Why NLTK lemmatization has wrong output even if verb.exc has added right value?
来源： https://stackoverflow.com/questions/33594721/why-nltk-lemmatization-has-wrong-output-even-if-verb-exc-has-added-right-value
wordnet
Why NLTK lemmatization has wrong output even if verb.exc has added right value?
来源： https://stackoverflow.com/questions/33594721/why-nltk-lemmatization-has-wrong-output-even-if-verb-exc-has-added-right-value
Why NLTK lemmatization has wrong output even if verb.exc has added right value?
来源： https://stackoverflow.com/questions/33594721/why-nltk-lemmatization-has-wrong-output-even-if-verb-exc-has-added-right-value
Why NLTK lemmatization has wrong output even if verb.exc has added right value?
来源： https://stackoverflow.com/questions/33594721/why-nltk-lemmatization-has-wrong-output-even-if-verb-exc-has-added-right-value
lemmatize an entire column using lambda function
来源： https://stackoverflow.com/questions/63807411/lemmatize-an-entire-column-using-lambda-function
what are the different techniques for comparing 2 words semantically? Which one is the best among them?
问题 Right now, I am at the starting point of a project in which I am supposed to compare two words semantically. I came to know about WordNet wherein we find distance between words to find how similar they are in terms of their meaning. It would be really helpful if you can suggest some more techniques and which method would be the best one. 来源： https://stackoverflow.com/questions/18570378/what-are-the-different-techniques-for-comparing-2-words-semantically-which-one
what are the different techniques for comparing 2 words semantically? Which one is the best among them?
问题 Right now, I am at the starting point of a project in which I am supposed to compare two words semantically. I came to know about WordNet wherein we find distance between words to find how similar they are in terms of their meaning. It would be really helpful if you can suggest some more techniques and which method would be the best one. 来源： https://stackoverflow.com/questions/18570378/what-are-the-different-techniques-for-comparing-2-words-semantically-which-one
Rewriting sentences while retaining semantic meaning
问题 Is it possible to use WordNet to rewrite a sentence so that the semantic meaning of the sentence still ways the same (or mostly the same)? Let's say I have this sentence: Obama met with Putin last week. Is it possible to use WordNet to rephrase the sentence into alternatives like: Obama and Putin met the previous week. Obama and Putin met each other a week ago. If changing the sentence structure is not possible, can WordNet be used to replace only the relevant synonyms? For example: Obama met
wordnet lemmatizer in NLTK is not working for adverbs [duplicate]
问题 This question already has answers here : Getting adjective from an adverb in nltk or other NLP library (2 answers) Closed 5 years ago . from nltk.stem import WordNetLemmatizer x = WordNetLemmatizer() x.lemmatize("angrily", pos='r') Out[41]: 'angrily' Here is reference documnetation for pos tags in nltk wordnet, http://www.nltk.org/_modules/nltk/corpus/reader/wordnet.html I may be missing some basic things. Please let me know 回答1: Try: >>> from nltk.corpus import wordnet as wn >>> wn.synset(
cs224n lesson1 note
本节课结合了旧版的前半部分（NLP的基本概念的内容）以及新版的绝大多数内容。 0. 本节课程计划 NLP的基本概念和人类语言的本质。 什么是深度学习。 课程安排。 为什么NLP难？ NLP的应用。 1. NLP的基本概念 1.1 什么是NLP？ NLP是计算机科学、人工智能、语言学的交叉学科。 它的目标是让计算机理解人类语言，从而完成有意义的任务。例如： 下订单或者买东西 知识问答，如Siri、谷歌助手、微软小冰等 完全理解和表达语言的内涵是极其困难的，完美的语言理解等效于实现完备的人工智能系统。 1.2 NLP的层次 语言的输入有两种方式，一种是语音识别，一种是文本(OCR和分词)。接下来是形态学(Morphology)，简单可理解为对英文单词进行形态变换，如act->action。 接下来是语法分析和语义分析。最后是篇章处理，它更加注重于通过上下文进行理解。 本门课主要注重于画圈的三大部分，尤其是后两部分（语法分析和语义分析）。 1.3 自然语言处理应用 推荐和广告依然是AI在互联网行业落地最早也最为成熟的两个方向。所以将NLP技术利用于这两个方向也是顺其自然的事情。具体来说： 搜索(拼写自动纠正) 在线广告匹配 现如今NLP最火热的两个方向分别是知识图谱和智能客服（聊天机器人）。 1.4 人类语言的特殊之处 自然语言与信号处理