Default Semantics

This chapter contains the exposition of the new theory of default Semantics. It is proposed that the problems with compositionality of meaning can be solved when compositionality is viewed ‘one level higher’ as pertaining to merger representations — semantic representations that are a product of the synthesis of information about meaning coming from various sources, such as word meaning, sentence structure, conscious pragmatic inference, cognitive defaults, and social-cultural defaults. Cognitive principles underlying such mergers are proposed, and the foundations of a metalanguage for formalization in relational semantics are specified.

Kecskes: Linguistic underspecification of utterance content is widely accepted across different frameworks, including the neo-Gricean approaches (cf. Horn 2005; Levinson 2000) and relevance theory (Carston 2002, 2005; Sperber & Wilson 1986, 1995). There is also an agreement that if linguistic underdeterminacy is given, pragmatic inference is required if a hearer is to recover a speaker's meaning successfully. In your Default Semantics, you reject the idea of underdetermined semantic representation, and offer an alternative approach in which semantic representation is established with the help of intentions in communication. This means that intentions “intrude” into the semantic representation, and the semantic and pragmatic components are interwoven. What are the advantages of this one-level semantics as opposed to the modular view? It can be argued that, in a way, Default Semantics is also a modular approach because intentions can be considered preverbal thoughts generated in the “conceptualizer” and linguistically shaped in the “formulator” using Levelt's terminology (Levelt 1989, 1999). Do you agree with this line of thinking?

This undergraduate thesis carries on as experimental study toward speaking ability at different self confidences. Since speaking needs correctness of pronunciation, form of the sentences uses and understandable dialogue. Here the students are expected to be able to speak in English with pronouncing an English word and the form of sentences well also making a better dialogue with their friends. To build the students speaking ability they can learn by practicing and repeating the words and structure of a sentence. The students speaking ability is also influenced by these self confidence. The purpose of this research is to find out the comparison average scores by applying Pair Discussion and Repetition Drill Technique at different self confidence. By applying the two techniques, students are expected to be able to speak confidently, pronounce the English word and along with the form of the sentences well. in this research, there are two classes of students the eleventh grade of SMA N 1 Rumbia as a sample. The finding of the result proves from experiment class which taught by Pair Discussion Technique find that students are able to pronounce and to form the sentences well, since this technique focus on discussion in pair as their practice to form the sentences and to pronounce of English words. While in the control class students taught by Repetition Drill Technique find that they are able to pronounce the sentence, since this technique focus on drill and repetition students are able to imitate the pronounce directly. But, by applying both of them, the students who have high and low self confidence have different result of speaking. It means that by applying two methods students can develop their speaking ability although in the different result of speaking ability. After conducting the research the researcher find that both Pair Discussion and Repetition Drill Technique are good to be applied for students in different self confidence, especially to develop the students’ speaking ability. The researcher expects to the English teachers have to be creative in conducting teaching learning technique so that students can interest in studying.

First published in 1981, Hans Kamp’s paper, “A theory of truth and semantic representation”, introduced Discourse Representation Theory, or DRT for short, which soon became one of the main frameworks in semantics and pragmatics, and in the meantime has been applied to an exceptionally wide range of phenomena, including anaphora, definites and proper names, quantification, tense and aspect, presupposition, implicatures, and attitude reports. DRT’s key innovations were two. First, it introduced a level of representation which was new to semantics. Kamp defined a formal language, with a model-theoretic interpretation, which served to represent the contents not only of individual sentences, but of entire discourses as well. Secondly, this formal language featured a new species of variables, called discourse referents, which were the keystone of DRT’s unified treatment of anaphoric pronouns. Discourse representations and discourse referents are the main topics of this commentary.KeywordsDiscourse representation theoryDiscourse referentsAnaphoraQuantificationConditionalsCompositionality

The purpose of this study was to determine the problems of Arabic sentence construction and its solutions for students of the Faculty of Science and Technology in the first semester of 2023 Maulana Malik Ibrahim State Islamic University Malang from the aspect of sentence structure (tarkib) during the learning of a special program for Arabic language development (PKPBA). This research method uses a qualitative-descriptive approach with case studies. The object of this research is first semester students in 2023, with a sample of 40 students. The data collection techniques used were observation and interview. Data analysis was carried out through three stages, data reduction, data presentation, and conclusions. Triangulation used to test the validity of the data is triangulation of sources and methods. The results showed that the problems of Arabic sentence structure are difficulties in arranging changes in the end of the sentence (i'rab), determining the order of words in the sentence, and determining the suitability of words in the sentence. The solutions include: studying Arabic grammar rules in depth, practicing making words in sentences continuously, and often reading Arabic texts carefully.

This paper describes a novel approach to linguistic mutual inference, which enables robots not only to linguistically interpret the motion patterns in the form of sentences but also to generate the motions from the sentences. The inference can be established based on two modules, the motion language model and the natural language model. The motion language model stochastically represents an association structure between symbols of motion patterns and the words in sentences assigned to the motion. This is a statistical model with a three layered structure of motion symbols, latent states and words. The natural language model statistically represents a structure of sentences based on word bigrams. The motion language model and the natural language model correspond to semantics and syntax respectively. An approach to the integration of motion language model with the natural language model allows the linguistic mutual inference for the robots. The two kinds of inference can be made by solving search problems, search for a sequence of words corresponding to a motion and search for a symbol of motion pattern corresponding to a sentence. The proposed approach to interpretation of motion patterns as sentences and generation of motion patterns from the sentences through the integration of motion language model with the natural language model is validated by an experiment on the human behavioral data.

This paper presents an automatically generated Intermediate Logic Form of WordNet’s glosses. Our proposed logic form includes neo-Davidsonian reification in a simple and flat syntax close to natural language. We offer a comparison with other semantic representations such as those provided by Hobbs and Extended WordNet. The Intermediate Logic Forms are straightforwardly obtained from the output of a pipeline consisting of a part-of-speech tagger, a dependency parser and our own Intermediate Logic Form generator (all freely available tools). We apply the pipeline to the glosses of WordNet 3.0 to obtain a lexical resource ready to be used as knowledge base or resource for a variety of tasks involving some kind of semantic inference. We present a qualitative evaluation of the resource and discuss its possible application in Natural Language Understanding.KeywordsDependency GraphWord Sense DisambiguationComputational LinguisticsLexical ResourceSyntactic ParsingThese 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.

Automatic evaluation of machine translation plays an important role in improving the performance of machine translation systems. In this paper, we firstly introduce three traditional methods of automatic evaluation, including BLEU, NIST and WER. All these methods are based on surface layer information of translations like vocabularies, so we do some studies on the evaluation method using the information of sentence structure. Because the Hierarchical Network of Concepts (HNC) theory thinks that sentence category and format transformations are two most important links in machine translation, we do some researches about sentence category and format transformations, and get the sentence structure information which is composed of sentence category information and format information of every sentence in the bilingual (Chinese and English) translation corpora. Then, considering the traditional methods above, we propose the method of automatic evaluation based on the information of sentence structure and have proved it effective by experiment.

Language grounding is broadly defined as the problem of mapping natural language instructions to robot behavior. To truly be effective, these language grounding systems must be accurate in their selection of behavior, efficient in the robot’s realization of that selected behavior, and capable of generalizing beyond commands and environment configurations only seen at training time. One choice that is crucial to the success of a language grounding model is the choice of representation used to capture the objective specified by the input command. Prior work has been varied in its use of explicit goal representations, with some approaches lacking a representation altogether, resulting in models that infer whole sequences of robot actions, while other approaches map to carefully constructed logical form representations. While many of the models in either category are reasonably accurate, they fail to offer either efficient execution or any generalization without requiring a large amount of manual specification. In this work, we take a first step towards language grounding models that excel across accuracy, efficiency, and generalization through the construction of simple, semantic goal representations within Markov decision processes. We propose two related semantic goal representations that take advantage of the hierarchical structure of tasks and the compositional nature of language respectively, and present multiple grounding models for each. We validate these ideas empirically with results collected from following text instructions within a simulated mobile-manipulator domain, as well as demonstrations of a physical robot responding to spoken instructions in real time. Our grounding models tie abstraction in language commands to a hierarchical planner for the robot’s execution, enabling a response-time speed-up of several orders of magnitude over baseline planners within sufficiently large domains. Concurrently, our grounding models for generalization infer elements of the semantic representation that are subsequently combined to form a complete goal description, enabling the interpretation of commands involving novel combinations never seen during training. Taken together, our results show that the design of semantic goal representation has powerful implications for the accuracy, efficiency, and generalization capabilities of language grounding models.

syntax is a semantic tree representation that lies between parse trees and logical forms. It abstracts away from word order and lexical items, but contains enough information to generate both surface strings and logical forms. Abstract syntax is commonly used in compilers as an intermediate between source and target languages. Grammatical Framework (GF) is a grammar formalism that generalizes the idea to natural languages, to capture cross-lingual generalizations and perform interlingual translation. As one of the main results, the GF Resource Grammar Library (GF-RGL) has implemented a shared abstract syntax for over 30 languages. Each language has its own set of concrete syntax rules (morphology and syntax), by which it can be generated from the abstract syntax and parsed into it. This paper presents a conversion method from abstract syntax trees to dependency trees. The method is applied for converting GF-RGL trees to Universal Dependencies (UD), which uses a common set of labels for different languages. The correspondence between GF-RGL and UD turns out to be good, and the relatively few discrepancies give rise to interesting questions about universality. The conversion also has potential for practical applications: (1) it makes the GF parser usable as a rule-based dependency parser; (2) it enables bootstrapping UD treebanks from GF treebanks; (3) it defines formal criteria to assess the informal annotation schemes of UD; (4) it gives a method to check the consistency of manually annotated UD trees with respect to the annotation schemes; (5) it makes information from UD treebanks available.

An adequate account of the nature of the so called 'propositional attitudes' has long been sought by philosophers of mind, and most theories which have been proposed have suffered from certain logical or epistemological defects or have been limited in scope. The issues centering around the nature of beliefs, desires, and the other attitudes pertain to two principal areas of investigation: the explication of a (psychological) theory concerning the objects of the propositional attitudes and the analysis of the logical structure of such sentences as those with the form 'A believes that p' or 'A wishes (desires) that p.' My main concern in this paper is with problems associated with the latter endeavor. Such problems in an important respect are more basic, for a successful attempt at characterizing the typical form of sentences that express propositional attitudes must be made before any theory about the objects of beliefs, desires, and other intentions can possibly take shape.1 The sentence Jones believes that there are unicorns has the typical form of the members of a class of sentences each one consisting of one or more names or denoting phrases followed by an intentional idiom of the kind 'believes that', 'desires that', 'wishes that', 'hopes that', 'fears that', 'says that', 'wonders whether', etc. followed by a complete sentence. Such complex sentences when taken as assertions have usually been seen as expressing a relationship (or a propositional attitude) of one kind or another between an object (usually a person) and a belief, desire, wish, hope, or other intention (or object of the propositional attitude). For the sake of facilitating the discussion regarding these sentences, I shall, with a certain degree of liberty, call such idioms consisting of the word 'that' preceded by a verb of intention 'proposing phrases'; 1 seek then an analysis of the role such phrases have in determining the logical form of sentences in which they occur. I will begin my account by specifying the logical requirements any such analysis must meet and then briefly examine some of the proposed analyses (each one of which I believe fails to satisfy at least one requirement) and then end by presenting and justifying my analysis, which, hopefully, does meet these requirements.

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.

This study aims to describe the acquisition of Indonesian children aged 5-6 years who are examined on the types of Indonesian sentences and patterns of Indonesian sentence structure obtained by Muhammad Ragil Satria Putra Agung. The method used is descriptive method. The form of qualitative research. The data source is Muhammad Ragil Satria Putra Agung and the research data are all the utterances produced by Muhammad Ragil Satria Putra Agung in the form of sentences according to the selected problem formulation. The data collection technique used is the conversational engagement technique, recording technique and note-taking technique on the data card. The technique of testing the validity of the data is the persistence of observations and the adequacy of references. The results showed that: declarative sentences (news) as many as 272 sentences, imperative sentences (commands) as many as 32 sentences, interrogative sentences (questions) as many as 71 sentences. Based on the data classification, the patterns of Indonesian sentence structure obtained by Muhammad Ragil Satria Putra Agung include: (SP) 246 patterns, (SPO) 8 patterns, (SP-Pel) 138 patterns, (SP-Ket) 29 patterns, (SPO- Note) 3 patterns, (SPO-Mop) 5 patterns, and (SP-Pel-Ket) 14 patterns. From the results of data analysis, the most dominant sentences obtained are declarative sentences, which are 272 sentences, while the sentence structure pattern that is most widely used is the Subject-Predicate (SP) pattern, which is 246 patterns. Keywords: Indonesian language acquisition, children aged 5-6 years, psycholinguistics.

Abstract: The Book of Daqoiqul Akhbar is a famous book and studied in various Islamic boarding schools. The contents of this book cause a lot of controversy because it discusses matters related to death and the afterlife. the study of the contents of this book related to sentence structure is still rarely done. Additive compound is every two nouns with a partial ratio between them, so the name of the first is called mudhaf and the name of the second is mudhaf ilaih. The rule of the genitive depends on its place in the sentence, and the rule of the genitive is never affixed. Therefore, this study aims to analyze the Tarkib Idhofi or Additive Compound in the book and the meaning contained in the use of this type of sentence structure. This type of research is content analysis research which can also be classified as qualitative research. the results of this research showed that: First, Daqoiqul Akhbar has this type of additive compound of 2053 words, 13 non-pure auxiliary words, and 2040 purely additional ones, either in the singular or in the sentence form. And second, the benefits of the additive compound are two things, (1) for definition and (2) for specialization, there are 1085 words for the benefit of definition and 202 words for the benefit of specialization, 26 words equal in pronunciation and meaning. The meanings included in the additional composition of 2053 words, 1423 words include the meaning "اللام", 423 words include the meaning "مِن", and 207 words contain the meaning "في".

Patients suffering from Alzheimer's disease (AD) have severe difficulties in tasks requiring the use of semantic knowledge. The semantic deficits associated with AD have been extensively studied by using behavioral methods. Many of these studies indicate that AD patients have a general deficit in voluntary access to semantic representations but that the structure of the representations themselves might be preserved. However, several studies also provide evidence that to some extent semantic representations in AD may in fact be degraded. Recently, a few studies have utilized event-related brain potentials (ERPs) that are sensitive to semantic factors in order to investigate the electrophysiological correlates of the semantic impairment in AD. Interest has focused on the N400 component, which is known to reflect the on-line semantic processing of linguistic and pictorial stimuli. The results from studies of N400 changes in AD remain somewhat controversial: Some studies report normal or enlarged N400 components in AD, whereas others report diminished ones. One issue not reported in previous studies is whether word-elicited ERPs other than N400 remain normal in AD. In the present study our aim was to find out whether the ERP waveforms N1, P2, N400, and Late Positive Component (LPC) to semantically congruous and incongruous spoken words are abnormal in AD and whether such abnormalities specifically reflect deficiencies in semantic activation in AD. Auditory ERPs from 20 scalp sites to semantically congruous and incongruous final words in spoken sentences were recorded from 17 healthy elderly adults and 9 AD patients. The early ERP waveforms N1 and P2 were relatively normal for the AD patients, but the N400 and LPC effects (amplitude difference between congruous and incongruous conditions) were significantly reduced. We interpret the present results as showing that semantic-conceptual activation and other high-level integration processes are defective in AD. However, a word congruity effect earlier than N400 (phonological mismatch negativity), reflecting lexical selection processes, is at least to some extent preserved in AD.