Hello everyone, welcome to Big Data and Language. Today let's talk about Intuitions versus Big Data. So which one do you think more important, Intuitions or Big Data? Do you think one Is more important than the other? Let's think about that one by one. So let's talk about the benefits of intuitions first. So intuitions are always useful in linguistics in order to invent grammatical, ungrammatical, or questionable examples sentences for linguistic analysis. For example, let me give you one sentence: two apple is delicious. Do you think this sentence is grammatically correct or not? One more time, two apple is delicious. You may say that, "No, Dr. Park. This one is not grammatically correct." But why? When did you notice that this sentence is grammatically not correct? Maybe you just automatically found that this is not grammatically correct. So your intuition actually is used to find the ungrammatical example. Do you want me to correct this sentence? Of course. Two apples are delicious. We can actually use not only intuitions like any kind of grammatical knowledge if you have, so two means plural, more than one. So we should use apples, plural, instead of apple, singular, and is, is not correct. So subject verb agreement instead of is, are important, something like that. Before we analyzing those kind of grammatical errors, you probably just noticed that this sentence is not grammatically correct because of your intuitions. So now let's move to the second benefit of intuitions. Intuitions are always useful in linguistics in order to make judgments about the acceptability, grammaticality, or meaning of an expression. For example, do you think this expression is suitable when you talk to your professor, "Hey dude. what's up?" Maybe not. You'll probably notice that again right away. Oh, my god. If you say like that, probably your professor may not like you. So what's the reason? "Hey dude, what's up?" This one is not proper, or not suitable, or not appropriate for formal relationship. This expression is pretty suitable only for a casual relationship. So this one, those kinds of, like whether, you can check the acceptability of the language or expressions right away with your intuition. Now, let's move to the third benefit of intuitions. Intuitions are always useful in linguistics in order to help with categorization. For example, if I say apples, bananas, and oranges, you probably imagine that or categorize those three words as a category, fruit. So your intuition is also used when you categorize any kind of words, or expressions, or even reading articles. However, intuitions with caution. You should be careful when you use intuitions, which means intuition should be applied with caution because probably biased as they are likely to be influenced by one's dialect or sociodialect. For example, if you are from the third to reason, then you may do not have enough knowledge or any kind of other dialects. So you may just check or determine something based on your own dialect or social dialect. Also in addition, introspective data is artificial and may not represent typical language use as one is consciously more monitoring one's language production. So you need to be careful when you use intuitions. The second caution, introspective data is decontextualized because it exists in the analyst's mind rather than in the real linguistic context, which means you just focus on their specific expression or maybe you do not think about the content or the other context. Intuitions are not observable and verifiable by everyone. So that's why using intuition is pretty sometimes not safe or you should be very careful. Also, excessive reliance on intuitions blinds the analyst to the realities of language usage because we tend to notice the unusual but overlook the commonplace, which means that if you pretty focus on unusual thing, but actually the real language situation. It's more like the car we use. They're common expressions. There are areas in linguistics where intuitions may not be used reliably. For example, historical linguistics, maybe data would be better than just using your intuitions, or language variation. The human beings have only the vaguest notion of the frequency of the construct or a word. For example, do you know how often we use the article, the? Maybe per million words. You don't know or you may not have the specific value or frequency numbers. However, the Big Data, it shows the general, or the mean, or average frequency of the the per million words in certain genre. Now, so far we've talked about the benefits of intuitions and also the intuitions with caution. So now let's move to the benefits of Big Data. The first benefit is Data is more reliable. What that mean is, data pools together linguistic intuitions of a range of language speakers, which offset the potential biases in intuitions of individual speakers. So based on the Big Data or based on the data there, we see that the finding is more reliable. The second, the data is more natural. So it is used in real communications instead of being invented specifically for linguistic analysis. So based on the natural context, then we can understand the linguistic features. Now let's move to the third benefit of Big Data. Data is contextualize, as I already mentioned that, attested language use which has already occurred in real linguistic contexts. The fourth benefit is data is quantitative. We can get the number. Data can provide frequencies and statistics readily. The fifth one is data can find differences that intuitions alone cannot perceive. For example, what is the synonyms of very? Do you have any specific thing right away? Maybe not, but from the data you can see that okay, so we have those kind of synonyms of very such as highly or extremely, something like that. So far today, we've talked about Intuitions versus Big Data. So what do you think? Do you think one is more important than the others? I don't think so. Both has their strengths, or weaknesses, advantages, and disadvantages. So we may need to use both intuitions and data. Next, we will talk about Data and Language. Thank you for your attention.
