Verb-based prediction during language processing: the case of Dutch and Turkish

2018 ◽  
Vol 46 (1) ◽  
pp. 80-97 ◽  
Author(s):  
Susanne BROUWER ◽  
Deniz ÖZKAN ◽  
Aylin C. KÜNTAY
Keyword(s):  
Eye Tracking ◽  
Language Processing ◽  
Word Order ◽  
Native Language ◽  
Visual Display ◽  
Tracking Task ◽  
Linguistic Differences ◽  
Sentence Meaning ◽  
Order Structures ◽  
Familiar Objects

AbstractThis study investigated whether cross-linguistic differences affect semantic prediction. We assessed this by looking at two languages, Dutch and Turkish, that differ in word order and thus vary in how words come together to create sentence meaning. In an eye-tracking task, Dutch and Turkish four-year-olds (N = 40), five-year-olds (N = 58), and adults (N = 40) were presented with a visual display containing two familiar objects (e.g., a cake and a tree). Participants heard semantically constraining (e.g., “The boy eats the big cake”) or neutral sentences (e.g., “The boy sees the big cake”) in their native language. The Dutch data revealed a prediction effect for children and adults; however, it was larger for the adults. The Turkish data revealed no prediction effect for the children but only for the adults. These findings reveal that experience with word order structures and/or automatization of language processing routines may lead to timecourse differences in semantic prediction.

How Bilingualism Affects Syntactic Processing in The Native Language

2019 ◽  
pp. 97-107
Author(s):  
Paola E. Dussias ◽  
Jorge R. Valdés Kroff ◽  
Michael Johns ◽  
Álvaro Villegas
Keyword(s):  
Second Language ◽  
Eye Tracking ◽  
Language Processing ◽  
Native Language ◽  
Syntactic Processing ◽  
Language Attrition ◽  
Fine Grained ◽  
Clear Cut

In this chapter, we survey recent contributions to the research on bilingual language processing that demonstrate how exposure to a second language, even for a brief period of time, can impact processing in the native language. We focus our discussion primarily on syntactic and morpho-syntactic processing. In light of this evidence, we argue that claims of language attrition may not be as clear-cut as one may think when online language processing is taken into account. A second goal of our chapter is to show that eye-tracking is a premier behavioural method by which we can come to understand fine-grained changes in online language processing. In doing so, we hope to illustrate how the study of online language processing via eye-tracking can help to clarify issues in language attrition.

scholarly journals Children's online use of word order and morphosyntactic markers in Tagalog thematic role assignment: an eye-tracking study

2019 ◽  
Vol 47 (3) ◽  
pp. 533-555 ◽  
Author(s):  
Rowena GARCIA ◽  
Jens ROESER ◽  
Barbara HÖHLE
Keyword(s):  
Eye Tracking ◽  
Language Processing ◽  
Noun Phrase ◽  
Word Order ◽  
Initial Conditions ◽  
Tracking Data ◽  
Thematic Role ◽  
Role Assignment ◽  
Heuristic Strategies

AbstractWe investigated whether Tagalog-speaking children incrementally interpret the first noun as the agent, even if verbal and nominal markers for assigning thematic roles are given early in Tagalog sentences. We asked five- and seven-year-old children and adult controls to select which of two pictures of reversible actions matched the sentence they heard, while their looks to the pictures were tracked. Accuracy and eye-tracking data showed that agent-initial sentences were easier to comprehend than patient-initial sentences, but the effect of word order was modulated by voice. Moreover, our eye-tracking data provided evidence that, by the first noun phrase, seven-year-old children looked more to the target in the agent-initial compared to the patient-initial conditions, but this word order advantage was no longer observed by the second noun phrase. The findings support language processing and acquisition models which emphasize the role of frequency in developing heuristic strategies (e.g., Chang, Dell, & Bock, 2006).

Practical Issues of Translated Texts Editing

2019 ◽  
Vol 25242644 ◽  
pp. 38-41
Author(s):  
Maryna Aleksandrovych
Keyword(s):  
Word Order ◽  
Native Language ◽  
Original Text ◽  
Copy Editor ◽  
Proper Nouns ◽  
Units Of Measurement ◽  
Work Shifts ◽  
Basic Features

This article contains a summary of some practical issues of translated texts editing with clear examples. Copy editing of translated texts is different from copy editing of texts written in the native language, because the focus of the work shifts from how to deliver an author’s message in the most appropriate way to how to deliver author’s text, written in the native language, in another language (Ukrainian) in the most appropriate way. The first question is the versatility: does the copy editor need to know the language of the original text in order to do effective copy editing. And she/he should at least understand the basic features of the language of the original text such as phonetics, grammar and syntax. Also a copy editor should pay particular attention to such aspects as: at the lexical level – false friends, transliteration of proper nouns, excess of possessive pronouns, translation or adaptation of lexical gaps; at the syntactic level – copulative verb, word order in a sentence, contrastive stress in a phrase, address words, syntax simplification. A necessary aspect is the unification of certain elements in the translated text: address words, units of measurement (length, weight, area, time, volume, etc.), transliterated proper and common nouns. Described in this article principles of transliteration, unification, adaptation, lexical and syntactic aspects of copy editing of translated texts will help to improve the quality of translated books into Ukrainian.

The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

2020 ◽  
Author(s):  
Kun Sun
Keyword(s):  
Eye Tracking ◽  
Semantic Similarity ◽  
Cognitive Processing ◽  
Language Processing ◽  
Language Comprehension ◽  
Word Processing ◽  
Reading Time ◽  
Computational Models ◽  
Tracking Data ◽  
Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

Processing subject focus across two Spanish varieties

Probus ◽  
2020 ◽  
Vol 32 (1) ◽  
pp. 93-127
Author(s):  
Bradley Hoot ◽  
Tania Leal
Keyword(s):  
Language Processing ◽  
Sentence Processing ◽  
Word Order ◽  
Judgment Task ◽  
Spanish Speakers ◽  
Experimental Results ◽  
Final Position ◽  
Reading Task ◽  
New Information ◽  
Basic Facts

AbstractLinguists have keenly studied the realization of focus – the part of the sentence introducing new information – because it involves the interaction of different linguistic modules. Syntacticians have argued that Spanish uses word order for information-structural purposes, marking focused constituents via rightmost movement. However, recent studies have challenged this claim. To contribute sentence-processing evidence, we conducted a self-paced reading task and a judgment task with Mexican and Catalonian Spanish speakers. We found that movement to final position can signal focus in Spanish, in contrast to the aforementioned work. We contextualize our results within the literature, identifying three basic facts that theories of Spanish focus and theories of language processing should explain, and advance a fourth: that mismatches in information-structural expectations can induce processing delays. Finally, we propose that some differences in the existing experimental results may stem from methodological differences.

scholarly journals Towards Accurate Deceptive Opinions Detection Based on Word Order-Preserving CNN

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Siyuan Zhao ◽  
Zhiwei Xu ◽  
Limin Liu ◽  
Mengjie Guo ◽  
Jing Yun
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Word Order ◽  
Text Analysis ◽  
Important Application ◽  
Detection Mechanism ◽  
Short Text

Convolutional neural network (CNN) has revolutionized the field of natural language processing, which is considerably efficient at semantics analysis that underlies difficult natural language processing problems in a variety of domains. The deceptive opinion detection is an important application of the existing CNN models. The detection mechanism based on CNN models has better self-adaptability and can effectively identify all kinds of deceptive opinions. Online opinions are quite short, varying in their types and content. In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis. In this paper, we optimize the convolutional neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes convolutional neural network more suitable for short text classification and deceptive opinions detection. The TensorFlow-based experiments demonstrate that the proposed detection mechanism achieves more accurate deceptive opinion detection results.

scholarly journals Bilingual Brains Learn to Use L2 Alliterations Covertly like Poets: Brain ERP Evidence

2021 ◽  
Vol 12 ◽  
Author(s):  
Siqin Yang ◽  
Xiaochen Zhang ◽  
Minghu Jiang
Keyword(s):  
Language Processing ◽  
English Learners ◽  
Native Language ◽  
First Language ◽  
Unrelated Word ◽  
Event Related Potential ◽  
Modulation Effect ◽  
Second Languages ◽  
English Translations ◽  
High Level

Bilinguals were documented to access their native or first language (L1) during comprehension of their second languages (L2). However, it is uncertain whether they can access L2 when reading their first language. This study used the event-related potential (ERP) technique to demonstrate the implicit and unconscious access to English words when Chinese–English bilinguals read words in Chinese, their native language. The participants were asked to judge whether the Chinese words presented in pairs were semantically related or not, meanwhile unconscious of the occasional alliteration (repetition of the first phoneme) if the Chinese words were translated into English. While the concealed prime in English translations failed to affect the reaction time, the alliteration significantly modulated N400 among advanced English learners, especially for semantically unrelated word pairs. Critically, this modulation effect was discrepant between bilinguals with high-level and normal-level English proficiency. These results indicate that L2 activation is an unconscious correlate of native-language processing depending on L2 proficiency.

scholarly journals A Survey on Using Gaze Behaviour for Natural Language Processing

2020 ◽  
Author(s):  
Sandeep Mathias ◽  
Diptesh Kanojia ◽  
Abhijit Mishra ◽  
Pushpak Bhattacharya
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Eye Tracking ◽  
Language Processing ◽  
Word Identification ◽  
Test Time ◽  
Gaze Behaviour ◽  
Cognitive Information ◽  
Essay Grading ◽  
Multiple Languages

Gaze behaviour has been used as a way to gather cognitive information for a number of years. In this paper, we discuss the use of gaze behaviour in solving different tasks in natural language processing (NLP) without having to record it at test time. This is because the collection of gaze behaviour is a costly task, both in terms of time and money. Hence, in this paper, we focus on research done to alleviate the need for recording gaze behaviour at run time. We also mention different eye tracking corpora in multiple languages, which are currently available and can be used in natural language processing. We conclude our paper by discussing applications in a domain - education - and how learning gaze behaviour can help in solving the tasks of complex word identification and automatic essay grading.

scholarly journals The male bias of a masculine generic pronoun: Evidence from eye-tracking and sentence evaluation

2020 ◽  
Author(s):  
Theresa Redl ◽  
Stefan L. Frank ◽  
Peter de Swart ◽  
Helen de Hoop
Keyword(s):  
Eye Tracking ◽  
Language Processing ◽  
Possessive Pronoun ◽  
Male Bias ◽  
Gender Information

Two experiments tested whether the Dutch possessive pronoun zijn ‘his’ gives rise to a gender inference and thus causes a male bias when used generically in sentences such as Everyone was putting on his shoes. Experiment 1 (N = 120, 48 male) was a conceptual replication of a previous eye-tracking study that had not found evidence of a male bias. The results of the current eye-tracking experiment showed the masculine generic pronoun to trigger a gender inference and cause a male bias, but for male participants and in neutral stereotype contexts only. No evidence for a male bias was thus found in stereotypically female and male contexts and for female participants altogether. Experiment 2 (N = 80, 40 male) used the same stimuli as Experiment 1, but employed the sentence evaluation paradigm. No evidence of a male bias was found in Experiment 2. Taken together, the results suggest that the masculine generic pronoun zijn ‘his’ can cause a male bias for male participants when no other gender information is provided, but only surfaces with a method such as eye-tracking, which taps directly into automatic language processing. Furthermore, the results suggest that the intended generic reading of the masculine possessive pronoun zijn ‘his’ is readily available for women.

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