SAUDI EFL LEARNERS KNOWLEDGE AND USE OF ENGLISH PREPOSITIONAL VERBS IN ACADEMIC WRITING

Prepositional verbs are essential for English as a foreign language (EFL) and English as a second language (ESL) learners in academic writing. However, most learners, regardless of their proficiency, encounter difficulties using these verbs, and there is a lack of research on these difficulties. This study sought to describe, analyze, and understand Saudi EFL learners knowledge and use of English prepositional verbs in academic writing. The study also assesses the relevant teaching contexts and reasons behind common errors.The study utilized a mixed-methods approach with data collected from a cloze test, multiple-choice test, and semi-structured interview. The two tests were administered to 46 fourth-year undergraduate Saudi EFL students (23 male, 23 female). The interviews were conducted with 20 participants chosen based on their test scores (seven with low scores, seven who scored in the middle, and six with high scores).The findings revealed Saudi EFL learners had extremely low knowledge of and poor performance using English prepositional verbs, committing frequent errors because of L1 interference and other issues. This study offers recommendations to develop EFL teaching methods and curricula to address this problem. One of the major suggestions is to encourage teachers to learn more about these verbs and expose students to more authentic input

Combination Strategies for Semantic Role Labeling

This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model the inference as a meta-learning problem using discriminative classifiers. These classifiers are developed with a rich set of novel features that encode proposition and sentence-level information. To our knowledge, this is the first work that: (a) performs a thorough analysis of learning-based inference models for semantic role labeling, and (b) compares several inference strategies in this context. We evaluate the proposed inference strategies in the framework of the CoNLL-2005 shared task using only automatically-generated syntactic information. The extensive experimental evaluation and analysis indicates that all the proposed inference strategies are successful -they all outperform the current best results reported in the CoNLL-2005 evaluation exercise- but each of the proposed approaches has its advantages and disadvantages. Several important traits of a state-of-the-art SRL combination strategy emerge from this analysis: (i) individual models should be combined at the granularity of candidate arguments rather than at the granularity of complete solutions; (ii) the best combination strategy uses an inference model based in learning; and (iii) the learning-based inference benefits from max-margin classifiers and global feedback

Towards Semi-Automated Annotation for Prepositional Phrase Attachment

This paper investigates whether high-quality annotations for tasks involving semantic disambiguation can be obtained without a major investment in time or expense. We examine the use of untrained human volunteers from Amazon’s Mechanical Turk in disambiguating prepositional phrase (PP) attachment over sentences drawn from the Wall Street Journal corpus. Our goal is to compare the performance of these crowdsourced judgments to the annotations supplied by trained linguists for the Penn Treebank project in order to indicate the viability of this approach for annotation projects that involve contextual disambiguation. The results of our experiments show that invoking majority agreement between multiple human workers can yield PP attachments with fairly high precision, confirming that this crowdsourcing approach to syntactic annotation holds promise for the generation of training corpora in new domains and genres

INVESTIGATING GRAMMATICAL PROBLEMS FACED BY THE SEVENTH SEMESTER STUDENTS OF STKIP ABDI PENDIDIKAN PAYAKUMBUH IN WRITING INTRODUCTION CHAPTER OF RESEARCH PROPOSAL

This research aims to analyze grammatical problems faced by the seventh semester students of STKIP Abdi Pendidikan Payakumbuh in writing introduction chapter of research proposal. The research also determines the caused of those problems. 50 students involved in the study. The study showed that the students had grammatical problems in writing introduction chapter of research proposal, exactly in tenses 293 problems (37.66%). The caused of students’ grammatical problems is overgeneralization. Dealing with the way of lecturer teaching grammar, it was found that the grammar lecturer strategy was still inappropriate. Thus, it is suggested to English lecturer to use various teaching strategies in teaching grammar to make the students master grammar before starting writing.

Proceedings of QG2010: The Third Workshop on Question Generation

These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge". QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)

Analysis of syntactic and semantic features for fine-grained event-spatial understanding in outbreak news reports

<p>Abstract</p> <p>Background</p> <p>Previous studies have suggested that epidemiological reasoning needs a fine-grained modelling of events, especially their spatial and temporal attributes. While the temporal analysis of events has been intensively studied, far less attention has been paid to their spatial analysis. This article aims at filling the gap concerning automatic event-spatial attribute analysis in order to support health surveillance and epidemiological reasoning.</p> <p>Results</p> <p>In this work, we propose a methodology that provides a detailed analysis on each event reported in news articles to recover the most specific locations where it occurs. Various features for recognizing spatial attributes of the events were studied and incorporated into the models which were trained by several machine learning techniques. The best performance for spatial attribute recognition is very promising; 85.9% F-score (86.75% precision/85.1% recall).</p> <p>Conclusions</p> <p>We extended our work on event-spatial attribute recognition by focusing on machine learning techniques, which are CRF, SVM, and Decision tree. Our approach avoided the costly development of an external knowledge base by employing the feature sources that can be acquired locally from the analyzed document. The results showed that the CRF model performed the best. Our study indicated that the nearest location and previous event location are the most important features for the CRF and SVM model, while the location extracted from the verb's subject is the most important to the Decision tree model.</p

A French Corpus Annotated for Multiword Nouns

International audienceThis paper presents a French corpus annotated for multiword nouns. This corpus is designed for investigation in information retrieval and extraction, as well as in deep and shallow syntactic parsing. We delimit which kind of multiword units we targeted for this annotation task; we describe the resources and methods we used for the annotation; and we briefly comment on the results. The annotated corpus is available at http://infolingu.univ-mlv.fr/ under the LGPLLR license.Cet article présente un corpus du français muni d'annotations sur les noms composés. Ce corpus est conçu pour la recherche sur l'extraction d'informations ainsi que sur l'analyse syntaxique superficielle ou profonde. Nous délimitons quels types de mots composés nous avons ciblés pour cette tâche d'annotation ; nous décrivons les ressources et les méthodes que nous avons utilisées pour l'annotation ; et nous commentons brièvement les résultats. Le corpus annoté est disponible sur http://infolingu.univ-mlv.fr/ sous licence LGPLLR

DEVELOPING WRITING THROUGH ORGANIZING ENGLISH LEARNING ACTIVITIES FOR COMMUNICATION WITH B-SLIM MODEL OF STUDENTS IN ENGLISH FOR INTERNATIONAL COMMUNICATION MAJOR, RAJAMANGALA UNIVERSITY OF TECHNOLOGY TAWAN-OK

This study aims to investigate developing writing through organizing English learning activities for communication with B – SLIM Model. The sample of this study was purposively selected from 35 of first year students in English for International Communication major, Rajamangala University of Technology Tawan-ok in Integrated Skills in the English Language course of 2021 academic year. The research instruments consisted of an essay writing assignment of at least 500 words, a form for writing error record, students' satisfaction with B-Slim Model learning activities and focus group activity to identify the causes of errors. The results based on the writing errors indicated errors before taking course including subject-verb agreement (33.10%), sentence boundaries (30.31%), and clause boundaries (36.59%) and after finishing course as follows: subject-verb agreement (9.82%), sentence boundaries (30.36%), and clause boundaries (59.82%). The efficiency of the English learning management based on B-SLIM Model was 73.45/80.32, which was higher than the criteria set at 70/70. The ability of English writing after learning using B-SLIM Model was better than writing without applying B-SLIM Model. The satisfaction the students taught by B – SLIM Model was most satisfied at (x) = 4.63 (S.D=1.44).&nbsp; Errors committed in English writing were mainly caused by L1 interference and limited knowledge of English structure