The frame system as an interlingual representation for parallel texts

One of the key problems in comparative studies based on frame semantics is the question whether frames can become an interlingua. This paper argues that not only single frames, but their systems or frame semantic domain representations consisting of frames and their relations are also useful in comparative studies. Such a system of frames helps one explain why seemingly unrelated expressions in different languages find a common denominator in higher-order frames, thus becoming semantic-pragmatic equivalents. To support this argument, an analysis of Polish, English and German lease agreements as parallel texts is conducted and the benefits of this approach to comparative studies are presented. The study is in line with the recent FrameNet initiatives, such as the Global FrameNet and automatic translation studies. However, it differs in some methodological aspects. Instead of using FrameNet as the given lexical resource, domain specific frames are defined starting from common general concepts of the analyzed semantic domain. A text-based approach rather than a comparison of bi-sentences or phrases is adapted. The work thus introduces a new approach to comparative studies based on frame semantics and frame semantic research. It also follows the recent research trend of adding a pragmatic dimension to frame semantic analysis by analyzing frames in context.

Neg-phrases in Eton (Bantu): An HPSG-analysis

This paper presents a study of so-called neg-phrases in Eton, a negative concord language spoken in Cameroon. These phrases strongly resemble negated noun phrases that consist of a negative determiner and a noun, however, I will show that Eton neg-phrases are built differently. Reconciling the non-negative approach to negative indefinites by Penka & Zeijlstra (2005) and the negative approach by Richter & Sailer (2004a,b, 2006), I will argue that Eton neg-phrases consist of an inherently negative modifier and a non-negative indefinite derived from a noun. Embedding the analysis in Lexical Resource Semantics, I will reveal the inherent negativity of Eton neg-phrases and account for their composition by using a lexical rule based on the semantic approach to noun phrases by Beavers (2003).

The grammatical representation of expletive negation

Expletive negation refers to constructions where a negator in the complement of certain lexical items does not change the polarity of the complement proposition. Jin & Koenig (2021) show that expletive negation occurs rather widely in languages of the world and in very similar environments. They propose a language production model of why such apparently illogical uses of negation arise in language after language. But their study does not address the grammatical status and representation of expletive negation. In this paper, we argue that expletive negation is part of the lexical knowledge speakers have of their language and that the negator in expletive negation constructions contributes a negation to a non-at-issue content associated with expletive negation triggers. We provide a Lexical Resource Semantics analysis of how triggers combine in a non-standard manner with the standard semantic content of their complements: the negation (and in some cases an additional modal operator) of the content of their complement is part of the trigger’s non-at-issue content while the scope of the negation is an argument of the trigger’s MAIN content. Finally, we suggest that the expletive use of the French negator ne includes a lexical constraint that requires it to modify a verb that reverse selects for an expletive negation trigger.

KGGCN: Knowledge-Guided Graph Convolutional Networks for Distantly Supervised Relation Extraction

Distantly supervised relation extraction is the most popular technique for identifying semantic relation between two entities. Most prior models only focus on the supervision information present in training sentences. In addition to training sentences, external lexical resource and knowledge graphs often contain other relevant prior knowledge. However, relation extraction models usually ignore such readily available information. Moreover, previous works only utilize a selective attention mechanism over sentences to alleviate the impact of noise, they lack the consideration of the implicit interaction between sentences with relation facts. In this paper, (1) a knowledge-guided graph convolutional network is proposed based on the word-level attention mechanism to encode the sentences. It can capture the key words and cue phrases to generate expressive sentence-level features by attending to the relation indicators obtained from the external lexical resource. (2) A knowledge-guided sentence selector is proposed, which explores the semantic and structural information of triples from knowledge graph as sentence-level knowledge attention to distinguish the importance of each individual sentence. Experimental results on two widely used datasets, NYT-FB and GDS, show that our approach is able to efficiently use the prior knowledge from the external lexical resource and knowledge graph to enhance the performance of distantly supervised relation extraction.

Estudio comparativo de la prueba de redacción académica IELTS por escrito frente a la realizada en computadora en participantes de IELTS de inglés como lengua extranjera en la Universidad de Teherán

The current study is an attempt to investigate whether learners perform differently on paper or on the computer in the International English Language Testing System (IELTS) writing test, in terms of Task response/achievement, coherence/cohesion, lexical resource, grammatical range and accuracy. In addition, it explores whether the candidates’ computer familiarity are different in paper or computer groups. To this end, a total number of 108 candidates were selected out of 144 based on the results of the Oxford Placement Test (OPT) in Tehran University, Iran. To gather the data, a retired IELTS academic writing sample and a computer familiarity questionnaire were administered. The participants were divided into two equal groups. In the Paper Mode (PM) group, students were given the test to write conventionally on paper. In the other, Computer Mode (CM) group, the students were given the same test; but were asked to type the test in the computer provided for them in their class. Also, all the participants took the computer familiarity questionnaire. The gathered data were analyzed through the Independent samples t-test. The findings reveal significant differences between paper-based and computer-based modes in both writing tasks. Moreover, the analysis of the questionnaire shows the impact of the candidates’ computer familiarity on their writing performance.

Error analysis of undergraduate students’ writing performances: IELTS-based activities

As English as a Foreign Language (EFL) students, it is expected that they should have good ability in four skills of English, including writing skill. However, errors are still found in students’ written works. This paper examines the different types of writing errors made by 10 Indonesian undergraduate students who were enrolled in an IELTS preparation class. Descriptive qualitative research was employed in this study. The errors in the term papers were identified and classified accordingly. The students’ works were assessed based on four aspects: task achievement, cohesion and coherence, lexical resource, grammatical range, and accuracy. The result of this study showed that the highest percentage of students’ errors was in terms of lexical resource and grammatical range (accuracy), followed by three other common errors: singular or plural, word choice, and punctuation. Regarding to the task achievement, in Task 1, most of the students addressed the task but they didn't cover all the information needed. While in Task 2, some students presented clear opinions but with limited and inadequately developed ideas and gave no conclusion. Furthermore, for the results of cohesion and coherence in Task 1, most students showed good logical sequence and overall progression but with faulty cohesion devices. However in Task 2, some students presented either incoherent or illogical ideas or information. These results contribute as fruitful insights for language learners who want to enhance the IELTS comprehension, particularly writing performance.

The Collaborative Discussion Model: Developing Writing Skills through Prewriting Discussion

This study aims to investigate the effect of peer-assisted prewriting discussion on second language (L2) academic writing and its benefits for students with different proficiency levels. While there is a significant body of research exploring the positive impact of collaboration on L2 writers' written performance and the ways it could be organised, there is little practical consideration on how to formulate explicit instruction. The rationale for this research lies in designing and arranging explicit instruction that could lead to L2 learners producing a higher quality writing output. Based on both qualitative and quantitative methods, and drawn on students’ written texts and data analysis, the current study was conducted to devise and test a proposed model, which the author will term the ‘collaborative discussion model’ (the CDM). The control and experimental groups of Russian EFL students (n = 48) were engaged in written assignments after naturally occurring discussions and then the latter group was involved in an instructor-led discussion. The practice writing tasks were rated with the analytic rubric used in IELTS, assessing task response, coherence and cohesion, lexical resource, and grammatical range. The findings suggest that collaborative prewriting tasks, accomplished in the experimental group of students with different levels of L2 proficiency, may encourage students to engage more in reflection about the content and language of the text. As the texts produced after introducing the CDM were scored higher, especially on the criteria of task response and lexical resource, it is suggested that scaffolding prewriting discussions can potentially augment the writing skills of learners and the CDM can be used as a complementary activity to address the challenges associated with academic writing. The results of the questionnaire can imply that there are benefits of explicit instruction for students with different levels of L2 proficiency, although in nuanced ways and different degrees.

PMI-based polarity computation for SVM-NN-based sentiment classification from user-generated reviews

PurposeThe purpose of this paper is to initially perform Senti-WordNet (SWN)- and point wise mutual information (PMI)-based polarity computation and based polarity updation. When the SWN polarity and polarity mismatched, the vote flipping algorithm (VFA) is employed.Design/methodology/approachRecently, in domains like social media(SM), healthcare, hotel, car, product data, etc., research on sentiment analysis (SA) has massively increased. In addition, there is no approach for analyzing the positive or negative orientations of every single aspect in a document (a tweet, a review, as well as a piece of news, among others). For SA as well as polarity classification, several researchers have used SWN as a lexical resource. Nevertheless, these lexicons show lower-level performance for sentiment classification (SC) than domain-specific lexicons (DSL). Likewise, in some scenarios, the same term is utilized differently between domain and general knowledge lexicons. While concerning different domains, most words have one sentiment class in SWN, and in the annotated data set, their occurrence signifies a strong inclination with the other sentiment class. Hence, this paper chiefly concentrates on the drawbacks of adapting domain-dependent sentiment lexicon (DDSL) from a collection of labeled user reviews and domain-independent lexicon (DIL) for proposing a framework centered on the information theory that could predict the correct polarity of the words (positive, neutral and negative). The proposed work initially performs SWN- and PMI-based polarity computation and based polarity updation. When the SWN polarity and polarity mismatched, the vote flipping algorithm (VFA) is employed. Finally, the predicted polarity is inputted to the mtf-idf-based SVM-NN classifier for the SC of reviews. The outcomes are examined and contrasted to the other existing techniques to verify that the proposed work has predicted the class of the reviews more effectually for different datasets.FindingsThere is no approach for analyzing the positive or negative orientations of every single aspect in a document (a tweet, a review, as well as a piece of news, among others). For SA as well as polarity classification, several researchers have used SWN as a lexical resource. Nevertheless, these lexicons show lower-level performance for sentiment classification (SC) than domain-specific lexicons (DSL). Likewise, in some scenarios, the same term is utilized differently between domain and general knowledge lexicons. While concerning different domains, most words have one sentiment class in SWN, and in the annotated data set their occurrence signifies a strong inclination with the other sentiment class.Originality/valueThe proposed work initially performs SWN- and PMI-based polarity computation, and based polarity updation. When the SWN polarity and polarity mismatched, the vote flipping algorithm (VFA) is employed.