gensim

i am going thorugh this paper http://cs.stanford.edu/~quocle/paragraph_vector.pdf and it states that " Theparagraph vector and word vectors are averaged or concatenated to predict the next word in a context. In the experiments, we use concatenation as the method to combine the vectors." How does concatenation or averaging work? example (if paragraph 1 contain word1 and word2): word1 vector =[0.1,0.2,0.3] word2 vector =[0.4,0.5,0.6] concat method does paragraph vector = [0.1+0.4,0.2+0.5,0.3+0.6] ? Average method does paragraph vector = [(0.1+0.4)/2,(0.2+0.5)/2,(0.3+0.6)/2] ? Also from this

I want to use output embedding of word2vec such as in this paper (Improving document ranking with dual word embeddings) . I know input vectors are in syn0, output vectors are in syn1 and syn1neg if negative sampling. But when I calculated most_similar with output vector, I got same result in some ranges because of removing syn1 or syn1neg. Here is what I got. IN[1]: model = Word2Vec.load('test_model.model') IN[2]: model.most_similar([model.syn1neg[0]]) OUT[2]: [('of', -0.04402521997690201), ('has', -0.16387106478214264), ('in', -0.16650712490081787), ('is', -0.18117375671863556), ('by', -0

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I am trying to do the following kaggle assignmnet . I am using gensim package to use word2vec. I am able to create the model and store it to disk. But when I am trying to load the file back I am getting the error below. -HP-dx2280-MT-GR541AV:~$ python prog_w2v.py Traceback (most recent call last): File "prog_w2v.py", line 7, in <module> models = gensim.models.Word2Vec.load_word2vec_format('300features_40minwords_10context.txt', binary=True) File "/usr/local/lib/python2.7/dist-packages/gensim/models/word2vec.py", line 579, in load_word2vec

自然语言处理(NLP)是指使用诸如英语之类的自然语言与智能系统进行通信的AI方法。 如果您希望智能系统(如机器人)按照您的指示执行操作，希望听取基于对话的临床专家系统的决策时，则需要处理自然语言。 NLP领域涉及使计算机用人类使用的自然语言执行有用的任务。 NLP系统的输入和输出可以是 - 言语(说话) 书面文字 NLP的组成部分 在本节中，我们将了解NLP的不同组件。 NLP有两个组件。 这些组件如下所述 - 1. 自然语言理解(NLU) 它涉及以下任务 - 将给定的自然语言输入映射为有用的表示。 分析语言的不同方面。 2. 自然语言生成(NLG) 它是从一些内部表现形式以自然语言的形式产生有意义的短语和句子的过程。 它涉及 - 文字规划 - 这包括从知识库中检索相关内容。 句子规划 - 这包括选择所需的单词，形成有意义的短语，设定句子的语气。 文本实现 - 这是将句子计划映射到句子结构。 NLU的难点 NLU的形式和结构非常丰富， 然而，它是不明确的。 可能会有不同程度的模糊性 - 词汇含糊不清 它处于一个非常原始的层面，如单词级别。 例如，将单词“board”视为名词或动词？ 语法级别歧义 一个句子可以用不同的方式解析。 例如，“他用红色帽子举起甲虫。” - 他用帽子举起甲虫，还是举起了一顶带有红色帽子的甲虫？ 参照歧义 参考使用代词的东西。 例如，里马去了高里。 她说，

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I have a gensim Word2Vec model computed in Python 2 like that: from gensim.models import Word2Vec from gensim.models.word2vec import LineSentence model = Word2Vec(LineSentence('enwiki.txt'), size=100, window=5, min_count=5, workers=15) model.save('w2v.model') However, I need to use it in Python 3. If I try to load it, import gensim from gensim.models import Word2Vec model = Word2Vec.load('w2v.model') it results in an error: UnicodeDecodeError: 'ascii' codec can't decode byte 0xf9 in position 0: ordinal not in range(128) I suppose the problem

I tried to follow this. But some how I wasted a lot of time ending up with nothing useful. I just want to train a GloVe model on my own corpus (~900Mb corpus.txt file). I downloaded the files provided in the link above and compiled it using cygwin (after editing the demo.sh file and changed it to VOCAB_FILE=corpus.txt . should I leave CORPUS=text8 unchanged?) the output was: cooccurrence.bin cooccurrence.shuf.bin text8 corpus.txt vectors.txt How can I used those files to load it as a GloVe model on python? You can do it using GloVe library: Install it: pip install glove_python Then: from glove