The Operators of the Two Corpus Tools

After analyzing target users and interfaces of the two corpus tools, this section focuses on learners’ attitudes toward the operators of the two corpus tools. It appears that learners believed Netspeak’s operators were considered to be more useful than Linggle’s operators for correcting the ten types of error.

Learners strongly agreed that the five operators provided by Netspeak were extremely useful for correcting the ten types of error. Although learners reported that they spent a lot more time screening through the results without the operators to search for parts of speech, they were still quite satisfied with the five operators and felt these five operators were very useful for them.

As for Linggle’s operators, learners somewhat agreed that they were useful for correcting the ten types of error. Learners reported that Linggle is functionally

comprehensive and useful for correcting the ten types of error. Nonetheless, all of the learners stated that they needed a longer period of time to be familiar with all the operators because many operators are provided. However, this does not mean that learners were not satisfied with Linggle’s operators. Most learners explained that if they were allowed to use Linggle for a longer period of time, they believed these

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operators would be extremely useful for them. On the contrary, only one learner claimed that he preferred to master a few instead of many operators.

To conclude for overall learners’ attitudes toward the operators of the two corpus tools, although learners appears to have slightly different viewpoints toward the operators of the two corpus tools after using them within a short period of time, longer effects remain unknown. Thus, future study could analyze learners’ attitudes toward Netspeak and Linggle’s operators after using them for a longer period of time.

To understand learners’ attitudes toward the operators of the two corpus tools, the most and the least useful operators of the two corpus tools were reported by learners.

First, learners believed the operators to compare options were the most useful operators to correct the ten error types, such as the [ ]-operator on Netspeak and the operator “/” on Linggle. These operators helped learners compare the frequencies of two or more possible usages and served as reference for learners to choose the answers. In addition, a learner preferred the symbol that stands for comparing options on Linggle (“/” ) than the one on Netspeak ( “[ ]”) because the former is more intuitive and easy to associate the symbol with the meaning. However, learners found that this option may not be useful for comparing more than two separate words. For example, when learners compared “each other” with “the other” on Netspeak, the frequencies would only show “the”, “other”, and “each” instead of “each other” and

“the other”. On the other hand, the results would change into “each the other” with

“the other other”, which could be used to check their frequencies.

Secondly, most learners reported that the operators to search for one word were useful for correcting the ten error types, such as the “_” operator on Linggle and the ?-operator on Netspeak. It is revealed that the searching results seemed clear because only one word before or after this operator would be shown immediately.

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If learners could choose from to either search for one word or one word with specific parts of speech, more learners tended to choose the one with specific parts of speech to be the most useful operator. Thus, when learners used Linggle, they

believed “POS” operators were considered as the most useful operators to correct the ten types of error. These operators also allow learners easy accesses to search for one word with specific parts of speech. It is reported that “POS” operators could be extremely useful for intermediate learners who have mastered basic vocabulary, language rules, and sentence structures in English. Otherwise, learners may not be able to use “POS” operators to correct answers if they were not familiar with these sentences structures and grammatical terms. On the other hand, another learner claimed that the ?-operator was the least useful operator. The reason was that a long list of words with all kinds of parts of speech would be shown after searching for one word on Netspeak, which was difficult to choose correct usages from them. Moreover, one learner further reported that the operator “?” on Netspeak are more easy to

associate with the meaning compared to the operator “_” on Linggle.

The third finding is about the operators that learners believed were not useful.

Learners believed the operators to search for many words were not useful, which is the ...-operator on Netspeak and the operator “*” on Linggle. Too many possible choices would be found so learners reported that they may not be patients enough to screen through all the possible answers. Moreover, learners mentioned that they had less confident in finding answers from all kinds of possible answers. After comparing this result with the finding mentioned earlier, learners prefer to search for one word than to search for many words, suggesting that learners preferred to search for answers in a more precisely way.

Fourthly, two operators were also considered not useful. For the { }-operator

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on Netspeak, it could only be used to correct word order errors rather than all ten types of error. However, it was considered extremely useful for correcting word order errors. In addition, the operator “?” on Linggle was seen as not that useful to correct the ten types of error. It is said that sometimes no satisfying results could be found or no results were shown after learners tried the operator “?” from time to time.

Finally, a conflicted result was found. Learners believed that Netspeak’s operator to search for synonyms (the #-operator) was useful, whereas Linggle’s operator to search for synonyms (the operator “~”) was considered as one the least useful operators. To explain this inconsistency, learners revealed that Netspeak provides fewer synonyms than Linggle, but more satisfying results were found on Netspeak because more words with both similar parts of speech and meanings could be found.

On the other hand, learners stated that more synonyms were shown on Linggle.

Nonetheless, learners felt confused when they saw synonyms with same parts of speech but different meanings on Linggle, or conceptually related words, were shown in the beginning of the searching results. For example, when they searched for the synonyms of “somewhere”, they saw the first two synonyms were “just” and “still”.

However, learners found “elsewhere” and “everywhere” shown on the bottom of the results.

The results of the operators to search for synonyms with previous finding, and two several findings are presented as follow. The present study’ findings are similar to Potthast and other’s results (2014), showing that Netspeak’s operator to search for synonyms outperformed the other operators. In addition, learners believed Netspeak’s operator to search for synonyms were very helpful. However, it is slightly different from another study. Boisson and others (2013) indicated that Linggle provides more synonyms than Netspeak because of the conceptually related words (Boisson et al.,

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2013). Although learners agreed that they found more words on Linggle, they seemed not to be accustomed to seeing conceptually related words. Learners revealed that they were more satisfied with synonyms with similar meaning and same parts of speech as the ones shown on Netspeak rather than finding conceptually related words on Linggle. To sum up, learners preferred to find synonyms with similar meaning and same parts of speech even few results could be found. In addition, learners were not accustomed to finding conceptually related words when they searched for synonyms.

To provide another possible solution, learners suggested that they preferred to have two kinds of operators to search for synonyms. One would allow users to search for conceptually related words with same parts of speech; the other operator would enable learners to search for synonyms with same part of speech and similar meanings.