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Children's online use of word order and morphosyntactic markers in Tagalog thematic role assignment
(2019)
We 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).
Matching participants (as suggested by Hope, 2015) may be one promising option for research on a potential bilingual advantage in executive functions (EF). In this study we first compared performances in three EF-tasks of a naturally heterogeneous sample of monolingual (n = 69, age = 9.0 y) and multilingual children (n = 57, age = 9.3 y). Secondly, we meticulously matched participants pairwise to obtain two highly homogeneous groups to rerun our analysis and investigate a potential bilingual advantage. The initally disadvantaged multilinguals (regarding socioeconomic status and German lexicon size) performed worse in updating and response inhibition, but similarly in interference inhibition. This indicates that superior EF compensate for the detrimental effects of the background variables. After matching children pairwise on age, gender, intelligence, socioeconomic status and German lexicon size, performances became similar except for interference inhibition. Here, an advantage for multilinguals in the form of globally reduced reaction times emerged, indicating a bilingual executive processing advantage.
The immense popularity of online communication services in the last decade has not only upended our lives (with news spreading like wildfire on the Web, presidents announcing their decisions on Twitter, and the outcome of political elections being determined on Facebook) but also dramatically increased the amount of data exchanged on these platforms. Therefore, if we wish to understand the needs of modern society better and want to protect it from new threats, we urgently need more robust, higher-quality natural language processing (NLP) applications that can recognize such necessities and menaces automatically, by analyzing uncensored texts. Unfortunately, most NLP programs today have been created for standard language, as we know it from newspapers, or, in the best case, adapted to the specifics of English social media.
This thesis reduces the existing deficit by entering the new frontier of German online communication and addressing one of its most prolific forms—users’ conversations on Twitter. In particular, it explores the ways and means by how people express their opinions on this service, examines current approaches to automatic mining of these feelings, and proposes novel methods, which outperform state-of-the-art techniques. For this purpose, I introduce a new corpus of German tweets that have been manually annotated with sentiments, their targets and holders, as well as lexical polarity items and their contextual modifiers. Using these data, I explore four major areas of sentiment research: (i) generation of sentiment lexicons, (ii) fine-grained opinion mining, (iii) message-level polarity classification, and (iv) discourse-aware sentiment analysis. In the first task, I compare three popular groups of lexicon generation methods: dictionary-, corpus-, and word-embedding–based ones, finding that dictionary-based systems generally yield better polarity lists than the last two groups. Apart from this, I propose a linear projection algorithm, whose results surpass many existing automatically-generated lexicons. Afterwords, in the second task, I examine two common approaches to automatic prediction of sentiment spans, their sources, and targets: conditional random fields (CRFs) and recurrent neural networks, obtaining higher scores with the former model and improving these results even further by redefining the structure of CRF graphs. When dealing with message-level polarity classification, I juxtapose three major sentiment paradigms: lexicon-, machine-learning–, and deep-learning–based systems, and try to unite the first and last of these method groups by introducing a bidirectional neural network with lexicon-based attention. Finally, in order to make the new classifier aware of microblogs' discourse structure, I let it separately analyze the elementary discourse units of each tweet and infer the overall polarity of a message from the scores of its EDUs with the help of two new approaches: latent-marginalized CRFs and Recursive Dirichlet Process.
There is evidence that infants start extracting words from fluent speech around 7.5 months of age (e.g., Jusczyk & Aslin, 1995) and that they use at least two mechanisms to segment words forms from fluent speech: prosodic information (e.g., Jusczyk, Cutler & Redanz, 1993) and statistical information (e.g., Saffran, Aslin & Newport, 1996). However, how these two mechanisms interact and whether they change during development is still not fully understood.
The main aim of the present work is to understand in what way different cues to word segmentation are exploited by infants when learning the language in their environment, as well as to explore whether this ability is related to later language skills. In Chapter 3 we pursued to determine the reliability of the method used in most of the experiments in the present thesis (the Headturn Preference Procedure), as well as to examine correlations and individual differences between infants’ performance and later language outcomes. In Chapter 4 we investigated how German-speaking adults weigh statistical and prosodic information for word segmentation. We familiarized adults with an auditory string in which statistical and prosodic information indicated different word boundaries and obtained both behavioral and pupillometry responses. Then, we conducted further experiments to understand in what way different cues to word segmentation are exploited by 9-month-old German-learning infants (Chapter 5) and by 6-month-old German-learning infants (Chapter 6). In addition, we conducted follow-up questionnaires with the infants and obtained language outcomes at later stages of development.
Our findings from this thesis revealed that (1) German-speaking adults show a strong weight of prosodic cues, at least for the materials used in this study and that (2) German-learning infants weight these two kind of cues differently depending on age and/or language experience. We observed that, unlike English-learning infants, 6-month-old infants relied more strongly on prosodic cues. Nine-month-olds do not show any preference for either of the cues in the word segmentation task. From the present results it remains unclear whether the ability to use prosodic cues to word segmentation relates to later language vocabulary. We speculate that prosody provides infants with their first window into the specific acoustic regularities in the signal, which enables them to master the specific stress pattern of German rapidly. Our findings are a step forwards in the understanding of an early impact of the native prosody compared to statistical learning in early word segmentation.