The Influence of Pseudo-relatives on Attachment Preferences in Spanish

This paper presents the results from an off-line experiment on the extent to which the availability of pseudo-relatives modulates attachment preferences in Spanish. Participants were presented with sentences in which different syntactic and semantic factors had been manipulated to allow for either both a pseudo-relative (PR) and a relative-clause (RC) reading or a RC reading only. All the experimental items included two potential antecedents with which the constituents of interest could be associated. The experimental items can be divided into four groups: group 1 consists of stimuli allowing for a double reading in direct object position, and groups 2, 3 and 4 consist of stimuli containing RCs in prepositional complement position, preverbal subject position, and postverbal subject position, respectively. A stronger preference for the higher antecedent was expected in the first group of experimental items. The results indicate that the availability of pseudo-relatives seems to influence attachment preferences; however, the results ensuing from the statistical comparison of groups 3 and 4 need further investigation

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan languages

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan Languages publishes 17 papers that were presented at the conference organised in Dubrovnik, Croatia, 4-6 Octobre 2010

16th International NooJ 2022 Conference: Book of Abstracts

Libro de resúmenes presentados en la "16th International NooJ 2022 Conference", de modalidad híbrida, realizada en el ECU (Espacio Cultural Universitario, UNR) en Rosario, Santa Fe, Argentina, entre el 14 y 15 de junio de 2022.Fil: Reyes, Silvia Susana. Universidad Nacional de Rosario. Facultad de Humanidades y Artes; Argentin

The Missing Link between Morphemic Assemblies and Behavioral Responses:a Bayesian Information-Theoretical model of lexical processing

We present the Bayesian Information-Theoretical (BIT) model of lexical processing: A mathematical model illustrating a novel approach to the modelling of language processes. The model shows how a neurophysiological theory of lexical processing relying on Hebbian association and neural assemblies can directly account for a variety of effects previously observed in behavioural experiments. We develop two information-theoretical measures of the distribution of usages of a morpheme or word, and use them to predict responses in three visual lexical decision datasets investigating inflectional morphology and polysemy. Our model offers a neurophysiological basis for the effects of morpho-semantic neighbourhoods. These results demonstrate how distributed patterns of activation naturally result in the arisal of symbolic structures. We conclude by arguing that the modelling framework exemplified here, is a powerful tool for integrating behavioural and neurophysiological results

El papel de la experiencia en el procesamiento sintáctico: una visión crítica desde la lingüística

Linguists with an interest in psycholinguistic research on the processing of language often feel concerned that psycholinguistic experiments truly reflect important aspects of the nature of language and not artefactual dimensions of the methodologies used in them. In this paper I intend to argue that one of the main theories of language comprehension, Tuning, is flawed precisely because the theory has virtually no connection with the world of linguistics. If my view is correct, information about language obtained within the Tuning paradigm is therefore unlikely to reflect truly significant aspects of the nature of language. Tuning is premised on the role played by frequency in many cognitive domains, including the processing of language. It claims that ambiguous sentences are processed initially by preferring more frequent syntactic trees over less frequent ones. A prerequisite to the verificability of the theory is that its corpus analyses be wellfounded. Another is that the theory spell out precisely what counts as a segment subject to frequency effects. I intend to argue that these two prerequisites are not adequately controlled by the proponents of the modelAlgunos lingüistas interesados en la investigación realizada sobre el procesamiento lingüístico suelen expresar su preocupación de que los experimentos psicolingüísticos reflejen verdaderamente aspectos importantes de la naturaleza de la facultad lingüística humana, y no sesgos incontrolados de las metodologías con las que se realizan. En este artículo pretendo defender la tesis de que una de las principales teorías de procesamiento lingüístico, Tuning, está fundada sobre bases teóricas endebles, precisamente porque apenas goza de conexión alguna con el mundo de la lingüística. De ser esto cierto, es poco probable que la información sobre la facultad del lenguaje que proceda de dicho paradigma investigador arroje un conocimiento de aspectos verdaderamente significativos sobre la naturaleza del lenguaje humano. Tuning enfatiza el papel de hecho desempeñado por la frecuencia en la formación de hábitos pertenecientes a diversos dominios cognitivos, entre los que figura el procesamiento lingüístico. Mantiene que las oraciones ambiguas se procesan en un primer barrido a través de un sesgo o predilección por los árboles sintácticos más frecuentes. Un primer prerrequisito para la verificabilidad de la teoría es que sus análisis de corpus sean fiables. Otro segundo prerrequisito es que la teoría explicite de modo preciso qué segmentos sintácticos concretos están sujetos a recuentos de frecuencia. Es mi intención demostrar que estos dos prerrequisitos no están suficientemente controlados por los defensores del modeloThis research was funded by the Fund for Scientific Research of the Autonomous Government of Galicia (grant number PGIDT01PXI20401PR)S

A Literature Research On Machine Learning Techniques Used For Training Annotated Corpus

The development of research in the annotation area is growing. Researchers perform annotation task using various forms of datasets such as text, sound, images, and videos. Various algorithms are used to perform tasks. The purpose of this survey is to find out algorithms that are often used by researchers to perform annotation tasks, especially on text data. The literature surveys thirteen research papers on text annotation from the last 5 years. The results of this review indicate that SVM is the algorithm used for all three annotation methods: manual, automatic and semi-automatic annotation, with a significant accuracy above 80%. The result of this survey will be referred by the authors as the basis for subsequent research that will be conducted, especially in the semi-automatic annotation method

On Pseudorelatives and Human Sentence Parsing

The debate over whether universal parsing mechanisms are necessary to explain sentence comprehension is clearly a fundamental one for cognitive science. This dissertation focuses on the relation between syntactic ambiguity and principles of economy in the parsing of ambiguous Pseudo Relative (PR)/ Relative Clause (RC) strings. While the principles of locality would predict local attachment in (exclusive) RC contexts, PR-first Hypothesis (Grillo & Costa, 2014) predicts high attachment (corresponding to a PR parse) in ambiguous PR/RC contexts. We test the offline and online effects of PR availability in Spanish using a variety of research methods (eye-tracking while reading, sentence completion task, forced-choice questionnaire, acceptability judgement), while also looking at the interaction with other factors such as aspectual properties of the embedded predicate. The results reported here are robust across studies and show an influence of PRs on the parsing of RCs: when PRs are not a confound, and relevant factors are controlled (e.g. length of the clauses), locality principles apply to RC attachment; when PRs are available, attachment preferences shift toward the non-local option. These results support the universality of parsing principles and suggest that crosslinguistic variation in RC attachment is epiphenomenal and largely attributable to the asymmetric availability of PRs across languages. This dissertation also provides a detailed description on PR-licensing contexts that might be useful for future research on RC attachment preferences to avoid the PR confound.O debate sobre se os mecanismos de análise universal são necessários para explicar a compreensão de frases é claramente fundamental para a Ciência Cognitiva. Esta dissertação centra-se na relação entre ambiguidade sintática e princípios de economia na análise de estruturaspseudorelativas (PR)/ orações relativas (OR) ambíguas. Enquanto os princípios de localidade prediriam a ligação local em contextos (exclusivos) das OR, a PR-first Hypothesis (Grillo & Costa, 2014) prevê uma alta ligação (correspondente a uma análise da PR) em contextos PR/OR ambíguos. Nesta tese testamos os efeitos offline e online da disponibilidade das PRs em Espanhol, utilizando uma variedade de métodos de investigação (técnica de registo dos comportamentos oculares (eye-tracking) durante a leitura, tarefa de preenchimento de frases, questionários, julgamento da aceitabilidade), ao mesmo tempo que também analisamos a interação com as propriedades aspetuais do predicado encaixado. Os resultados obtidos nesta dissertação mostram uma influência das PRs na análise das ORs: quando as PRs estão disponíveis e os fatores relevantes são controlados (por exemplo, o comprimento das orações), os princípios da localidade aplicam-se à adjunção das ORs; quando as PRs estão disponíveis, as preferências de adjunção mudam para a opção não-local. Estes resultados apoiam a universalidade dos princípios de análise e sugerem que a variação linguística na adjunção da OR é epifenomenal e amplamente atribuível à disponibilidade assimétrica das PRs entre línguas. Esta dissertação também fornece uma descrição detalhada dos contextos de licenciamento da PR, que podem ser úteis para evitar a ambiguidade PR/OR em futuras pesquisas sobre as preferências da ligação da OR

Continually improving grounded natural language understanding through human-robot dialog

As robots become ubiquitous in homes and workplaces such as hospitals and factories, they must be able to communicate with humans. Several kinds of knowledge are required to understand and respond to a human's natural language commands and questions. If a person requests an assistant robot to take me to Alice's office, the robot must know that Alice is a person who owns some unique office, and that take me means it should navigate there. Similarly, if a person requests bring me the heavy, green mug, the robot must have accurate mental models of the physical concepts heavy, green, and mug. To avoid forcing humans to use key phrases or words robots already know, this thesis focuses on helping robots understanding new language constructs through interactions with humans and with the world around them. To understand a command in natural language, a robot must first convert that command to an internal representation that it can reason with. Semantic parsing is a method for performing this conversion, and the target representation is often semantic forms represented as predicate logic with lambda calculus. Traditional semantic parsing relies on hand-crafted resources from a human expert: an ontology of concepts, a lexicon connecting language to those concepts, and training examples of language with abstract meanings. One thrust of this thesis is to perform semantic parsing with sparse initial data. We use the conversations between a robot and human users to induce pairs of natural language utterances with the target semantic forms a robot discovers through its questions, reducing the annotation effort of creating training examples for parsing. We use this data to build more dialog-capable robots in new domains with much less expert human effort (Thomason et al., 2015; Padmakumar et al., 2017). Meanings of many language concepts are bound to the physical world. Understanding object properties and categories, such as heavy, green, and mug requires interacting with and perceiving the physical world. Embodied robots can use manipulation capabilities, such as pushing, picking up, and dropping objects to gather sensory data about them. This data can be used to understand non-visual concepts like heavy and empty (e.g. get the empty carton of milk from the fridge), and assist with concepts that have both visual and non-visual expression (e.g. tall things look big and also exert force sooner than short things when pressed down on). A second thrust of this thesis focuses on strategies for learning these concepts using multi-modal sensory information. We use human-in-the-loop learning to get labels between concept words and actual objects in the environment (Thomason et al., 2016, 2017). We also explore ways to tease out polysemy and synonymy in concept words (Thomason and Mooney, 2017) such as light, which can refer to a weight or a color, the latter sense being synonymous with pale. Additionally, pushing, picking up, and dropping objects to gather sensory information is prohibitively time-consuming, so we investigate strategies for using linguistic information and human input to expedite exploration when learning a new concept (Thomason et al., 2018). Finally, we build an integrated agent with both parsing and perception capabilities that learns from conversations with users to improve both components over time. We demonstrate that parser learning from conversations (Thomason et al., 2015) can be combined with multi-modal perception (Thomason et al., 2016) using predicate-object labels gathered through opportunistic active learning (Thomason et al., 2017) during those conversations to improve performance for understanding natural language commands from humans. Human users also qualitatively rate this integrated learning agent as more usable after it has improved from conversation-based learning.Computer Science

Sentence Processing in a Second Language: Ambiguity Resolution in German Learners of English

The dissertation argues against fundamental differences between first and second language processing with regard to access to deep syntactic structures and phrase structure heuristics. This claim is supported by empirical data from off-line and on-line syntactic ambiguity resolution in non-immersed German learners of English. Although non-proficient learners’ processing deviates from native speakers because of L1 interference as well as less automatic processing and difficulties to recover from misanalyses resulting from processing capacity limitations, these effects were found to attenuate gradually with increasing second language proficiency. Moreover, it depends on the demands of the specific tasks and materials whether the learners’ limited processing resources actually lead to non-native like performance. In structures involving complex syntactic movement via an intermediate gap, the learners showed native-like intermediate gap and filler integration effects. Hence they have access to deep syntactic structures during on-line sentence processing. Moreover, participants did not show a general preference for ambiguous over disambiguated structures, which indicates that they have access to native-like syntactic processing principles. Taken together, the findings show that it is possible even for non-immersed learners to develop native-like syntactic processing in their second language