Natural language research

Senses are the extensions of language and interrelations between word meanings within language and the basis of flexible language use. Computers are basically made to process symbols. Computational linguistics is to simulate part of, if not all of, the natural language competence. From the perspective of computational linguistics, this paper sheds some light on semantic formalization, and proposes tagging of senses along with tagging weight values when processing word meanings. Also, this paper makes attempts to apply sense weight values to sentence processing in computational linguistics. Keywords-semantics, sense, weight, formalization, computational linguistics I. SEMANTIC KNOWLEDGE IN LANGUAGE PROCESSING According to prototype theory, semantic knowledge is part of human’s cognitive and belief systems. Semantics is also the critical research area since its initiation in 1893 by Michael Breal, and then by Trier and Lakoff who contributes to semantic research from both the descriptive and generative perspective. In linguistic semantics, rules and principles explain how literal meaning is coded in language and how to understand the “senses” of an expression or sense-relations that hold between one word and other expressions within the same language (Ceusters et al, 1999). Language is the carrier of both thoughts and forms. Thoughts are to induce, deduce, infer and judge, while senses are the contents and basis of thoughts. Senses are not the repeated addition of forms, rather a psychological process. Semantics is the mapping of cognitive structure from language expressions. Based on concepts, which are the abstract and general categorization of objects, people can apply semantic principles, situational contexts and encyclopedic knowledge to construct cognitive schema of the outside world. With frequent interactions and cognitive strategies, limited forms can be used to express unlimited connotation of thoughts (Tesser and Leone, 1977). For example, same meanings can be expressed with different semantic forms. a) 他这个人真有意思 (funny); b) 于是人们以为他们有了意思 (wish); c) 你向他意思意思 (express)如何; d) .我根本没有那个意思 (thought); e) 他常说人生真没有意思 (nonsense); f) 你们这么说是什么意思 (intention) These sentences are easy to comprehend. People not only use the grammatical structures but also the sense weights to process the words. The sense weight schema are stored in memory, and activated to represent and distill other sense weights. In this chain activation, sense weights are restructured and created to reflect prototypes. For example, “你好?”, “你好么?”, “好么你?”. Different structures are used in the above examples, but their essential meaning remains the same. So, semantic knowledge is of utmost importance in language comprehension. II. THE STATUS QUO OF COMPUTATIONAL LINGUISTICS Three domains and three corresponding levels of study are involved in building natural language processing systems, i.e. theoretical linguistics and grammar level, computational linguistics and formalization level, computer technology and realization level. Among them, grammar level concerns the innate characteristics of language and paves the way for the latter modules. Realization level focuses on how to control computer operation and provides the efficient development tools and environment. Formalization level is the interface of grammar level and realization level, with the task to readjust the general grammar modules to form grammar modules for convenient computer processing. Computational linguists’ mission is to establish formal grammars (Kaufmann and Pfister, 2012). Computational linguistics is also to establish formalized mathematical modules to analyze and process natural language, and develop computer programs to make it possible that machines can simulate part of, if not all of, human behavior and competence. Computers can only be used to process symbols and all those processed by computers are symbolized with no exception of languages (Yushi, 2003). Meanwhile, there have been breakthroughs in processing Chinese characters and phrases, so sentence level processing has become critical. Basic researches in syntactic, semantic and pragmatic knowledge are the frontier studies in Chinese processing. And how to acquire syntactic and semantic knowledge is the key, while the latter constitutes the major difficulty in processing the word level senses. As aforementioned, semantic formalization can take on many impossible missions in artificial intelligence and computational linguistics, and face challenges at the same time. Thus, the researcher will use semantic field to investigate formalization issues. Semantic fields are the sememe systems, which include the semantemes to represent similarities and differences, and are classified by the subordinates, i.e. degree and number of abstraction of Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) Published by Atlantis Press, Paris, France.

In this paper, we show how a constraint-based approach influences the modeling of (1) preposition lexicalization and of (2) the choice of syntactic structure in natural language generation (in particuler in French). We concentrate on the linguistic description, which is the most challenging. The CSP procedures themselves are then rather straightforward. In the first case, preposition choice depends on the verb and its requirements, on the one hand, and the characteristics of the NP the preposition heads, on the other hand. In the second case, the choice of a particular syntactic structure depends on syntactic, semantic and pragmatic constraints on a verb, its arguments and on the parameters of the semantic representation. These dependencies may induce somewhat contradictory expectations. A constraint-based approach introduces a declarative model of these complex relations, allowing to identify all the possible (1) lexical choices or (2) syntactic structures which can be predicted from lexical descriptions.KeywordsSyntactic StructureConstraint Satisfaction ProblemNatural Language GenerationLinguistic DescriptionMovement VerbThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The vision of the Semantic Web is to reduce manual discovery and usage of Web resources (documents and services) and to allow software agents to automatically identify these Web resources, integrate them and execute them for achieving the intended goals of the user. Such a composed Web service may be represented as a workflow, called service flow. Current studies of Web services are not sufficient for automatic composition. This paper presents different types of compositional knowledge required for Web service discovery and composition: syntactic, semantic and pragmatic knowledge. As a proof of concept, we have implemented our framework in a cardiovascular domain which requires advanced service discovery and composition across heterogeneous platforms of multiple organizations. Within this framework, we describe (1) How to represent the compositional knowledge, which plays a role in service discovery and composition, in DAML-S; (2) How heterogeneous medical services intemperate in a composed medical service flow. (3) To solve this knowledge level integration, we build on an ontology integration method called SEMIO (Semantic Interoperability).

The measure of sentence similarity is useful in various research fields, such as artificial intelligence, knowledge management, and information retrieval. Several methods have been proposed to measure the sentence similarity based on syntactic and/or semantic knowledge. Most proposals are evaluated on English sentences where the accuracy can decrease when these proposals are applied to other languages. Moreover, the results of these methods are unsatisfactory, as much relevant semantic knowledge, such as semantic class, thematic role and syntactico-semantic knowledge like the semantic predicates, are not taken into account. We must acknowledge that this kind of knowledge is rare in most of the lexical resources. Recently, the International Organization for Standardization (ISO) has published the Lexical Markup Framework (LMF) ISO-24613 norm for the development of lexical resources. This norm provides, for each meaning of a lexical entry, all the semantic and syntactico-semantic knowledge in a fine structure. Profiting from the availability of LMF-standardized dictionaries, we propose, in this paper, a generic method that enhances the measure of sentence similarity by applying semantic and syntactico-semantic knowledge. An experiment was carried out on Arabic, as this language is processed within our research team and an LMF-standardized Arabic dictionary is at hand where the semantic and the syntactico-semantic knowledge are accessible and well structured. Moreover, the experiments yielded better results, showing a high correlation with human ratings.

The PRC Adaptive Knowledge-based Text Understanding System (PAKTUS) is an environment for developing natural language understanding (NLU) systems. It uses a knowledge-based approach in an integrated hybrid architecture based on a factoring of the NLU problem into its lexi-cal, syntactic, conceptual, domain-specific, and pragmatic components. The goal is a robust system that benefits from the strengths of several NLU methodologies, each applied where most appropriate. PAKTUS employs a frame-based knowledge representation and associative networks throughout. The lexical component uses morphological knowledge and word experts. Syntactic knowledge is represented in an Augmented Transition Network (ATN) grammar that incorporates rule-based programming. Case grammar is used for canonical conceptual representation with constraints. Domain-specific templates represent knowledge about specific applications as patterns of the form used in logic programming. Pragmatic knowledge may augment any of the other types and is added wherever needed for a particular domain. The system has been constructed in an interactive graphic programming environment. It has been used successfully to build a prototype front end for an expert system. This integration of existing technologies makes limited but practical NLU feasible now for narrow, well-defined domains.

The purpose of this paper is to analyse Open Design from a perspective of knowledge integration in semiotic manufacturing integration. It proposes a framework for analysis based on the idea of distinguishing syntactical knowledge from semantic and pragmatic knowledge, as a conceptual tool to assess the issues involved. It reviews Free Software and Open Source Software as the origins of Open Design, and presents a brief survey of the current expression of Open Design in the Web. It analyses prospects for manufacturing integration set forth by Open Design at the inter-enterprise and market levels.

Public Interest Technology (PIT) is an emerging field focused upon ensuring that innovative technologies are designed, distributed, and mediated for the well-being of the public. PIT seeks to educate technologists, engineers, and researchers to be cognizant of the public impact of their work and to train policymakers to equitably mediate the scope and reach of technology. However, the reach and impact of PIT work can be further enhanced through theoretical and pragmatic Science, Technology, and Society (STS) knowledge. For the past four years, I have worked on a large-scale change initiative in an electrical and computer engineering (ECE) department. An in-depth qualitative study of the department revealed several challenges within the department regarding identity, sense of belonging, invisible work, and career trajectory. As a scholar-practitioner, I employed STS theory and pragmatic knowledge to create three critically crafted interventions to address these challenges. First, STS knowledge was utilized to investigate the challenges within the department and understand how these challenges affected the multiple stakeholders and the culture of the ECE department. Second, pragmatic STS knowledge was utilized to create projects to shift the boundaries within the engineering department that limited the professional, academic, and personal opportunities of the engineering student. The purpose of this paper is to illustrate how my application of STS theoretical and pragmatic scholarship to address the culture of an electrical and computer engineering department can contribute to the field of PIT.

Sentence shifts often occur when a sentence in the source language cannot be directly translated into the target language without altering the meaning or message of the source language. This research investigates sentence-level and its types of transpositions in The Book You Wish Your Parents Had Read and examines their influence on translation quality in terms of accuracy, acceptability, and readability. A qualitative method with a single-embedded case study design was used to gather the data from transposed sentences. Findings revealed a predominance of downward rank shifts (189 instances) over upward rank shifts (15 instances), indicating the translator’s tendency to simplify complex source structures into more concise target-language forms. The translation quality achieved high scores in accuracy (2.9) and readability (2.9), suggesting that the intended meaning was preserved and the text remained easy to comprehend. However, acceptability scored slightly lower (2.7), implying that some lexical and syntactic choices did not fully conform to the cultural and linguistic norms of the target language. With an overall mean score of 2.8, the translation is categorised as “good” but could be improved through refined vocabulary selection, enhanced idiomatic expressions, and greater cultural adaptation. These findings highlight the functional role of transposition in balancing semantic fidelity, naturalness, and clarity in English–Indonesian translation.

The higher output quality of neural machine translation (NMT) has fostered research into the possibility of using it for literary texts, leaving the translator the finishing touch through post-editing. However, as previous studies have shown (Moorkens et al.,, 2018; Guerberof-Arenas and Toral, 2020, 2022), the creativity that characterises literary translators’ reading and writing is hindered by the predominantly literal MT output. While subscribing to the idea of leaving the human translator in charge of literary texts, our article tests another use of technology—namely aligners and NMT to assist in the revision of previously published translations which show good quality and relevance to today’s world, yet contain factual errors, omissions, misinterpretations and dated lexical or syntactic choices. Two chapters from Sinclair Lewis’ The Job (1917) and Rachel Carson’s Silent Spring (1962) were aligned with existing translations (Italian for the former, published in 1955 and 1970, and Finnish for the latter, published in 1963) and with the output produced by DeepL Pro to see whether some of its segments could replace the parts to be revised. Then, professionals working in literary translation were asked if 1) they were in favour of the use of technologies in the revision process, and 2) if the method we proposed was viable.

We design a new technique for the distributional semantic modeling with a neural network-based approach to learn distributed term representations (or term embeddings) – term vector space models as a result, inspired by the recent ontology-related approach (using different types of contextual knowledge such as syntactic knowledge, terminological knowledge, semantic knowledge, etc.) to the identification of terms (term extraction) and relations between them (relation extraction) called semantic pre-processing technology – SPT. Our method relies on automatic term extraction from the natural language texts and subsequent formation of the problem-oriented or application-oriented (also deeply annotated) text corpora where the fundamental entity is the term (includes non-compositional and compositional terms). This gives us an opportunity to changeover from distributed word representations (or word embeddings) to distributed term representations (or term embeddings). The main practical result of our work is the development kit (set of toolkits represented as web service APIs and web application), which provides all necessary routines for the basic linguistic pre-processing and the semantic pre-processing of the natural language texts in Ukrainian for future training of term vector space models.

La lignée mastocytaire RBL : modèle expérimental in vitro et application en pharmacologie clinique

This research investigated children's ability to acquire semantic and syntactic knowledge of derived nominals and derived adjectives in the context of short passages. The study also investigated the relation of morphological awareness and the ability to acquire knowledge of derived words in context. A total of 106 children in Grade 4 were given an author-created evaluation, the Derivational Acquisition in Context Evaluation (DAICE), which consisted of several short passages. Each passage contained two instances of a target base word followed by one instance of a derived form of the base word. A multiple-choice measure was administered to assess the children's knowledge of 12 derived words (6 nominals and 6 adjectives). A measure of morphological awareness was given to assess the children's relational knowledge between root words and their suffixes. No significant differences were found for overall performance on derived nominals and derived adjectives. However, the acquisition of semantic and syntactic knowledge was dependent on target word type. A significant relation was found between performance on the measure of morphological awareness and performance on derived nominals. This research provided evidence for fast mapping of derived nominals and derived adjectives within the context of short passages. Future directions of related research are suggested.

In view of the evolution of Chinese and U.S. American research policy in the past 60 years, the two countries have undergone different stages of development. The S&T policy system in the United States was gradually built up since World War II, the benefit of research freedom adding to academic interest. The intervention and support for science and technology from the government has been gradually strengthened, promoting S&T development. Since the 1990s, the U.S. government has defined the five functions of science as serving national objectives, emphasizing S&T progress to promote sustainable economic and social development, and continued innovation to maintain the U.S. American leadership position. Although the country has experienced a number of economic or financial crises, the U.S. government's investment in basic research has followed a course of rapid growth, treating basic research as a source of innovation and power. The Chinese S&T system was established upon and developed on a very weak basis after the establishment of the new Chinese republic. In the 1950s, the recovery and establishment of the S&T system almost fully emulated the Soviet model. Up to the late 1950s, With the successful implementation of the 12-year S&T development plan, the key tasks and key engineering projects drove the development of the basic disciplines and initially established the basic research system in China. The Cultural Revolution decade made Chinese S&T undertakings grind to a standstill, further widening the gap in relation to advanced global levels. With the reform and opening up in 1979, the Chinese S&T system was restored again. Especially since the 1990s, the state increasingly invested in basic research more and more significantly, and S&T development in China entered a fast track of development while basic research as the source of innovation received more attention from government and community.

Human beings appear to be very particular information processing systems whose outstanding plasticity and capability to cope with changing environmental conditions (adaptation) is essentially tied to their use of natural languages in communication to acquire knowledge (learning). Their knowledge based processing of information makes them cognitive,and their sign and symbol generation, manipulation, and understanding capabilities render them semiotic. Semiotic cognitive information processing (SCIP) is inspired by information systems theory according to which living systems process and structure environmental data according to their own structuredness. When these processes are modeled as operating on structures whose representational status is not so much a presupposition to but rather a result from such processing, then the resulting models — being able to simultaneously instanciate, create and/or modify these structures — may attain a quality of sign and symbol understanding which may computationally be realized. This quality will in the sequel be studied and identified as a particular form of knowledge acquisition or learning whose results can be visualized as incremental dynamics of structure formation. Its formal delineation, operational specification, and algorithmic implementation allows for experimental testing of the SCIP system’s capability for meaning constitution from natural language texts without prior morphological, lexical, syntactic and/or semantic knowledge.

Bridging knowledge boundaries among project team members is essential to prevent delays or complete failure of software development projects. Prior researchers have reported that software prototypes can be used to help bridge knowledge boundaries between team members in traditional software development settings, yet their use in an agile development setting remains unexplored. Agile development centers the interactions between team members on emerging representations of the prototype whose properties are prone to change over time. Therefore, we conducted an in‐depth study of an agile development project to enhance our understanding on how software prototypes are used as boundary objects in a distributed team setting. Our analyses of team member interactions during 46 virtual meetings that took place over a period of 6 months revealed four different prototype use practices (exemplifying, contrasting, relating, framing) that were effective in bridging syntactic, semantic, or pragmatic knowledge boundaries. We also provide empirically grounded evidence of how variations in object properties can afford different use practices, how the use practices take advantage of these properties, and how object properties are reshaped through these use practices as different types of knowledge boundaries are bridged. These findings bear important implications for research on prototypes as boundary objects in software development in particular and boundary object use in general.