The design of a lexical difficulty filter for language learning on the internet

[[abstract]]The authors describe the design and implementation of a tool called the Lexical Difficulty Filter (LDF) intended to help learners or teachers in selecting sentences appropriate to the level of students for English vocabulary learning. The LDF serves as a bridge between learner and information resource bank. It selects suitable sentences from the output of standard English corpus concordancers. The core technology in the design of LDF is a fuzzy expert system. The results obtained are very encouraging in this pioneering work. We achieve 94.33% accuracy rate in imitating the judgments of a human expert in determining the degree of difficulty of a sentence for a given learner level[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]20010806~20010808[[conferencelocation]]USA, Madiso

Refining the use of the web (and web search) as a language teaching and learning resource

The web is a potentially useful corpus for language study because it provides examples of language that are contextualized and authentic, and is large and easily searchable. However, web contents are heterogeneous in the extreme, uncontrolled and hence 'dirty,' and exhibit features different from the written and spoken texts in other linguistic corpora. This article explores the use of the web and web search as a resource for language teaching and learning. We describe how a particular derived corpus containing a trillion word tokens in the form of n-grams has been filtered by word lists and syntactic constraints and used to create three digital library collections, linked with other corpora and the live web, that exploit the affordances of web text and mitigate some of its constraints

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

Ghost Peppers: Using Ensemble Models to Detect Professor Attractiveness Commentary on RateMyProfessors.com

In June 2018, RateMyProfessors.com (RMP), a popular website for students to leave professor reviews, removed a controversial feature known as the “chili pepper” which allowed students to rate their professors as “hot” or “not hot.” Though past research has rigorously analyzed the correlation of the chili pepper with higher ratings in other categories (Felton, Mitchell, and Stinson, 2004; Felton et al., 2008), none has measured the effect of the removal of the chili pepper on the text content submitted by students. While it is a positive step that the chili pepper has been removed, text commentary on teacher attractiveness persists and is submitted to the site through the “additional comments” text field. Using text classification and ensemble learning methods, we identify these reviews and their perpetuation after the chili pepper with high accuracy. Our analysis of 358,000 reviews from RMP representing a cross-section of professors from private and public universities across the U.S. finds two important trends: (1) the frequency of attractiveness comments in teacher reviews has been in decline over an eight-year period; and (2) the removal of the chili pepper from the web interface is significantly associated with this declining trend. These findings validate the activism behind asking web companies like RMP to remove online rating features that might seem entertaining, but foster workplace harassment and other harms

Unsupervised Learning from Narrated Instruction Videos

We address the problem of automatically learning the main steps to complete a certain task, such as changing a car tire, from a set of narrated instruction videos. The contributions of this paper are three-fold. First, we develop a new unsupervised learning approach that takes advantage of the complementary nature of the input video and the associated narration. The method solves two clustering problems, one in text and one in video, applied one after each other and linked by joint constraints to obtain a single coherent sequence of steps in both modalities. Second, we collect and annotate a new challenging dataset of real-world instruction videos from the Internet. The dataset contains about 800,000 frames for five different tasks that include complex interactions between people and objects, and are captured in a variety of indoor and outdoor settings. Third, we experimentally demonstrate that the proposed method can automatically discover, in an unsupervised manner, the main steps to achieve the task and locate the steps in the input videos.Comment: Appears in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016). 21 page

The effects of computer-mediated interaction in L2 vocabulary learning

This study investigated the effects of a computer-mediated (CM) interaction task on university level ESL learners\u27 lexical development while doing collaborative dialogues using MSN instant messenger in non-native student to non-native student (NNS-NNS) dyads. Specifically, the study examined the learners\u27 interaction by looking at MSN messenger scripts to find the negotiation routines. Also, the mean pre-test and two post-test scores were compared to assess the acquisition of new lexical items. This study investigated whether the learners used an online dictionary and whether they selected the most appropriate words in the given context. The participants were 10 (6 male, 4 female) native Koreans who were enrolled in Iowa State University. The research design included a pre-test, a treatment activity, an immediate post-test, and a 3 week delayed post-test. The pre-test containing 35 vocabulary words whose referents were food and kitchen items was given to choose the target items. The type of treatment activity used in this study was an information-gap activity in which the students were required to request and obtain information from each other to complete the task. Two post-tests were administered 1 day and 3 weeks after the treatment activity to assess the acquisition of new lexical items. Finally, each student completed a follow-up survey regarding the computer-assisted language learning (CALL) task they had performed. The result showed that the CM interaction task helped the students to acquire new lexical items, especially when they interacted with the words. Moreover, all of the students were able to negotiate the meaning of new lexical items while completing their tasks, especially on the first day activities. All of the eight target lexical items prompted negotiation for all of the dyads. In addition, most of the students reported a positive attitude towards CM interaction and that they found synchronous chat as an interesting way of learning. Moreover, the data suggested that the CM interaction task encouraged the students to use various types of interactional modifications. The online dictionary was actively, and in some instances creatively, used by the students. However, the online dictionary did not sufficiently help the learners to understand the words

Improving Ontology Recommendation and Reuse in WebCORE by Collaborative Assessments

In this work, we present an extension of CORE [8], a tool for Collaborative Ontology Reuse and Evaluation. The system receives an informal description of a specific semantic domain and determines which ontologies from a repository are the most appropriate to describe the given domain. For this task, the environment is divided into three modules. The first component receives the problem description as a set of terms, and allows the user to refine and enlarge it using WordNet. The second module applies multiple automatic criteria to evaluate the ontologies of the repository, and determines which ones fit best the problem description. A ranked list of ontologies is returned for each criterion, and the lists are combined by means of rank fusion techniques. Finally, the third component uses manual user evaluations in order to incorporate a human, collaborative assessment of the ontologies. The new version of the system incorporates several novelties, such as its implementation as a web application; the incorporation of a NLP module to manage the problem definitions; modifications on the automatic ontology retrieval strategies; and a collaborative framework to find potential relevant terms according to previous user queries. Finally, we present some early experiments on ontology retrieval and evaluation, showing the benefits of our system