A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail

Descriptive pronouns revisited: the semantics and pragmatics of identification-based descriptive interpretations

This thesis confronts the semantic/pragmatic issues raised by identification - based descriptive uses of pronouns. The phenomenon, also known as deferred uses (Nunberg, 1993), arises when the correct understanding of a pronoun is dependent on the identification of a specific individual in the context that provides it with a descriptive (as opposed to a singular) interpretation. Moreover, the identification of the salient individual makes the interpretation available in a rather indirect way. For example, by pointing at a huge footprint in the sand and uttering ‘He must be a giant’, the speaker can convey the proposition that the footprint maker must be a giant, where the mental representation footprint (necessary for identification) and the representation the footprintmaker (the pronoun’s interpretation) are not identical. These uses also display interesting properties when it comes to their ability to provide antecedents for other pronouns. As such, they are at the cross-road of many topics in philosophy of language and linguistics, including indexicality, anaphora, and figurative uses of language (metonymy). In this thesis, I propose that the data is best accounted for by a combination of relevance-theoretic pragmatics (Sperber and Wilson 1995, Carston 2002), certain motivated assumptions about visual information processing, and the grammar formalism of Dynamic Syntax (Kempson et al 2001; Cann et al 2005). DS models pronouns as encoding procedures that introduce a variable-like entity (e.g. a metavariable), which needs to be replaced by a semantic value (of the appropriate type), allowing for descriptive constituents, which emerge as a result of relevance-driven processes of identification and inference, to provide the pronoun with the relevant descriptive interpretation. Alternatively, the pronoun can be replaced by a singular value that communicates a descriptive proposition as an implicature. The context and the pronominal form used determine which of these approaches is the best suited

Anaphora Resolution and Text Retrieval

Empirical approaches based on qualitative or quantitative methods of corpus linguistics have become a central paradigm within linguistics. The series takes account of this fact and provides a platform for approaches within synchronous linguistics as well as interdisciplinary works with a linguistic focus which devise new ways of working empirically and develop new data-based methods and theoretical models for empirical linguistic analyses

THE ROLE OF NON-LINGUISTIC COGNITIVE DEVELOPMENT AND LANGUAGE-SPECIFIC MORPHOLOGICAL PROPERTIES IN THE FIRST LANGUAGE ACQUISITION OF DEMONSTRATIVES

This dissertation investigates children’s comprehension of demonstratives, such as this and that in English. As deictic spatial expressions, the interpretation of demonstratives is context-dependent: a proximal demonstrative (e.g., this) picks out the entity near the speaker, while a distal demonstrative (e.g., that) picks out the entity apart from the speaker; crucially, the entity-speaker distance is determined by the speaker’s perspective, which varies across contexts. Studies have shown that children tend to be non-adult-like when comprehending demonstratives uttered by a speaker who has a different perspective from their own (e.g., Clark & Sengul, 1978; Zhao, 2007). To better understand children’s comprehension of demonstratives, this dissertation explores (i) the cognitive factors which might hinder children’s adult-like knowledge, and (ii) the language-specific factors which might improve children’s demonstrative comprehension. This dissertation first discusses Theory of Mind (ToM) and Executive Function (EF) and how the development of each may hinder children’s comprehension of demonstratives. Successful comprehension of demonstratives requires the listener to incorporate the speaker’s perspective, in which cognitive abilities may play a role. It has been suggested that children’s non-adult-like demonstratives may be related to their still-developing ToM (de Villiers, 2007) and EF (Nilsen & Graham, 2012). Two experiments directly tested this hypothesis with English-speaking and Chinese-speaking children, respectively. Both experiments utilized two demonstrative comprehension linguistic tasks, and two cognitive tasks measuring ToM and EF, respectively. The results from both experiments suggest that children’s successful comprehension of demonstratives may be related to their ToM development, but not EF. This dissertation then examines whether a language-specific morphological representation of demonstratives may interact with children’s comprehension in a way that prevents them from committing non-adult-like comprehension. Demonstratives in Mandarin Chinese are of particular interest because they typically occur with classifiers. Classifiers are semantically dependent on their associated referents; interestingly, classifiers are known to facilitate adults’ sentence processing (e.g., Hsu, 2006; Wu, Kaiser, & Andersen, 2009). Thus, this dissertation examined whether and to what extent the classifier may improve Chinese-speaking children’s demonstrative comprehension. Results reveal that the classifier semantics improves children’s demonstrative comprehension, particularly when the classifier semantics itself is sufficient to identify the referent. In sum, the results of the studies discussed in this dissertation suggest that both cognitive factors and language-specific factors play an important role in children’s demonstrative comprehension

Linguistic Representation of Problem Solving Processes in Unaided Object Assembly

This thesis investigates the linguistic representation of problem solving processes in data recorded during unaided object assembly. It combines traditional approaches of analyzing verbal protocols with the recent approach of Cognitive Discourse Analysis

Anaphora Resolution and Text Retrieval

Empirical approaches based on qualitative or quantitative methods of corpus linguistics have become a central paradigm within linguistics. The series takes account of this fact and provides a platform for approaches within synchronous linguistics as well as interdisciplinary works with a linguistic focus which devise new ways of working empirically and develop new data-based methods and theoretical models for empirical linguistic analyses

Crossroads between contrastive linguistics, translation studies and machine translation

Contrastive Linguistics (CL), Translation Studies (TS) and Machine Translation (MT) have common grounds: They all work at the crossroad where two or more languages meet. Despite their inherent relatedness, methodological exchange between the three disciplines is rare. This special issue touches upon areas where the three fields converge. It results directly from a workshop at the 2011 German Association for Language Technology and Computational Linguistics (GSCL) conference in Hamburg where researchers from the three fields presented and discussed their interdisciplinary work. While the studies contained in this volume draw from a wide variety of objectives and methods, and various areas of overlaps between CL, TS and MT are addressed, the volume is by no means exhaustive with regard to this topic. Further cross-fertilisation is not only desirable, but almost mandatory in order to tackle future tasks and endeavours, and this volume is committed to bringing these three fields even closer together

Students´ language in computer-assisted tutoring of mathematical proofs

Truth and proof are central to mathematics. Proving (or disproving) seemingly simple statements often turns out to be one of the hardest mathematical tasks. Yet, doing proofs is rarely taught in the classroom. Studies on cognitive difficulties in learning to do proofs have shown that pupils and students not only often do not understand or cannot apply basic formal reasoning techniques and do not know how to use formal mathematical language, but, at a far more fundamental level, they also do not understand what it means to prove a statement or even do not see the purpose of proof at all. Since insight into the importance of proof and doing proofs as such cannot be learnt other than by practice, learning support through individualised tutoring is in demand. This volume presents a part of an interdisciplinary project, set at the intersection of pedagogical science, artificial intelligence, and (computational) linguistics, which investigated issues involved in provisioning computer-based tutoring of mathematical proofs through dialogue in natural language. The ultimate goal in this context, addressing the above-mentioned need for learning support, is to build intelligent automated tutoring systems for mathematical proofs. The research presented here has been focused on the language that students use while interacting with such a system: its linguistic propeties and computational modelling. Contribution is made at three levels: first, an analysis of language phenomena found in students´ input to a (simulated) proof tutoring system is conducted and the variety of students´ verbalisations is quantitatively assessed, second, a general computational processing strategy for informal mathematical language and methods of modelling prominent language phenomena are proposed, and third, the prospects for natural language as an input modality for proof tutoring systems is evaluated based on collected corpora