WOLFRAM

Wolfram 语言与系统参考资料中心

FindTextualAnswer[text,"question"]

提供对 question 给出最佳答案的 text 的子字符串.

FindTextualAnswer[text,"question",n]

列出最有可能的 n 个答案.

FindTextualAnswer[text,"question",n,prop]

给出每个答案的指定属性.

更多信息和选项
Details and Options Details and Options
范例  
基本范例  
范围  
选项  
PerformanceGoal  
TargetDevice  
应用  
在维基百科搜索一个普通问题的答案  
可能存在的问题  
参见
相关指南
历史
按以下格式引用：

FindTextualAnswer

FindTextualAnswer[text,"question"]

提供对 question 给出最佳答案的 text 的子字符串.

FindTextualAnswer[text,"question",n]

列出最有可能的 n 个答案.

FindTextualAnswer[text,"question",n,prop]

给出每个答案的指定属性.

更多信息和选项

  • 在 FindTextualAnswer[text,…] 中，text 可以是一个字符串、纯文本文件、一个 ContentObject 表达式或这些文本对象的列表.
  • FindTextualAnswer[text,{"question1","question2",…},…] 给出每个 questioni 的答案.
  • 属性 prop 可以为以下任意形式：
  • "String"对应于候选答案的子字符串
    "Probability"
  • 估计的答案正确的概率
    • "Position"
  • 答案 text 中的起始位置和结束位置
    • "Sentence"含有候选答案的句子
    "Line"含有候选答案的行
    "Paragraph"含有候选答案的段落
    "HighlightedSentence"其中候选答案被突出显示的句子
    "HighlightedLine"其中候选答案被突出显示的行
    "HighlightedParagraph"其中候选答案被突出显示的段落
    {prop1,prop2,…}属性指定列表
  • 可以给出下列选项：
  • TargetDevice "CPU"执行计算的目标设备
    PerformanceGoal Automatic优化的目标
  • PerformanceGoal 可能的设置包括 "Speed" 和 "Quality".
  • FindTextualAnswer 试图利用统计方法找到答案. 文字和问题的细节可能会影响其性能.
  • FindTextualAnswer 可能会下载一些资源，将其保存在 $LocalBase 的本地对象存储，可以用 LocalObjects[] 来显示或用 ResourceRemove 将其移除.

范例

打开所有单元 关闭所有单元

基本范例  (2)

回答句子中的问题：

Wolfram Language code: FindTextualAnswer["Paris is the capital and most populous city of France, with a 2015 population of 2,229,621.", "How many people live in Paris?"]

回答段落中的问题：

Wolfram Language code: FindTextualAnswer["The dodo (Raphus cucullatus) is an extinct flightless bird that was endemic to the island of Mauritius, east of Madagascar in the Indian Ocean. The dodo's closest genetic relative was the also extinct Rodrigues solitaire, the two forming the subfamily Raphinae of the family of pigeons and doves. The closest living relative of the dodo is the Nicobar pigeon. A white dodo was once thought to have existed on the nearby island of Réunion, but this is now thought to have been confusion based on the Réunion ibis and paintings of white dodos. Subfossil remains show the dodo was about 1 metre (3 ft 3 in) tall and may have weighed 10.6–17.5 kg (23–39 lb) in the wild. The dodo's appearance in life is evidenced only by drawings, paintings, and written accounts from the 17th century.", "How big was the dodo?"]

范围  (6)

从文本中找出多个可能的答案：

Wolfram Language code: FindTextualAnswer["Paris is the capital and most populous city of France, with a 2015 population of 2,229,621.", "How many people live in Paris?", 2]//TableForm

找出多个可能的答案及其概率：

Wolfram Language code: FindTextualAnswer["Paris is the capital and most populous city of France, with a 2015 population of 2,229,621.", "How many people live in Paris?", 2, {"Probability", "String"}]//TableForm

回答几个问题：

Wolfram Language code: FindTextualAnswer["Paris is the capital and most populous city of France, with a 2015 population of 2,229,621.", {"How many people live in Paris?", "In which country is Paris?"}]

找出含有答案的句子：

Wolfram Language code: FindTextualAnswer["John Smith is co-founder (with cognitive psychologist Abigail Housen) of Visual Understanding in Education, a nonprofit educational research organization that develops and studies programs that train teachers to use art to teach thinking. He is the author of several children's books about art and is co-author with Abigail Housen of an art-based elementary curriculum, Visual Thinking Strategies", "What did John write?", 1, "Sentence"]

获取答案在问题中的位置：

Wolfram Language code: text = "Paris is the capital and most populous city of France, with a 2015 population of 2,229,621.";
Wolfram Language code: FindTextualAnswer[text, {"How many people live in Paris?", "In which country is Paris?"}, 1, {"String", "Position"}]
Wolfram Language code: StringTake[text, {82, 90}]
Wolfram Language code: StringTake[text, {48, 53}]

回答几个问题，并在上下文中显示答案：

Wolfram Language code: context = "The population of Paris in its administrative city limits was 2,241,346 in January 2014. Paris is the capital and most populous city of France, with a 2015 population of 2,229,621. By the 17th century, Paris had become one of Europe's major centres of finance, commerce, fashion, science, and the arts, a position that it retains still today"; questions = {"What is Paris?", "When did Paris have a population of 2.24 million?", "Why is Paris famous in Europe?"};
Wolfram Language code: Thread[questions -> FindTextualAnswer[context, questions, 1, "HighlightedSentence"]]//TableForm

查找来自 ContentObject 表达式的问题的答案：

Wolfram Language code: contentObject = First@TextSearch["ExampleData/Text", "Alice", "ContentObject"]
Wolfram Language code: FindTextualAnswer[contentObject, "What did Alice take in her hands?", 2, {"Probability", "HighlightedSentence"}]//TableForm

选项  (2)

PerformanceGoal  (1)

用 PerformanceGoal"Quality" 获取更准确的答案：

Wolfram Language code: text = "The dodo (Raphus cucullatus) is an extinct flightless bird that was endemic to the island of Mauritius, east of Madagascar in the Indian Ocean. The dodo's closest genetic relative was the also extinct Rodrigues solitaire, the two forming the subfamily Raphinae of the family of pigeons and doves. The closest living relative of the dodo is the Nicobar pigeon. A white dodo was once thought to have existed on the nearby island of Réunion, but this is now thought to have been confusion based on the Réunion ibis and paintings of white dodos. Subfossil remains show the dodo was about 1 metre (3 ft 3 in) tall and may have weighed 10.6–17.5 kg (23–39 lb) in the wild. The dodo's appearance in life is evidenced only by drawings, paintings, and written accounts from the 17th century.";
Wolfram Language code: FindTextualAnswer[text, "Where did the dodo live?", 1, "HighlightedSentence", PerformanceGoal -> "Quality"]
Wolfram Language code: FindTextualAnswer[text, "Where did the dodo live?", 1, "HighlightedSentence", PerformanceGoal -> "Speed"]

TargetDevice  (1)

如果有的话，用缺省的系统 GPU 回答问题：

Wolfram Language code: question = "Who was the architect of the Sagrada Familia?";
Wolfram Language code: articles = WikipediaData /@ WikipediaSearch["Content" -> question];
Wolfram Language code: AbsoluteTiming[FindTextualAnswer[articles, question, TargetDevice -> "GPU"]]

计算速度常常比用 CPU 要快：

Wolfram Language code: AbsoluteTiming[FindTextualAnswer[articles, question]]

如果没有兼容的 GPU，就发送一条消息：

Wolfram Language code: FindTextualAnswer["You need a GPU device with recent drivers.", "What is needed?", TargetDevice -> {"GPU", 999}]

应用  (1)

在维基百科搜索一个普通问题的答案  (1)

在上下文中显示维基百科搜索的可能的答案：

Wolfram Language code: question = "Who was the architect of the Sagrada Familia?";
Wolfram Language code: articles = WikipediaData /@ WikipediaSearch["Content" -> question];
Wolfram Language code: FindTextualAnswer[articles, question, 5, {"Probability", "HighlightedSentence"}]

可将名字相同的答案及其概率放在一起：

Wolfram Language code: namesAndProbabilities = FindTextualAnswer[articles, question, 10, {"String", "Probability"}];
Wolfram Language code: groupedInstances = GroupBy[namesAndProbabilities, First -> Last]

总概率最大的可能会是合适的答案：

Wolfram Language code: TakeLargestBy[groupedInstances, Total, 1]

可能存在的问题  (1)

FindTextualAnswer 能够在一定程度上理解语义概念，但不能用逻辑来猜测一个数字：

Wolfram Language code: FindTextualAnswer["I have either 1, 2, 3, 4, or 5 eggs. I had 5 eggs, but lost 3 and then bought 2 new ones.", "How many eggs do I have?"]
Wolfram Research (2018)，FindTextualAnswer，Wolfram 语言函数，https://reference.wolfram.com/language/ref/FindTextualAnswer.html （更新于 2020 年）.

文本

Wolfram Research (2018)，FindTextualAnswer，Wolfram 语言函数，https://reference.wolfram.com/language/ref/FindTextualAnswer.html （更新于 2020 年）.

CMS

Wolfram 语言. 2018. "FindTextualAnswer." Wolfram 语言与系统参考资料中心. Wolfram Research. 最新版本 2020. https://reference.wolfram.com/language/ref/FindTextualAnswer.html.

APA

Wolfram 语言. (2018). FindTextualAnswer. Wolfram 语言与系统参考资料中心. 追溯自 https://reference.wolfram.com/language/ref/FindTextualAnswer.html 年

BibTeX

@misc{reference.wolfram_2026_findtextualanswer, author="Wolfram Research", title="{FindTextualAnswer}", year="2020", howpublished="\url{https://reference.wolfram.com/language/ref/FindTextualAnswer.html}", note=[Accessed: 05-September-2026]}

BibLaTeX

@online{reference.wolfram_2026_findtextualanswer, organization={Wolfram Research}, title={FindTextualAnswer}, year={2020}, url={https://reference.wolfram.com/language/ref/FindTextualAnswer.html}, note=[Accessed: 05-September-2026]}