natural language processing coursera

A very important research direction in natural language processing (NLP) is semantic affective analysis (sentiment). For example, there are a lot of comments about movies on the IMDB, so we can evaluate the reputation of a movie by sentiment analysis, if it's just released, and even predict whether it can make a box-office hit. Similar to this, the domestic water

Python-based Natural Language Processing Toolkit: NLPT (Natural Language Processing toolkit), sudo pip-u install when installed NLTKNLPT's websiteNatural Language

Python Natural Language Processing (1): NLP, nlp Python Natural Language Processing (1): NLP first recognized Natural Language

During the summer vacation, I started to study NLP. I started learning NLP from Zong Chengqing's "Natural Language Processing Statistics. I. Language: A language consists of speech, vocabulary, and syntax. Speech and text constitute two basic attributes of a

Second lecture: Simple word vector representation: Word2vec, Glove (easy word vector representations:word2vec, Glove)Reprint please specify the source and retention link "I love Natural Language processing": http://www.52nlp.cnThis article link address: Stanford University deep Learning and Natural

Natural Language Processing (NLP) is a technique for studying computer-processing human languages, including:1. Syntactic analysis : For a given sentence, word segmentation, part-of-speech tagging, named entity recognition and linking, syntactic analysis, semantic role recognition and polysemy disambiguation.2. Informa

parameters, complex page interaction and other issues. Often using tools such as the above can easily solve these problems, the biggest drawback is due to the real browser based on the operation, it is less efficient, so often need and httpclient combination, to achieve efficient and practical purposes. Based on Phantomjs do Baidu meta-search capture also proves this point, the next step can be combined with it to complete the simulation of micro-Bo crawler to get the cookie part, after the use

, I prefer to use Python gensim to solve the problem.About Word2vec, this aspect regardless of the Chinese and English reference material is quite many, English aspect both can look at the official recommendation paper, may also see Gensim author Radim? Dr. Ek wrote some articles. In terms of Chinese, it is recommended to @licstar "deep learning in NLP (a) Word vector and language model", Youdao Technology salon "deep learning Combat Word2vec", @ Fei

Python natural language processing to fetch data from the networkWrite in frontThis section learns the technology of extracting data from the network python2.7 BeautifulSoup Library, in a nutshell, the crawler technology. Network programming is a complex technology, in the need of the basic place, the text gives the link address, are very good tutorials, you can

The basis of text sentiment analysis is natural language processing, affective dictionary, machine learning method and so on. Here are some of the resources I've summed up.Dictionary resources:Sentiwordnet"Knowledge Network" Chinese versionChinese Affective polarity dictionary NTUSDEmotion Vocabulary Ontology DownloadNatural