Quantification

This article describes a corpus-based investigation of quantifier scope preferences. Following recent work on multimodular grammar frameworks in theoretical linguistics and a long history of combining multiple information sources in natural language processing, scope is treated as a distinct module of grammar from syntax. This module incorporates multiple sources of evidence regarding the most likely scope reading for a sentence and is entirely data-driven. The experiments discussed in this article evaluate the performance of our models in predicting the most likely scope reading for a particular sentence, using Penn Treebank data both with and without syntactic annotation. We wish to focus attention on the issue of determining scope preferences, which has largely been ignored in theoretical linguistics, and to explore different models of the interaction between syntax and quantifier scope.

In this study I investigate how artificial intelligence (AI) identifies and analyzes two types of linguistic ambiguity: structural and semantic. I also seek to bridge the gap between theoretical linguistics and recent advancements in AI. I examine examples of linguistically ambiguous sentences using two AI applications, ChatGPT-4o and Google Gemini. Using a comparative analysis, I evaluate their comprehension and capabilities in resolving linguistic ambiguities. To enhance the evaluation, I use some prompts to elicit a second interpretation in cases where the applications fail to detect either structural or semantic ambiguities. The results reveal that ChatGPT-4o is more effective at identifying such ambiguities than is Google Gemini, which often struggles with ambiguous sentences. Semantic ambiguities, particularly those involving modals such as “must” and “ought to,” or scope issues with quantifiers such as “every,” pose greater challenges for both applications than do structural ambiguities. Despite these challenges, both ChatGPT-4o and Google Gemini demonstrate useful disambiguation strategies, including referencing the context of the utterance and paraphrasing ambiguous sentences to clarify their meaning. The study highlights the significant potential of AI in imitating human capabilities related to sentence comprehension and the resolution of linguistic ambiguities.

Sentences containing quantifiers are often ambiguous, as quantificational DPs interact with one another, and with wh ‐phrases, negation, and adverbials. This kind of ambiguity is an ambiguity with respect to the scope of the quantifiers. However, sentences containing several quantifiers or operators are not always scope ambiguous: compare, for example, the ambiguous Some of us always showed up with the unequivocal Always, some of us showed up . Thus, the scope of quantifiers is not essentially free; there is a complex pattern of ambiguous and unequivocal sentences containing quantifiers and operators. An adequate analysis of scope ambiguities should be able to predict this complex pattern in a principled way, which should encompass a sensible view of the interface between syntax and semantics. According to the dominant view in generative grammar, scope relations can be deduced from the c‐command relations on Logical Form, where quantifiers are assigned scope by quantifier raising. Variants of and alternatives to this dominant view have been proposed, for some partly motivated by the scope facts in languages other than English (in particular, Mandarin Chinese and Hungarian have been taken into consideration), and for some partly motivated by a more thorough look at the (English) data and/or by modification or substitution of the basic framework. It became apparent that quantifier scope relations crucially depend on certain properties of the quantifiers, whose exact nature is not yet settled, however. The interplay of these properties seems to determine the complex pattern of ambiguous and unequivocal sentences containing quantifiers and operators. The challenge is to uncover the nature of these scope‐relevant properties and account for their interplay within a general conception of the interface between syntax and semantics.

In this paper we use German data to evaluate configurational and multi-factor approaches to quantifier scope. Configurational theories derive scope relations syntactically at the level of Logical Form; semantic and pragmatic factors are either built into the syntactic representation or ignored, at least during the first derivational step. By contrast, multi-factor approaches consider syntactic, semantic and pragmatic properties of quantifiers as multiple constraints affecting quantifier scope. We examined predictions for quantifier scope in German of the configurational theory by Frey (1993) and of the multi-factor account by Pafel (2005). These fundamentally different approaches were tested in a series of picture verification experiments to assess scope preferences in doubly quantified German sentences. The results show that at least three factors affect the preferred scope. Our findings are neither fully consistent with Frey’s configurational theory nor with Pafel’s multi-factor approach; both theories made incorrect predictions for German doubly quantified sentences with a subject-before-object word order. For object-before-subject sentences, however, the experimental data by and large support the predictions of Pafel’s (2005) multi-factor approach.

For practical research in natural language processing, it is indispensabh . to develop a large scale sentantic dictionary for computers. It is especially important to improve the tecbuiques ibr compiling semantic dictionaries torn natural language texts such as those in existing human dictionaries or in large corpora. 11owever } there are at least two ditIicultics in analyzing existing texts: tbe problem of syntactlc ambiguities and the problem of polysemy. Our approach to solw, these difficulties is to make use of translation exam- pies in two dis[inet languagcs that haw (luitc different sylltactic structures and word mealtings. The reason we took tbis approach is that in many cmcs both syn tactic alrd scnlantic ambiguities arc resolved by comparing analyzed results from botb lauguages. In this paper, we propose a method ibr resolving the syntac tie ambiguities of translation examples of bihngual corpora and a method for acquiring lexical knowledge, sucb a.s case frames of verbs and attribute sets of noutts.

This research discusses how both theoretical linguistics and computational linguistics process natural language by clarifying the foundations which the two fields are based on. Also, this study attempts to analyze the differences and convergences that can lay a clear foundation for linguistics to be computed by studying the goals and operational procedures in different language systems such as morphology, phonetics, syntax and semantics. The research consists of an introduction and two sections. The first one is theoretical, explaining the linguistic theory with its two parts: Arabic and Western. Then the concept of computational linguistics and its role in studying, understanding, and generating the natural language processing is explored. The second section is applied, discussing the difference between theoretical linguistics and computational theories in terms of their starting points, their objectives, and the way they process linguistic systems. The research aims to represent computational procedures using linguistics in computers in order to achieve the goals of users of natural language in an ideal way. Keywords: Linguistics theory, Computational linguistics, Rule, Procedure, Computing.

The aim of this paper is to present parts of our system [2], which is to construct a database out of a narrative natural language text. We think the parts are of interest in their own. The paper consists of three sections:(1) We give a detailed description of the PROLOG - implementation of the parser which is based on the theory of lexical functional grammar (LFG). The parser covers the fragment described in [1,§;4]. I.e., it is able to analyse constructions involving functional control and long distance dependencies. We will to show that- PROLOG provides an efficient tool for LFG-implementation: a phrase structure rule annotated with functional schemata likes S → NP VP is to be interpreted as, first, indetifying the special grammatical relation of subject position of any sentence analyzed by this clause to be the NP appearing in it, and second, as identifying all grammatical relations of the sentence with those of the VP. This universal interpretation of the LFG-metavariables ↑ and ↓ corresponds to the universal quantification of variables appearing in PROLOG-clauses. The procedural semantics of PROLOG is such that the instantiation of the variables in a clause is inherited from the instantiation given by its subgoals, if they succeed. Thus there is no need for a separate component which solves the set of equations obtained by applying the LFG algorithm.-there is a canonical way of translating LFG into a PROLOG programme.(II) For the semantic representation of texts we use the Discourse Representation Theory developped by Hans Kamp. At present the implementation includes the fragment described in [4]. In addition it analyses different types of negation and certain equi- and raising-verbs. We postulate some requirements a semantic representation has to fulfill in order to be able to analyse whole texts. We show how Kamp's theory meets these requirements by analyzing sample discourses involving anaphoric NP's.(III) Finally we sketch how the parser formalism can be augmented to yield as output discourse representation structures. To do this we introduce the new notion of 'logical head' in addition to the LFG notion of 'grammatical head'. The reason is the wellknown fact that the logical structure of a sentence is induced by the determiners and not by the verb which on the other hand determiners the thematic structure of the sentence. However the verb is able to restrict quantifier scope ambiguities or to induce a preference ordering on the set of possible quantifier scope relations. Therefore there must be an interaction between the grammatical head and the logical head of a phrase.

Preface Typographical conventions 1. Language, speech and writing 2. In defence of (so-called) autonomous linguistics 3. Linguistic theory and theoretical linguistics 4. Natural, non-natural and unnatural languages: English, Urdu and other abstractions 5. The origin of language, speech and languages 6. Phonemic and non-phonemic phonology: some typological reflections 7. Towards a 'notional' theory of the 'parts of speech' 8. Deixis as the source of reference 9. Deixis and anaphora Appendix: the scientific study of language. Inaugural lecture, Edinburgh, 1965 Notes References Subject index Names index.

Anaphora in natural language processing and information retrieval

The concept of polarity is pervasive in natural language. It relates syntax, semantics and pragmatics narrowly (Giannakidou, in: Maienborn, von Heusinger, Portner (eds.), Semantics: an international handbook of natural language meaning, De Gruyter Mouton, Berlin, 2011; Israel in The grammar of polarity: pragmatics, sensitivity, and the logic of scales, Cambridge studies in linguistics, Cambridge University Press, Cambridge, 2014), it refers to items of many syntactic categories such as nouns, verbs and adverbs. Neutral polarity items appear in affirmative and negative sentences, negative polarity items cannot appear in affirmative sentences, and positive polarity items cannot appear in negative sentences. A way of reasoning in Natural Language is through Natural Logic (van Benthem in Essays in logical semantics, vol. 29 of Studies in linguistics and philosophy, Reidel, Dordrecht, 1986; Language in action: categories, lambdas, and dynamic logic, vol. 130 of Studies in logic, Elsevier, Amsterdam, 1991). This logic is based on the concept of polarity in order to make the meaning of a sentence weaker o stronger without changing its truth value. There exist many proposals to compute polarity in the Natural Logic context, the most widely known are the ones by: van Benthem (1986, 1991), Sanchez-Valencia (Studies on natural logic and categorial grammar, Ph.D. thesis, Universiteit van Amsterdam, 1991), Dowty (Proceedings of the 4th conference on semantics and theoretical linguistics, Cornel University, CLC Publications, Rochester, 1994), and van Eijck (in: ten Cate, Zeevat (eds.), 6th international Tbilisi symposium on logic, language, and computation, Batumi, Georgia, Springer, 2007). If Natural Logic is going to be used, as an inferential mechanism between text fragments, in Natural Language Processing applications such as text summarization, question answering, and information extraction, it is a priority to know what the existing relationship among the aforementioned algorithms is; for example, to implement the most general. We show in this paper the equivalence among the analyzed algorithms, filling a gap in Natural Logic research, particularly in computing polarity, and the soundness of their algorithms.

Generative syntax is a major subfield of generative grammar, an outgrowth of American structuralism in its insistence on rigorous formal modeling of linguistic patterns. Generative syntax breaks with the structuralist tradition by attaching no significance to discovery procedures and by not seeing accurate description of individual languages as the ultimate goal of linguistics. Rather, the goal is to extract cross-linguistic commonalities in order to characterize a core system of grammar shared by all natural languages. This core system, called Universal Grammar, is seen as a system of primitives and principles that determine how these primitives can be put together to form complex linguistic structures by recursive structure-building operations that will construct an infinite set of sentences and characterize the relations between them. The most interesting and controversial feature of generative grammar lies on the conceptual side. By seeing Universal Grammar as rooted in an innate language faculty specific to humans, generative grammar places the study of language within the cognitive sciences. Since its inception in the 1950s, the generative theory of syntax has spawned a number of approaches that differ from one another with respect to many theoretical assumptions but still seem to agree that natural language syntax is rooted in a species-specific cognitive capacity dedicated to language. These various theoretical developments can be grouped into two major categories for expository purposes. On the one hand, lexical-functional grammar (LFG) and head-driven phrase structure grammar (HPSG) develop architectures sufficiently different from that of Chomskyan generative syntax to be regarded as distinct branches of the generative enterprise. On the other hand, five different stages can be identified within the line of development that has continued to be informed by Noam Chomsky’s own work: early generative syntax, 1957–1965; the aspects theory (also known as the standard theory), 1965–1973; the (revised) extended standard theory, roughly 1973–1981; government and binding theory, 1981–1993; and minimalism, 1993 onward, which must in the early 21st century be regarded as the mainstream paradigm. The research questions that arise concern both the internal organization of the syntactic component (levels of representation) and the interaction between syntax and other components of grammar, such as morphology, phonology, and semantics. These questions are intimately linked to the effort to isolate the essential properties of syntactic structures and of the syntactic operations and the general principles that govern their application.

In the three decades of its existence, the annual Going Romance conference has turned out to be the major European discussion forum for theoretically relevant research on Romance languages where current theoretical ideas about language in general and about Romance languages in particular are exchanged. The twenty-ninth Going Romance conference was organized by the Radboud University and took place in December 2015 in Nijmegen. The present volume contains a selection of 18 peer-reviewed articles dealing with syntax, phonology, morphology, semantics and acquisition of the Romance languages. They represent the wide range of topics at the conference and the variety of research carried out on Romance languages within theoretical linguistics and will be of interest to scholars in Romance and in general linguistics.

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.

The present article is devoted to the investigation of structural ambiguity in English. Investigation has been carried out on specifically selected sentences containing structural ambiguity. Ambiguity is the inner feature of every language. We come across the process of ambiguity in both spoken and written discourse. Ambiguity has been a topic of interest not only for linguists but also for psycholinguists. The interest in the study of the decoding of the meaning of the sentence can be considered a new sphere in general linguistics. Here a great attention has been paid to the experimental-phonetic analysis of the ambiguous sentences.

This paper aims to provide a brief overview of the past studies on negation, starting from Jespersen (1917) to the more recent ones that are elaborated within the generative grammar and based on Klima (1964). In the work of Klima (1964) and all subsequent works on the grammar of negation, such as Lasnik (1972), Pollock (1989), Laka (1990), Progovac (1994, 2005), Haegeman (1995) and Zanuttini (1997), attention has been confined to a formal means for expressing the meaning of sentence negation. Linguists have been trying to establish whether natural languages have a universal formal feature that corresponds to the meaning of negation. As a formal representation of negation they proposed the Neg symbol, the NegP projection and the Spec-Head relation. Furthermore, the focus of their studies has been onthe licensing of negative polarity items (henceforth, the NPI licensing), relative scope of negation and c-command. T he NPI licensing has been examined in terms of whether the licensing principles mainly syntactic in nature, semantic-pragmatic, or both, and whether it is autonomous in the sense that functions only for the NPI licensing or it is tied to the principles of other modules. Studies on relative scope of negation and c-command have been trying to determine to what extent, if at all, syntactic structures are responsible for the scope of negation. Keywords: generative grammar, Jesperson (1917), Klima (1964), formal representation of negation, Negsymbol, NegPprojection, Spec-Head relation, NPI licensing, scope of negation and c-command