用Python代碼自動生成文獻的IEEE引用格式的實現(xiàn)

今天嘗試著將引用文獻的格式按照IEEE的標準重新排版，感覺手動一條一條改太麻煩，而且很容易出錯，所以嘗試著用Python寫了一個小程序用于根據(jù)BibTeX引用格式來生成IEEE引用格式。

先看代碼，如下：

import re

def getIeeeJournalFormat(bibInfo):
  """
  生成期刊文獻的IEEE引用格式：{作者}, "{文章標題}," {期刊名稱}, vol. {卷數(shù)}, no. {編號}, pp. {頁碼}, {年份}.
  :return: {author}, "{title}," {journal}, vol. {volume}, no. {number}, pp. {pages}, {year}.
  """
  # 避免字典出現(xiàn)null值
  if "volume" not in bibInfo:
    bibInfo["volume"] = "null"
  if "number" not in bibInfo:
    bibInfo["number"] = "null"
  if "pages" not in bibInfo:
    bibInfo["pages"] = "null"

  journalFormat = bibInfo["author"] + \

      ", \"" + bibInfo["title"] + \

      ",\" " + bibInfo["journal"] + \

      ", vol. " + bibInfo["volume"] + \

      ", no. " + bibInfo["number"] + \

      ", pp. " + bibInfo["pages"] + \

      ", " + bibInfo["year"] + "."

  # 對格式進行調(diào)整，去掉沒有的信息，調(diào)整頁碼格式
  journalFormatNormal = journalFormat.replace(", vol. null", "")
  journalFormatNormal = journalFormatNormal.replace(", no. null", "")
  journalFormatNormal = journalFormatNormal.replace(", pp. null", "")
  journalFormatNormal = journalFormatNormal.replace("--", "-")
  return journalFormatNormal

def getIeeeConferenceFormat(bibInfo):
  """
  生成會議文獻的IEEE引用格式：{作者}, "{文章標題}, " in {會議名稱}, {年份}, pp. {頁碼}.
  :return: {author}, "{title}, " in {booktitle}, {year}, pp. {pages}.
  """
  conferenceFormat = bibInfo["author"] + \

          ", \"" + bibInfo["title"] + ",\" " + \

          ", in " + bibInfo["booktitle"] + \

          ", " + bibInfo["year"] + \

          ", pp. " + bibInfo["pages"] + "."

  # 對格式進行調(diào)整，，調(diào)整頁碼格式
  conferenceFormatNormal = conferenceFormat.replace("--", "-")
  return conferenceFormatNormal

def getIeeeFormat(bibInfo):
  """
  本函數(shù)用于根據(jù)文獻類型調(diào)用相應(yīng)函數(shù)來輸出ieee文獻引用格式
  :param bibInfo: 提取出的BibTeX引用信息
  :return: ieee引用格式
  """
  if "journal" in bibInfo: # 期刊論文
    return getIeeeJournalFormat(bibInfo)
  elif "booktitle" in bibInfo: # 會議論文
    return getIeeeConferenceFormat(bibInfo)

def inforDir(bibtex):
  #pattern = "[\w]+={[^{}]+}"  用正則表達式匹配符合 ...={...} 的字符串
  pattern1 = "[\w]+=" # 用正則表達式匹配符合 ...= 的字符串
  pattern2 = "{[^{}]+}" # 用正則表達式匹配符合 內(nèi)層{...} 的字符串

  # 找到所有的...=，并去除=號
  result1 = re.findall(pattern1, bibtex)
  for index in range(len(result1)) :
    result1[index] = re.sub('=', '', result1[index])
  # 找到所有的{...}，并去除{和}號
  result2 = re.findall(pattern2, bibtex)
  for index in range(len(result2)) :
    result2[index] = re.sub('\{', '', result2[index])
    result2[index] = re.sub('\}', '', result2[index])

  # 創(chuàng)建BibTeX引用字典，歸檔所有有效信息
  infordir = {}
  for index in range(len(result1)):
    infordir[result1[index]] = result2[index]
  return infordir

def inputBibTex():
  """
  在這里輸入BibTeX格式的文獻引用信息
  :return:提取出的BibTeX引用信息
  """
  bibtex = []
  print("請輸入BibTeX格式的文獻引用：")
  i = 0
  while i  15: # 觀察可知BibTeX格式的文獻引用不會多于15行
    lines = input()
    if len(lines) == 0: # 如果輸入空行，則說明引用內(nèi)容已經(jīng)輸入完畢
      break
    else:
      bibtex.append(lines)
    i += 1
  return inforDir("".join(bibtex))

if __name__ == '__main__':
  bibInfo = inputBibTex() # 獲得BibTeX格式的文獻引用
  print(getIeeeFormat(bibInfo)) # 輸出ieee格式

下面我來詳細說說這個代碼怎么使用。

首先，我們需要獲取到文獻的BibTeX引用格式，可以在百度學(xué)術(shù)，或者谷歌學(xué)術(shù)的應(yīng)用欄中找到，例如這里以谷歌學(xué)術(shù)舉例：

在搜索框搜索論文：Reinforcement learning to rank in e-commerce search engine: Formalization, analysis, and application，跳轉(zhuǎn)到以下頁面：

點擊“引用”，再點擊“BibTex”

跳轉(zhuǎn)到以下頁面，復(fù)制所有字符串

運行我們上面給出的代碼，在交互窗口把我們復(fù)制的字符串粘貼過去：

之后點擊兩下回車，即可得到IEEE格式的文獻引用了：

這里我分了會議論文和期刊論文種格式，大家如果想要其他引用格式，可以在我的代碼的基礎(chǔ)上進行增刪改，下面我放一些引用格式轉(zhuǎn)換的例子：

會議論文1：

Reinforcement learning to rank in e-commerce search engine: Formalization, analysis, and application

BibTeX格式：

@inproceedings{hu2018reinforcement,
title={Reinforcement learning to rank in e-commerce search engine: Formalization, analysis, and application},
author={Hu, Yujing and Da, Qing and Zeng, Anxiang and Yu, Yang and Xu, Yinghui},
booktitle={Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery Data Mining},
pages={368–377},
year={2018}
}

IEEE格式：

Hu, Yujing and Da, Qing and Zeng, Anxiang and Yu, Yang and Xu, Yinghui, “Reinforcement learning to rank in e-commerce search engine: Formalization, analysis, and application,” , in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery Data Mining, 2018, pp. 368-377.

會議論文2：

A contextual-bandit approach to personalized news article recommendation

BibTeX格式：

@inproceedings{li2010contextual,
title={A contextual-bandit approach to personalized news article recommendation},
author={Li, Lihong and Chu, Wei and Langford, John and Schapire, Robert E},
booktitle={Proceedings of the 19th international conference on World wide web},
pages={661–670},
year={2010}
}

IEEE格式：

Li, Lihong and Chu, Wei and Langford, John and Schapire, Robert E, “A contextual-bandit approach to personalized news article recommendation,” , in Proceedings of the 19th international conference on World wide web, 2010, pp. 661-670.

期刊論文1：

Infrared navigation-Part I: An assessment of feasibility

BibTeX格式：

@article{duncombe1959infrared,
title={Infrared navigation—Part I: An assessment of feasibility},
author={Duncombe, JU},
journal={IEEE Trans. Electron Devices},
volume={11},
number={1},
pages={34–39},
year={1959}
}

IEEE格式：

Duncombe, JU, “Infrared navigation—Part I: An assessment of feasibility,” IEEE Trans. Electron Devices, vol. 11, no. 1, pp. 34-39, 1959.

期刊論文2（arXiv）：

Reinforcement learning for slate-based recommender systems: A tractable decomposition and practical methodology

BibTeX格式：

@article{ie2019reinforcement,
title={Reinforcement learning for slate-based recommender systems: A tractable decomposition and practical methodology},
author={Ie, Eugene and Jain, Vihan and Wang, Jing and Narvekar, Sanmit and Agarwal, Ritesh and Wu, Rui and Cheng, Heng-Tze and Lustman, Morgane and Gatto, Vince and Covington, Paul and others},
journal={arXiv preprint arXiv:1905.12767},
year={2019}
}

IEEE格式：

Ie, Eugene and Jain, Vihan and Wang, Jing and Narvekar, Sanmit and Agarwal, Ritesh and Wu, Rui and Cheng, Heng-Tze and Lustman, Morgane and Gatto, Vince and Covington, Paul and others, “Reinforcement learning for slate-based recommender systems: A tractable decomposition and practical methodology,” arXiv preprint arXiv:1905.12767, 2019.

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