I'm asking for a way to convert multiple instances of LaTeX format, which is a typesetting language used for mathematical and technical documents, in a Word 2019 document to equations all at once.I mean the process of taking the text written in LaTeX code and converting it into a visual representation of an equation. In other words, the LaTeX format, which is a typesetting language primarily used for mathematical and technical documents, is being transformed into an image or visual representation of the equation that the code represents. It's like when you write an equation in word document, it is in text format but when you convert it to an equation, it will be a visual representation of the same equation.
For exmaple: The derivative of the natural logarithm function, $ln(x)$, with respect to $x$ is equal to the reciprocal of $x$, or mathematically written as: $$\frac{d}{dx} \ln(x) = \frac{1}{x}$$. In this text I have to convert each LaTeX code one by one to an equation such as $ln(x)$ then $x$ then $$\frac{d}{dx} \ln(x) = \frac{1}{x}$$.
In other words, I wants to find a way to quickly and efficiently convert all the LaTeX format in their Word document to equations, rather than having to do it one by one.
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I am using a GloVe based pre-trained embedded vectors for words in my I/P sentences to a NMT-like model. The model then generates a series of word embeddings as its output for each sentence.
How can I convert these output word embeddings to respective words? One way I tried is using cosine similarity between each output embedding vector and all the i/p embedding vectors. Is there a better way than this?
Also, is there a better way to approach this than using embedding vectors?
First of all the question is lacking a lot of details like the library used for word embedding, the nature of the model, and the training data, etc ...
But I will try to give you an idea what you can do in those situations, assuming you are using some word embedding library like Gensim.
How to get the word from the vector:
We are dealing with predicted word vectors here, so our word vector may not be the exact vector of the original word, we have to use similarity, in Gensim you can use similar_by_vector, something like
target_word_candidates = similar_by_vector(target_word_vector,top=3)
That's would solve the reverse lookup problem, as highlighted here, given all the word vectors how to get the most similar word, but we need to find the best single word according to the context.
You can use some sort of post-processing on the output target word vectors, this would be beneficial for trying to solve some problems like:
1.How to guide the translation of out-of-vocabulary
terms?
2.How to enforce the presence of a
given translation recommendation in the decoder’s
output?
3.How to place these word(s) in the right
position?
One of the ideas is to use an external resource for the target language, i.e. language model, to predict which combination of words are gonna be used. Some other techniques incorporate the external knowledge inside the translation network itself
I have the following huge matrix:(taken from a latex code)
My doubts are two:
1) whether this matrix is written rightly for a MATLAB platform?
2) how can I display the whole code in a pdf generated by writing it in LaTeX so that the whole thing does not exceed the A4 size page limit?
I have used latex interpreter in the plot command in MATLAB (R2014b) to produce especial characters and hat in the figure legends.
The quality of the texts in the legend became rather bad and when I copy the figure to use it in my word document; it is hard to identify the subscript of the variables so it is not suitable to be used in the article.
The command that I have used is this:
legend({'${\rho}_1(\hat{\theta})$','$\rho_1(\theta)$',},1,'Interpreter','latex');
I appreciate any help on this problem.
Thanks!
I've been reading about neural networks and how CBOW and Skip-Gram works but I can't figure out one thing: How do I generate the word vectors itself?
It always seems to me that I use those methods to calculate the weight matrix, and I use the word vector to do adjust it, and I'm struggling to understand how I got the word vectors in the first place.
When I found Rumelhart paper I thought I would find the answer there, but all I got was the same thing: calculate the error comparing the output expected with the one I found and adjust the model. But who is my expected output? How did I get it?
For example, Omer Levy and Yoav Goldberg explained in a perfect clear way (in Linguistic Regularities in Sparse and Explicit Word Representations) how the Explicit Vector Space Representation works, but I couldn't find an explanation on how Distributed representations for words works.
I need to generate a report which should contain a set of different mathematical equations. I need a way to inserted in my report, there's no condition on the way one can represent the equations so any solution will do, either if the equations come in latex format or MathML or whatever. Is there a way to do it or do I have to insert the equations as Images?
I am still fairly new to Jasper Reports, but I believe you will have to insert images of the equations into your report (just like how Wikipedia uses images for their equations).
How to Show an Image on Jasper Report