Analysis of Li-Young Lee’s Persimmons
The speaker in Li-Young Lee’s poem “Persimmons” has been clearly raised in a bi-lingual, bi-cultural atmosphere. His experiences, although not entirely positive, have helped him grow into the man he is today. By using sensory imagery and “precise” diction along with the informal stanza structure, the speaker shows the reader that, despite his bi-cultural past, he now has realized, thanks to his experiences, that some of the most important things are not “visible” and that he is indeed proud of his ethnicity.
The poem begins with a painful memory from the speaker’s sixth grade classroom where he was slapped on the head and commanded to sit in the corner for not knowing the difference between the two words “persimmon” and “precision.” Immediately the speaker’s attitude is that of confusion.
The next stanza, however, proves that he does in fact know the difference between the two words. The speaker shows his understanding of “precision” in choosing the diction to describe how to choose and eat a persimmon. The words “soft,” “sweet,” “sniff,” “suck,” and “swallow” all alert one’s senses. The alliteration further proves the speaker’s “precision.” The speaker then leaves the reader with a feeling of fulfillment after having explained how to “peel the skin tenderly, not to tear the meat” of the persimmon.
The speaker’s attitude, like the reader’s, is proven here to be that of gratification, too. Although he suffered through rocky situations in the past because of his bi-cultural upbringing, he has proven that he has overcome these experiences by clarifying his present knowledge of the difference between “persimmon” and “precision.”
The next stanza further proves he has overcome his...
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...peaker, is his “bi-cultural” heritage.
The fact that this is the last stanza and the italicized font demonstrate that the speaker has come to an understanding of this realization and that he is okay with it. Even though his “bi-cultural” lifestyle had brought him some hardships in the past, he has overcome them and now appreciates his heritage.
Through the use of “precise” diction, vivid sensory imagery and a natural flowing stanza structure, the speaker in Li-Young Lee’s poem “Persimmons” has literally showed the reader how his attitude has developed over time from confusion, to contentment to deep gratification. The speaker has also helped the reader understand that sometimes in life, the most important things are not things we can “see,” but are rather things we can feel.
WORKS CITED
Lee, Li-Young. “Persimmons.” Rose: Poems. Brockport: BOA, 1986.
Chang-Rae Lee’s Native Speaker expresses prominent themes of language and racial identity. Chang-Rae Lee focuses on the struggles that Asian Americans have to face and endure in American society. He illustrates and shows readers throughout the novel of what it really means to be native of America; that true nativity of a person does not simply entail the fact that they are from a certain place, but rather, the fluency of a language verifies one’s defense of where they are native. What is meant by possessing nativity of America would be one’s citizenship and legality of the country. Native Speaker suggests that if one looks different or has the slightest indication that one should have an accent, they will be viewed not as a native of America, but instead as an alien, outsider, and the like. Therefore, Asian Americans and other immigrants feel the need to mask their true identity and imitate the native language as an attempt to fit into the mold that makes up what people would define how a native of America is like. Throughout the novel, Henry Park attempts to mask his Korean accent in hopes to blend in as an American native. Chang-Rae Lee suggests that a person who appears to have an accent is automatically marked as someone who is not native to America. Language directly reveals where a person is native of and people can immediately identify one as an alien, immigrant, or simply, one who is not American. Asian Americans as well as other immigrants feel the need to try and hide their cultural identity in order to be deemed as a native of America in the eyes of others. Since one’s language gives away the place where one is native to, immigrants feel the need to attempt to mask their accents in hopes that they sound fluent ...
Privacy Preserving Data Mining (PPDM) was proposed by D. Agrawal and C. C. Agrawal [1] and by Y. Lindell and B. Pinkas [5] simultaneously. To address this problem, researchers have since proposed various solutions that fall into two broad categories based on the level of privacy protection they provide. The first category of the Secure Multiparty Computation (SMC) approach provides the strongest level of privacy; it enables mutually distrustful entities to mine their collective data without revealing anything except for what can be inferred from an entity’s own input and the output of the mining operation alone by Y. Lindell and B. Pinkas in [5], J. Vaidya and C.W.Clifton in [6]. In principle, any data mining algorithm can be implemented by using generic algorithms of SMC by O.Goldreich in [7].However, these algorithms are extraordinarily expensive in practice, and impractical for real use. To avoid the high computational cost, various solutions those are more efficient than generic SMC algorithms have been proposed for specific mining tasks. Solutions to build decision trees over the horizontally partitioned data were proposed by Y. Lindell and B. Pinkas in [5]. For vertically partitioned data, algorithms have been proposed to address the association rule mining by J. Vaidya and C.W.Clifton in [6], k-means clustering by J. Vaidya and C. Clifton in[8], and frequent pattern mining problems by A.W.C. Fu, R.C.W. Wong, and K. Wang in [9]. The work of by B. Bhattacharjee, N. Abe, K. Goldman, B. Zadrozny, V.R. Chillakuru, M.del Carpio, and C. Apte in [10] uses a secure coprocessor for privacy preserving collaborative data mining and analysis. The second category of the partial information hiding approach trades pr...
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Jiawei Han, Jian Pei, and Yiwen Yin, ’ Mining Frequent Patterns without Candidate Generation’, SIGMOD, 2000.