The most influential NLP paper on natural language processing

Recently there is time I will read from the Nlper this blog, found that "most influential NLP Papers" This article compared with reference value, but written in early 06, slightly earlier, but gold, put here for your reference.
"I conducted a mini survey recently, asking people I knew what they thought were the very influential papers in NLP from T He past and decades. Here is the wholly unscientific results, sorted from more votes and subsorted by author. Note that I is only got responses from 7 people. I ' ve not listed papers this got only one vote and has not included my personal votes. "
According to the author, he did a small survey by asking the researchers he knew about natural language processing, "the most influential natural language processing paper they had thought of in the past 20 years" to get the findings. In fact, the authors only received seven responses, and six of them were from the University of Southern California (the author's work unit) and the University of Pennsylvania. The following is the final result of the survey, sorted by the number of votes, if the number of votes is the same, according to the name of the author of the paper sort, note that it does not include only one vote of the paper and the author's own vote:
(7 votes): Brown et al., 1993; The mathematics of Statistical Machine translation (statistical MT)
(5 votes): Collins, 1997; Three generative, lexicalised Models for statistical parsing (statistical syntactic analysis)
(4 Votes): Marcus, 1993 Building A large annotated corpus of English:the Penn Treebank (Corpus)
(3 Votes): Berger et al., 1996; A maximum entropy approach to natural language processing (maximum entropy)
(2 Votes): Bikel et al., 1997; An algorithm which learns what ' s in a Name
(2 Votes): Collins, 2002; Discriminative Training Methods for Hidden Markov models:theory and experiments with Perceptron algorithms
(2 Votes): Lafferty et al., 2001; Conditional random fields:probabilistic Models for segmenting and labeling Sequence Data (conditional random field)
(2 Votes): Och, 2003; Minimum Error rate Training for statistical machine translation (statistical MT)
(2 Votes): Papineni et al., 2001; Bleu:a method for automatic evaluation of Machine translation (Mt automatic evaluation)
(2 Votes): Ratnaparkhi, 1999; Learning to Parse Natural Language with Maximum Entropy Models
(2 Votes): Yarowsky, 1995; Unsupervised Word sense disambiguation rivaling supervised Methods (word-sense disambiguation)
In parentheses, the area where I commented, machine translation accounted for three, and was estimated to be related to the University of Southern California vote.
I wonder if we can do a survey like this here. After all, the capacity of the individual is limited, and everyone's strength is endless, if we nlpers together, perhaps there will be a good survey results, for everyone and later will have some reference.
The initial idea is: if the reader is familiar with natural language processing or computational linguistics in an area, you can list the more influential several natural language processing papers, if you can get enough reply, I will finally unify these results, make a similar nlper findings.
52NLP is far from nlper so much influence, I do not know whether the investigation will eventually succeed, but I hope that the Dear Nlper can act, whether it is a article or two articles.

Note: Original article, reproduced please specify the source "I love Natural Language processing": www.52nlp.cn

From:http://www.52nlp.cn/most-influential-nlp-papers