utterances as motivated by the meaning that the speaker wants to express, this study aims to provide new insights with regards to the characteristics of the components of grammar as a reflection of cognition.

An Overview of NLIDB Approaches and Implementation for Airline Reservation System

Relational databases are queried using database query languages such as SQL. Natural language interfaces to databases (NLIDB) are systems that translate a natural language sentence into a database query. In this modern techno-crazy world, as more and more laymen access various systems and applications through their smart phones and tablets, the need for Natural Language Interfaces (NLIs) has increased manifold. The challenges in Natural language Query processing are interpreting the sentence correctly, removal of various ambiguity and mapping to the appropriate context. Natural language access problem is actually composed of two stages Linguistic processing and Database processing. NLIDB techniques encompass a wide variety of approaches. The approaches include traditional methods such as Pattern Matching, Syntactic Parsing and Semantic Grammar to modern systems such as Intermediate Query Generation, Machine Learning and Ontologies. In this report, various approaches to build NLIDB systems have been analyzed and compared along with their advantages, disadvantages and application areas. Also, a natural language interface to a flight reservation system has been implemented comprising of flight and booking inquiry systems. General Terms Natural Language Processing (NLP), Natural Language Interface to Databases (NLIDB)

A Study on the Semantic Grammar as a Theory and Bi-directional Grammar as a Research Method

A Study on the Semantic Grammar as a Theory and Bi-directional Grammar as a Research Method

An ERP study of structural anomalies in native and semantic free artificial grammar: Evidence for shared processing mechanisms

Artificial grammars have been widely applied to the study of sequential learning in language, but few studies have directly compared the neural correlates of artificial and native grammar processing. In this study, we examined Event Related Potentials (ERPs) elicited by structural anomalies in semantic-free artificial grammar sequences and sentences in the subjects’ native language (Spanish). Although ERPs differed during early stages, we observed similar posterior negativities (N400) and P600 effects in a late stage. We interpret these results as evidence of at least partially shared neural mechanisms for processing of language and artificial grammars. We suggest that in both the natural and artificial grammars, the N400 and P600 components we observed can be explained as the result of unfulfilled predictions about incoming stimuli.