论文爬取（三）

英语论文关键字统计
# -*- coding: utf-8 -*-
import sys

sys.path.append('../')

import jieba
import jieba.analyse
import MysqlUtil
from optparse import OptionParser

# file_name = "test.txt"
#
# content = open(file_name, 'rb').read()
# content = "Few-shot learning is an important area of research.  Conceptually, humans are readily able to understand new concepts given just a few examples, while in more pragmatic terms, limited-example training situations are common practice. Recent effective approaches to few-shot learning employ a metric-learning framework to learn a feature similarity comparison between a query (test) example, and the few support (training) examples.  However, these approaches treat each support class independently from one another, never looking at the entire task as a whole.  Because of this, they are constrained to use a single set of features for all possible test-time tasks, which hinders the ability to distinguish the most relevant dimensions for the task at hand.  In this work, we introduce a Category Traversal Module that can be inserted as a plug-and-play module into most metric-learning based few-shot learners.  This component traverses across the entire support set at once, identifying task-relevant features based on both intra-class commonality and inter-class uniqueness in the feature space.  Incorporating our module improves performance considerably (5%-10% relative) over baseline systems on both miniImageNet and tieredImageNet benchmarks, with overall performance competitive with the most recent state-of-the-art systems."
# 10表示输出的前10个
# tags = jieba.analyse.extract_tags(content, topK=10, withWeight=True)
#
# print(tags)
# print(",".join(tags))


def getKey(str):
    counts = {}
    for i in str:
        content = jieba.lcut(i[0])
        for word in content:
            if len(word) == 1 or word in nolist:  # 单个词不计算在内
                continue
            else:
                counts[word]=counts.get(word, 0)+1  # 遍历所有词语，每出现一次其对应值加1

    items = list(counts.items())  # 将键值对转化为列表
    items.sort(key=lambda x:x[1], reverse=True)  # 根据词语出现的次数进行从大到小的排序

    for i in range(20):
        word, count = items[i]
        MysqlUtil.insert_key(word, count)
        print('{0:<5}{1:<5}'.format(word, count))

    return items


if __name__ == '__main__':
    nolist ={'are','is','am','and','of','but','so','which','where','when','how','what','that','who','whose','in','at','with','of','for','the','a','an','to','on','we','We','this','by','from','our','as','in','The','can','he','He','The','be','In'}
    res = MysqlUtil.select_ab()
    # print(res[0])
    getKey(res)
论文爬取（三）

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