Learner language morphology as a window to crosslinguistic influences: A key structure analysis

2017 ◽  
Vol 40 (2) ◽  
pp. 225-253 ◽  
Author(s):  
Ilmari Ivaska ◽  
Kirsti Siitonen

The study of crosslinguistic influences (CLI) has proven that morphosyntactic features exhibit CLI. Technical development and novel resources have enabled detection-based approaches, where potential CLI are revealed based on their observed frequencies and on differences between learners with different language backgrounds. The two research questions are as follows: (i) How construction-specific typological (dis)similarities between L1 and L2 affect the frequencies of linguistic features? (ii) Can such (dis)similarities be detected by comparing feature frequency data of L2? The data come from the International Corpus of Learner Finnish, and the methodology applied is the key structure analysis. The results support the applicability of the method: they show that constructional similarities may trigger CLI construction by construction, irrespective of the general similarities or genealogical categorizations. The results further imply the importance of controlling the genre-related and topical variation to account for skewed nature of the data when dealing with naturally occurring learner language data.

1996 ◽  
Vol 1 (2) ◽  
pp. 171-197 ◽  
Author(s):  
Douglas Biber

The present paper argues that analyses of language use provide an important complementary perspective to traditional linguistic descriptions, and that empirical approaches are required for such investigations. Corpus-based techniques are particularly well suited to these research purposes, enabling investigation of research questions that were previously disregarded. Specifically, the paper discusses the use of corpus-based techniques to identify and analyze complex "association patterns": the systematic ways in which linguistic features are used in association with other linguistic and non-linguistic features. Several illustrative analyses are discussed, investigating the use of lexical features, grammatical features, and the overall patterns of variability among texts and registers.


2018 ◽  
Vol 23 (1) ◽  
pp. 28-54 ◽  
Author(s):  
Yan Huang ◽  
Akira Murakami ◽  
Theodora Alexopoulou ◽  
Anna Korhonen

Abstract Current syntactic annotation of large-scale learner corpora mainly resorts to “standard parsers” trained on native language data. Understanding how these parsers perform on learner data is important for downstream research and application related to learner language. This study evaluates the performance of multiple standard probabilistic parsers on learner English. Our contributions are three-fold. Firstly, we demonstrate that the common practice of constructing a gold standard – by manually correcting the pre-annotation of a single parser – can introduce bias to parser evaluation. We propose an alternative annotation method which can control for the annotation bias. Secondly, we quantify the influence of learner errors on parsing errors, and identify the learner errors that impact on parsing most. Finally, we compare the performance of the parsers on learner English and native English. Our results have useful implications on how to select a standard parser for learner English.


Author(s):  
Phyo Htet Hein ◽  
Varun Menon ◽  
Beshoy Morkos

Prior research performed by Morkos [1], culminated in the automated requirement change propagation prediction (ARCPP) tool which utilized natural language data in requirements to predict change propagation throughout a requirements document as a result of an initiating requirement change. Whereas the prior research proved requirements can be used to predict change propagation, the purpose of this case study is to understand why. Specifically, what parts of a requirement affect its ability to predict change propagation? This is performed by addressing two key research questions: (1) Is the requirement review depth affected by the number of relators selected to relate requirements and (2) What elements of a requirement are responsible for instigating change propagation, the physical (nouns) or functional (verbs) domain? The results of this study assist in understanding whether the physical or functional domain have a greater effect on the instigation of change propagation. The results indicated that the review depth, an indicator of the performance of the ARCPP tool, is not affected by the number of relators, but rather by the ability of relators in relating the propagating relationships. Further, nouns are found to be more contributing to predicting change propagation in requirements. Therefore, the physical domain is more effective in predicting requirement change propagation than the functional domain.


Pomorstvo ◽  
2016 ◽  
Vol 30 (1) ◽  
pp. 67-74
Author(s):  
Milena Dževerdanović

Deck officers must be very familiar with the content of the Standing Orders. This peculiar genre, which is usually written on one page of text and with a distinguishable graphical layout, combines administrative and legal discourse. The subtle interrelation between linguistic features on different levels of the analysis implies the ship’s hierarchy and organization which is a precondition for the safety of the ship. Analysis in this paper relies on discourse and genre knowledge and represents a continuation of the author’s research on maritime written genres in terms of both their structure an interpretation of language facts. Structure analysis in the paper is based on Bhatia’s (1993) model. After the moves of Standing Orders were established, the analysis focused on discursive characteristics of each move. As Standing Orders as genre for the most part belong to legal discourse (apart from commercial and merchant discourse), the analysis tends to show correlation between discourse exponents of this legal genre and crew members’ position and duties on board ship. Findings of this paper can be pedagogically useful in terms of providing a teaching model that will aid Maritime English teachers to convey knowledge of this genre to students, especially future deck officers and ship masters.


2021 ◽  
Vol 69 (3) ◽  
pp. 2845-2861
Author(s):  
Ebrahim Heidary ◽  
Ham飀 Parv飊 ◽  
Samad Nejatian ◽  
Karamollah Bagherifard ◽  
Vahideh Rezaie

2021 ◽  
Author(s):  
◽  
Lani Pomeroy

<p>This thesis is an investigation into laughter in psychotherapeutic interactions. Conversation analysis was the method used to analyse laughter practices by client and therapist that aid in the business of psychotherapy. Analysing naturally occurring talk is important as it reveals how actions are accomplished, as some past studies on laughter in psychotherapy rely on anecdotal evidence and categorical analysis. Additionally, past psychological literature on laughter can view the phenomenon of laughter as random, and as a by-product of humour. An assumption of conversation analysis is the view of talk being systematic and organised. There is no detail too small that it does not contribute to an interaction (Jefferson, 1985). With this viewpoint in mind conversation analysts have revealed laughter to be an orderly phenomenon that is capable of other actions in talk besides appreciating humour. However, there is a lack of conversation analytical work in laughter during therapy; a gap this thesis sought to address. In particular there were two research questions. If laughter does not have the sole role of appreciating humour, what can it do in psychotherapy? Additionally, past studies in psychotherapy have linked laughter to affiliation in therapy sessions, but do not illustrate the specific sequence of how rapport is achieved in the interaction itself. Psychotherapy can be known as the „talking cure‟ (Perakyla, Antaki, Vehvilainen, & Leudar, 2008), thus, the second question is how does laughter display affiliation in therapeutic talk? Using the fundamental literature of conversation analysis there were two findings regarding laughter in psychotherapy found in this thesis. Firstly, clients would laugh responsively to an action of therapeutic import, the laughter functioned as a marker of dis-preference and an invitation for the therapist to laugh. The therapist would dis-attend the client‟s laughter in order to prompt talk which progressed the therapy from the client. Secondly, therapist could affiliate with the client by display a shared stance towards a matter spoken of by the client. During or after these displays the therapist invited laughter from the client so that the two could laugh together in a further display of shared emotional alignment. These results expanded conversation analytical work on laughter regarding laughter invitations (Jefferson, 1979) and work on psychotherapeutic interactions regarding the prompting of talk (Muntigl, & Hadic Zabala, 2008). The findings also provide empirical evidence for how therapists affiliate with their clients using laughter at the micro-analytical level. The findings of this thesis contribute to psychological, conversation analytical, and psychotherapeutic knowledge on laughter.</p>


Author(s):  
Adriana Picoral ◽  
Shelley Staples ◽  
Randi Reppen

Abstract This paper explores the use of natural language processing (NLP) tools and their utility for learner language analyses through a comparison of automatic linguistic annotation against a gold standard produced by humans. While there are a number of automated annotation tools for English currently available, little research is available on the accuracy of these tools when annotating learner data. We compare the performance of three linguistic annotation tools (a tagger and two parsers) on academic writing in English produced by learners (both L1 and L2 English speakers). We focus on lexico-grammatical patterns, including both phrasal and clausal features, since these are frequently investigated in applied linguistics studies. Our results report both precision and recall of annotation output for argumentative texts in English across four L1s: Arabic, Chinese, English, and Korean. We close with a discussion of the benefits and drawbacks of using automatic tools to annotate learner language.


2019 ◽  
Vol 5 (1) ◽  
pp. 67-90 ◽  
Author(s):  
Gaja Jarosz

Recent advances in computational modeling have led to significant discoveries about the representation and acquisition of phonological knowledge and the limits on language learning and variation. These discoveries are the result of applying computational learning models to increasingly rich and complex natural language data while making increasingly realistic assumptions about the learning task. This article reviews the recent developments in computational modeling that have made connections between fully explicit theories of learning, naturally occurring corpus data, and the richness of psycholinguistic and typological data possible. These advances fall into two broad research areas: ( a) the development of models capable of learning the quantitative, noisy, and inconsistent patterns that are characteristic of naturalistic data and ( b) the development of models with the capacity to learn hidden phonological structure from unlabeled data. After reviewing these advances, the article summarizes some of the most significant consequent discoveries.


2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Mengyao Yu ◽  
Sheng Fu ◽  
Yinbo Gao ◽  
Hao Zheng ◽  
Yonggang Xu

A simple method was developed to detect damage based on a discrete mathematical model for fan blades using changes in natural frequencies combined with a fluid-structure analysis. In addition, a numerical approach was developed for the fluid-structure analysis. The results of numerical simulation provided the natural frequency data for each mode under different locations and sizes of a single crack in a blade. A fault database was built using Matlab. The damage of a blade was detected using the changes in natural frequencies. This study will assist in investigating the effect of a crack on a structure from different perspectives; the simulation results show the effectiveness of this approach.


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