Thus common A,B is the unigrams that both skill descriptions share, and description A,B is the union of the unigrams from both skill descriptions. The words are stemmed first so that the words with the same root e. A stop-word list is also used so that the commonly used words in most of the documents e.
A more formal evaluation of this approach will be presented in Section 4 where the similarity results for 75 pairs of skills will be evaluated against human judgments. Thus, we can see that the similarity computation would be more accurate if it matches on corresponding semantic roles, instead of matching key words from any places in the skill descriptions. We also want to concentrate more on the matching between important semantic roles, i.
Our goal is to extract all such semantic role patterns for all the skill descriptions. We started with the skill titles first, which summarize the skill descriptions and use a limited number of skill verbs. Our corpus is from such a different domain and there are many domain-specific terms in the skill descriptions.
Given this, we would expect an even worse performance from these automatic semantic role taggers. Moreover, the semantic role information we need to extract is more detailed and deep than most of the automatic semantic role taggers can identify and extract e.
The input needed for the semantic role parser is syntactic parse trees generated by a syntactic parser from the original skill titles in natural language. However, among all the titles, of them were not parsed as verb phrases, an a priori requirement, After examining the error cases, we found that abbreviations are used very widely in the skill titles.
So the first step of the preprocessing was to expand abbreviations. There are valid abbreviations identified by the expertise taxonomy team. However, we found many abbreviations that appeared in the skill titles but were not listed there.
Since most of the abbreviations are not words in a dictionary, in order to find the abbreviations that appear frequently in the skill titles, we first found all the words in the skill titles that were not in WordNet. By this approach, we added another abbreviations to the list a total of We realized the reason for this error was that all the words, except for prepositions, are capitalized in the original titles, and the parser tends to tag them as proper nouns.
To solve this problem, we changed all the capitalized words to lower case, except for the first word and the acronyms words that have all letters capitalized e. After applying these two steps of preprocessing i. This time, skills were not parsed as verb phrases -- more than additional skills were parsed as verb phrases after the preprocessing.
This was quite promising. When we examined the error cases more closely this time, we found that the errors occur mostly when the skill verbs can be both a noun and a verb e. In those cases, the parser may parse the entire title as one noun phrase, instead of a verb phrase. After applying this additional step of preprocessing, we parsed the skill titles again. This time, only 28 skills were not parsed as verb phrases, which is a significant improvement. Its parse tree has the same structure as the Charniak parser.
Figure 3. It represents sentence structures as a set of dependency relationships. Thus, our evaluation of the parses is only on the correctness of the bracketing and the POS of the phrases NP or VP , not the total correctness of the parses.
To our task, the correctness of the prepositional attachments is especially important for extracting accurate semantic role patterns. To evaluate the performance of the parsers, we randomly picked skill titles from our corpus, preprocessed them, and then parsed them using the four different parsers.
We then evaluated the parses using the above evaluation measures. The parses were rated as correct or not. No partial score was given. Table 1 shows the evaluation results, where both the published accuracy and the accuracy for our task are listed. Parser Evaluation and Comparison Results. From the results, we can see that all the parsers perform worse for our task than their published results. After analyzing the error cases, we found out the reasons are 1 Many domain specific terms and acronyms.
Since there were so many such phrases in the test set and in the corpus, this kind of error significantly reduced the performance for our task. From the evaluation and comparison results we can see that the Charniak parser performs the best for our domain. Since our goal is to use the syntactic parses to extract semantic role patterns, the bracketing information i.
Thus, we decided to use the Charniak parser for our task. Our initial attempt at this matching process was to match the skill verbs. The simplest approach is to count the number of nodes on the shortest path between two concepts in the taxonomy Quillian, The fewer nodes on the path, the more similar the two concepts are.
Despite its simplicity, this approach has achieved good results for some information retrieval IR tasks Rada et al. The assumption for this shortest path approach is that the links in the taxonomy represent uniform distances. However, in most taxonomies, sibling concepts deep in the taxonomy are usually more closely related than those higher up. Different approaches have been proposed to discount the depth of the concepts to overcome the problem.
Budanitsky and Hirst thoroughly evaluated six of the approaches i. For our task, we compare two approaches to computing skill verb similarities: shortest path vs. Since the words are compared based on their specific senses, we first manually assign one appropriate sense for each of the 18 skill verbs from WordNet. We then use the implementation by Pedersen et al.
Table 2 and 3 show the top nine pairs of skill verbs with the highest similarity scores from the two approaches. We can see that the two approaches agree on the top four pairs, but disagree on the rest in the list.
A domain-specific taxonomy for skill verbs may improve the performance. Shortest Path Results Table 3. Evaluation In order to evaluate our approach to semantic similarity computation of skill descriptions, we first conducted experiments to evaluate how humans agree on this task, providing us with an upper bound accuracy for the task.
These 75 skill pairs are then given to three raters to independently judge their similarities on a 5 point scale -- 1 as very similar, 2 as similar, 3 as neither similar nor dissimilar, 4 as dissimilar, and 5 as very dissimilar.
Since this 5 point scale is very fine-grained, we also convert the judgments to more coarse-grained, i. Since the judgment on the 5 point scale is ordinal data, the weighted kappa statistic Landis and Koch, is used to take the distance of disagreement into consideration e.
The inter-rater agreement results for both the fine-grained and coarse-grained judgments are shown in Table 4. In general, a kappa value above 0. We can see that the agreement on the fine-grained judgment is moderate, whereas the agreement on the coarse-grained judgment significant.
Inter-Rater Agreement Results. From the inter-rater agreement evaluation, we can also get an upper bound accuracy for our task, i. For our task, the average P A for the coarse-grained binary judgment is 0.
Considering the reliability of the data, only the coarse-grained binary judgments are used. The gold standard is obtained by majority voting from the three raters, i. We first evaluated the standard statistical approach described in Section 3.
Among 75 skill pairs, 53 of them were rated correctly according to the human judgments, i. The error analysis shows that the many of the errors can be corrected if the skills are matched on their corresponding semantic roles.
We will then evaluate the utility of the extracted semantic role information using a rule-based approach, and see whether it can improve the performance. However, both ways have their drawbacks: 1 Match on the entire semantic role. It's a too strict matching criterion. It may not only miss the similar ones, for example, Perform [Web Services Planning] 6 head noun: planning Perform [Web Services Assessment] head noun: assessment but also misclassify the dissimilar ones as similar, for example, Advise about [Async Transfer Mode ATM Solutions] head noun: solutions Advise about [CTI Solutions] head noun: solutions In order to solve these problems, we used a simple matching criterion from Tversky : use only the common features for determining similarity.
We set a threshold of 0. We apply this criterion to only the text contained in the most important semantic role concept. The words in the calculation are preprocessed first: abbreviations are expanded, stop- words are excluded e.
Words connected by punctuation e. We have also evaluated this approach on our test set with the 75 skill pairs. Among 75 skill pairs, 60 of them were rated correctly i.
Lin's approach is suitable for computing a ranked list of similar pairs. Although WordNet is a poplular resource for the noun similarity computation, there are many domain-specific terms and acronyms in our data that are not in WordNet, so a domain ontology may be needed for such approximate matches. Again, more domain knowledge would be needed to distinguish such cases. Conclusion In this paper, we have presented our work on a semantic similarity computation for skill descriptions in natural language.
We compared and evaluated four different natural language parsers for our task, and matched skills on their corresponding semantic roles extracted from the parses generated by one of these parsers. The evaluation results showed that the skill similarity computation based on semantic role matching can outperform a standard statistical approach and reach the level of human agreement.
References A. Budanitsky and G. Computational Linguistics. Assessing agreement on classification tasks: the kappa statistic. Computational Linguistics, 22 2 — A maximum-entropy-inspired parser. A coefficient of agreement for nominal scales. Krashen suggested that a high level of anxiety in adults might be the cause of seemingly lower levels of competencies and performance. Therefore, to lower it, Krashen points out that the theory comprehensive input is a necessary provision for second language acquisition to occur Krashen, , , Comprehensible input requires repetitions, confirmation, clarifications, modified stuctures used for interactions, and should focus on the 'here and now' Long, Harley stated that children who learn the language in natural settings as they interact with speakers of the native language at play or other more relaxed social environments are more successful.
Linguists are also of the opinion that the age of ELLs at the university level can be a barrier for quick language acquisition. Should we- English teachers use integrated skill approach? Is there any statistically significant gender differences existing in 'Imroving general level of English'?
In which skills do the learners use more effective strategies? Literature Review Language educators have long used the concepts of four basic language skills: Listening, Speaking, Reading, Writing.
These four language skills are sometimes called the "macro-skills". This is in contrast to the "micro-skills", which are things like grammar, vocabulary, pronunciation and spelling. The four basic skills are related to each other by two parameters: the mode of communication: oral or written and the direction of communication: receiving or producing the message. Listening comprehension is the receptive skill in the oral mode.
When we speak of listening what we really mean is listening and understanding what we hear. Speaking is the productive skill in the oral mode. It, like the other skills, is more complicated than it seems at first and involves more than just pronouncing words.
Speaking is often connected with listening. For example, the two-way communication makes up for the defect in communicative ability in the traditional learning.
Temple and Gillet also emphasize the close relationship between listening and speaking in this way: Listening cannot be separated from the expressive aspects of oral communication. Listening is as much a part of group discussions, dramatic play, or puppetry, for example, as the dialogues and actions created. When children develop their communicative powers they also develop their ability to listen appreciately and receptively. It can develop independently of listening and speaking skills, but often develops along with them, especially in societies with a highly-developed literary tradition.
Reading can help build vocabulary that helps listening comprehension at the later stages, particularly. Writing is the productive skill in the written mode. It, too, is more complicated than it seems at first, and often seems to be the hardest of the skills, even for native speakers of a language, since it involves not just a graphic representation of speech, but the development and presentation of thoughts in a structured way. The whole-language theoreticians strongly imply that all aspects of language interrelate and intertwine.
They further claim that students should be given the opportunity to simultaneously use all language arts listening, speaking, reading, and writing in meaningful, functional, and cooperative activities Carrasquillo, ; Farris, ; Farris and Kaczmarski, These activities are often centered around topics that build upon students' background knowledge Edelsky et al.
In recent years we have seen the emergence of several diverse teaching methodologies. Each one is attracting practitioners who often contend that their particular technique is superior, to the exclusion of the others.
However, despite the claims of these proponents, no single methodology adequately addresses the needs of all English-language students. On the contrary, evidence gained from practical experience strongly suggests that the strong points of a variety of methodologies, if skillfully combined, can complement one another, together forming a cohesive, realistic, and highly motivational teaching strategy.
Wilhoit, Richards , cited in Omaggio, , p. We need to develop our language skills, and specifically, our academic English, in order to: Understand and make the most effective use of our study materials, develop the specialised language and vocabulary relevant to our subject, interpret assignment questions and select relevant and appropriate material for our response, write well-structured and coherently presented assignments, without plagiarism, communicate our needs to our tutors work productively with other students.
As Abdel-Salam El-Koumy points out about skills-based approach in Teaching and Learning English as a Foreign Language: A Comprehensive Approach; The skills-based approach drew its theoretical roots from behavioral psychology and structural linguistics. Specifically, it is based on the following principles: 1 The whole is equal to the sum of its parts; 2 There are differences between spoken and written language; 3 Oral language acquisition precedes the development of literacy; 4.
Language learning is teacher-directed and fact-oriented; and 5 Students' errors are just like 'sins' which should be eliminated at all costs.
In accordance with the above principles, advocates of the skills-based approach view language as a collection of separate skills. Each skill is divided into bits and pieces of subskills. These subskills are gradually taught in a predetermined sequence through direct explanation, modeling and repetition.
Furthermore, the skill-building teacher constantly uses discrete-point tests e. Nation points out that 'one of the most useful procedures is the movement from individual to pair to group to whole class activity' p.
The teaching of EFL students should be based on an integrated approach which brings linguistic skills and communicative abilities into close association with each other, this is due to the fact that both language use and language use are important. Ibrahim, , p. Research has also shown that there is a correlation between word knowledge and reading comprehension e. Hypotheses We defined the following hipotheses, based on results from previous studies and based on logical expectations: 1.
Improving general level of English, vocabulary learning, studying grammar, reading in English, writing in English and speaking in English outside class are intercorrelated statistically significant. Other five subscales of Questionnaire about the Learning English explain in total statistically significant part of variance of speaking in English outside class, i. Participants have better skills for vocabulary learning than for studying grammar and this result will be statistically significant.
Participants will report that they are better in writing in English than in reading in English and this finding will be statistically significant. We suppose that there will not exist gender differences in average results on six subscales of Questionnaire about the Learning English.
Methodology 4. Our respondents were from age group ranged from 17 to 27 years old. Gender distribution of our sample of students is displayed in Figure 1.
As we can see, there were 81 females Frequencies of males and females in the sample 4. Our participants were asked how much do they agree with each of the items in the questionnaire. The scale we used was a five-point Likert scale. Results and Discussion In order to conduct analysis which will allow us to make statistical conclusions about studied variables, we calculated descriptive values for all six subscales see Table 1. This result we could expect, because students mostly think about school grades, rather than about practical value of their knowledge and learned strategies.
Next, we examined relations between six subscales of QLE. For that purpose, we calculated Pearson's correlation coefficients. The results are shown in Table 2. We can notice that all of QLE subscales are intercorrelated statistically significant. Therefore, our scale questionnaire is very homogenous and participants answered pretty similar to the questions of all subscales. Hence, we have completely proved our first hypothesis. To test our second hypothesis, we conducted multiple regression analysis MRA.
The results are displayed in Table 3. Hence, we proved the bigger part proved our second hypothesis. In order to test our third and fourth hypothesis, we applied t-test for paired samples. The results are shown in Table 4 and Table 5. Hence, we proved the third hypothesis. To test the fifth proposed hypothesis, we conducted t-test for independent samples. The results are displayed in Table 6. That is, we have proved most part of our last hypothesis.
Conclusions 1 All QLE subscales are in statistically significant correlations with each other. Improving general level of English and Writing in English are statistically significant predictors of Speaking in English outside class context.
The average results on other variables subscales are pretty similar for males and females. In our research, the students offered that the practice should be continued and used not only in English lessons but in all other subject matters.
They expressed that interaction between teacher-student and student- student enhanced the level of interest, affection and motivation. As a result of the research, writing skill has been found to be the dominant skill focused on by the students both in teaching and measurement compared to other three language skills. The participants do not reveal a wide spectrum of varied experiences.
Significant relationships have been found between the levels of several assessment activities and genders. There are not statistically significant differences between males and females in speaking and reading comprehension for daily language, as well as reading comprehension for academic language purposes.
It is critically important that students' written language skills are assessed at certain intervals and that training programmes are developed for enhancing the written language skills. Even though it is not a vast-scale research, this study has aided to bring out very significant issues regarding the development and assessment of adult language learners at tertiary level in Turkey.
Thus, our implication from the research is that skill integration is inevitably vital where all language skills are not used separately but instead all language skills are used in every class. The integrated-skill approach, as opposed to merely segregated approach, confronts English language learners to authentic language and challenges the learners to interact naturally in the language.
If these four skills are separated from one another, a language is taught; however, if they are integrated with each other, authentic communication is taught Oxford While doing it, the English teachers are supposed to create materials and topics that meet the students' needs and interests reflecting on their current approach and evaluate the extent to which the skills are integrated.
For instance, when we teach writing as a process of drafting, revising and letting them brainstorm, all of those units must be assessed with the students' participation and need analysis, unlike administering a conventional timed essay test.
Our instructional materials, textbooks and technologies we use must promote the integration of listening, reading, speaking, and writing beside the associated skills of syntax, vocabulary and so on.
If the tapestery of all four skills is interwoven, English language learners will use them effectively for oral and written communication. Furthermore, the study results indicate that the integrated-skill approach no matter it is found in content-based or task-based instruction, can be quite motivating to students of all age groups and backgrounds through appropriate tasks.
In task-based instruction tasks are defined as activities that can stand alone as fundamental units and that require comprehending, producing, manipulating, or interacting in authentic language while attention is principally paid to meaning rather than form Nunan, The aim is to increase the collaboration and and interaction among students.
It is a very influential form of content-based instruction in our days and we can use it quite often in our classes. Suez Canal University, Egypt. Benson, P.
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