For this Ngram graph, I chose to highlight which University of California schools/word associations have been mentioned more often than others. I used the Wildcard advanced search to discover this. My graph shows the most commonly mentioned University of California schools, with University of California at Berkley being the top mention. I limited the time frame to 1920-2019 because the universities were mentioned much more often after 1920.
For my second Ngram graph, I chose to display which version of the word "dance" was most commonly mentioned, the verb form or the noun form. I used the Part-of-Speech advanced search tag to distinguish this. My graph shows that "dance" was more often mentioned as a noun than as a verb, and that this trend has always been the case.
I chose the books/texts Pride and Prejudice ("panda") and Jane Eyre ("janeeyre").
I found many of the tools useful, especially just the simple summary ones that told me the Vocabulary Density (janeeyre: 0.068 and pandp: 0.052), the Average Words per Sentence (pandp: 20.7 and janeeyre: 19.7), and the Distinctive Word Differences (pandp: "darcy" 374 and janeeyre: "rochester" 317).
Otherwise, I loved playing around the different display tools like the Word Cloud, Trends, and Terms Berry:
Word Cloud
Trends
Terms Berry
Using a New York Times article on "'Ghost Guns'":
Result from a 2 paragraph selection:
- Score -2
- Comparative 0.25
- 4 positive: matter,gaining,powerful,like...
- 6 negative: disruptive,ghost,banned,gun,ill,gun...
Words I think could either be positive or negative depending on interpretation:
1. matter
2. powerful
Weighting I think that is seriously wrong:
1. gaining
2. powerful
Using text from The Scarlett Letter:
Sentences where Sentimood and the Commercial Analyzer both agree and are correct:
1. The bright morning sun, therefore, shone on broad shoulders and well-developed busts, and on round and ruddy cheeks, that had ripened in the far-off island, and had hardly yet grown paler or thinner in the atmosphere of New England. (Positive)
2. The women who were now standing about the prison-door stood within less than half a century of the period when the man-like Elizabeth had been the not altogether unsuitable representative of the sex. (Negative)
Sentences that differed markedly in their assessment:
1. "It was a circumstance to be noted, on the summer morning when our story begins its course, that the women, of whom there were several in the crowd, appeared to take a peculiar interest in whatever penal infliction might be expected to ensue.: (Sentimood: Positive & Analyzer: Negative)
2. "Morally, as well as materially, there was a coarser fibre in those wives and maidens of old English birth and breeding, than in their fair descendants, separated from them by a series of six or seven generations; for, throughout that chain of ancestry, every successive mother has transmitted to her child a fainter bloom, a more delicate and briefer beauty, and a slighter physical frame, if not a character of less[56] force and solidity, than her own." (Sentimood: Positive & Analyzer: Negative)
Agree and both appear to be clearly wrong:
1. "When the young woman-the mother of this child-stood fully revealed before the crowd, it seemed to be her first impulse to clasp the infant closely to her bosom; not so much by an impulse of motherly affection, as that she might thereby conceal a certain token, which was wrought or fastened into her dress." (Should have some negative undertone)
2. "The unhappy culprit sustained herself as best a woman might, under the heavy weight of a thousand unrelenting eyes, all fastened upon her, and concentrated at her bosom." (Should have some negative undertone)
Translating English to Mandarin (Simplified Chinese pinyin - not in character form and without tone marks because Western ASCII does not allow) and then back to English:
1) Using a hotel review for the Four Seasons Hotel Washington, D.C.: "Hotel service was amazing. Pool was the most relaxing swim experience..."
Google Translate:
English to Chinese: "Jiudian fuwu hen bang. Youyongchi shi zui fangsong de youyong tiyan."
- The first sentence is basically entirely incorrect because the translation of each word is wrong. The second sentence gets some of the words correct, but most of the grammar is wrong, so overall this translation was not great.
Chinese Back to English: The hotel service is great. The swimming pool is the most relaxing swimming experience.
- Pretty good, the adjective from the first original English sentence is different.
Bing:
English to Chinese: "Jiu dian du wu shi jing ren de. You yong chi shi zyi Qing song de yong ti yan."
- These sentences are more correct than Google Translate's translations. The translation for "hotel service," "swimming pool," and "relaxing" are accurate this time. Although there are minor incorrect grammar translations, this is a much better translation overall than Google's.
Chinese Back to English: "The hotel service is amazing. The swimming pool is the most relaxing swimming experience."
- This translation is almost exactly the same as the original English sentence, only differing in tense (the original was in past while this is in present tense).
2 Using the first few lines of the book If You Give a Mouse a Cookie: "If you give a mouse a cookie, he's going to ask for a glass of milk. When you give him the milk, he'll probably ask you for a straw. "
Google Translate:
English to Chinese: "Ruguo ni gei laoshu yikuai binggan, ta hui yao yibei niunai. Dang ni gei ta niunai shi, ta keening hui Xiang ni tao yi gen xiguan."
- This translation is a decently good translation. However, there are minor translation errors like the translation of the word "ask" and "straw," also there are also a few extra characters (words) that are unnecessary and confusing.
Chinese Back to English: "If you give the mouse a biscuit, it will ask for a glass of milk. When you give him milk, he may ask you for a straw."
- This translation is not terrible, but it messes up the original word "cookie" and uses the wrong pronouns.
Bing:
English to Chinese: "Ru guy ni gei lao she yi kai bing gan, ta hui tao yi being nit nap de. Dang ni ba nit nai gei ta shi, ta ke neng hui xiang ni tao yi gen dao cao."
- This translation follows the rules of Chinese grammar slightly better than Google translate did, yet, there are still some extra unnecessary characters. This translation gives the correct translation of "straw."
Chinese Back to English: "If you give a mouse a cookie, he's going to ask for a glass of milk. When you give him the milk, he'll probably ask you for a straw."
- This translation is perfect compared with original English sentence.
***Overall, I was much more pleased with Bing's translations from Chinese to English, and also their translations from English to Chinese. Before this lab, I was unaware that there was any translation medium that was even close to accurate when translating Chinese sentences, most mediums get the grammar entirely wrong.
Example 1: MASKS
For this experiment I wanted to test how well the computer would be able to distinguish between people wearing masks or not. In trying times like these, this kind of program could be very useful to make sure everyone is following the mandates put in place. I originally tried only using 8 photos far away, but I soon realized it would take far more examples (68 in this case) and the photos would need to be closer up.
Example 2: CHINESE v. ENGLISH
For this experiment, I tested out how well the program would be able to distinguish between Chinese and English. I chose this idea because I thought it would be useful for different technologies, tours, etc, so the person that was about to start whatever they were about to do, would be able to receive their instructions, tours, etc. in the correct language without having to write anything. It improved with more recordings. It was almost 100% accurate every time once I added 12 more recordings to each audio from the original 8.