Linguist | Generalized syntax highlighter addin for Visual Studio
kandi X-RAY | Linguist Summary
kandi X-RAY | Linguist Summary
Linguist is an extension for Visual Studio 2010 allowing customizable syntax highlighting based on file names. Support for new languages may be easily added using regular expressions to identify the various language elements and language elements may be customized using different fonts, point sizes, colors, and font styles (e.g. bold and italic). The built-in languages include C#, C++, C, Python, Makefiles, patch files, etc. To install the extension close studio and double-click the Linguist.vsix file. To uninstall the extension select the Extension Manager… item from Studio’s Tool menu, select Linguist, and press the Uninstall button. To upgrade the extension uninstall the old version and install the new version. To check for newer versions of the extension visit github. The software is distributed under the terms of the MIT.X11 licensing agreement (see Licence.txt).
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Trending Discussions on Linguist
QUESTION
Out of nowhere, the build is crashing with a strange error related to the navigation component even though it used to work before, the error is in the generated class, in my case NativeLanguageSelectionFragmentDirections
Here is the error
...ANSWER
Answered 2021-May-25 at 04:14I had this problem too. Until they release the fix. Please try this:
QUESTION
here is my code
...ANSWER
Answered 2021-May-27 at 10:301. Change your html structure
QUESTION
I have a very linguistic issue to solve. I'm creating a java spring boot application which has to support, among the other languages, french. So in the .properties file I've created for french I have a line similar to this:
getstarted=commençons
However, when shown in the HTML5 file the output is: commen�ons, with the question mark instead of the cedilla. Now, this application also supports japanese, so when I paste japanese-language text in the japanese .properties file it gets automatically escaped with unicode. I was wondering if someone could tell me how to properly escape the cedilla... all help is appreciated. Bye.
...ANSWER
Answered 2021-May-12 at 17:43Nothing guys, the text in the properties file just had to be edited to:
getstarted=commen\u1E09\ons
QUESTION
How do I provide an OpenNLP model for tokenization in vespa? This mentions that "The default linguistics module is OpenNlp". Is this what you are referring to? If yes, can I simply set the set_language index expression by referring to the doc? I did not find any relevant information on how to implement this feature in https://docs.vespa.ai/en/linguistics.html, could you please help me out with this?
Required for CJK support.
...ANSWER
Answered 2021-May-20 at 16:25Yes, the default tokenizer is OpenNLP and it works with no configuration needed. It will guess the language if you don't set it, but if you know the document language it is better to use set_language (and language=...) in queries, since language detection is unreliable on short text.
However, OpenNLP tokenization (not detecting) only supports Danish, Dutch, Finnish, French, German, Hungarian, Irish, Italian, Norwegian, Portugese, Romanian, Russian, Spanish, Swedish, Turkish and English (where we use kstem instead). So, no CJK.
To support CJK you need to plug in your own tokenizer as described in the linguistics doc, or else use ngram instead of tokenization, see https://docs.vespa.ai/documentation/reference/schema-reference.html#gram
n-gram is often a good choice with Vespa because it doesn't suffer from the recall problems of CJK tokenization, and by using a ranking model which incorporates proximity (such as e.g nativeRank) you'l still get good relevancy.
QUESTION
I have trained a doc2vec (PV-DM) model in gensim
on documents which fall into a few classes. I am working in a non-linguistic setting where both the number of documents and the number of unique words are small (~100 documents, ~100 words) for practical reasons. Each document has perhaps 10k tokens. My goal is to show that the doc2vec embeddings are more predictive of document class than simpler statistics and to explain which words (or perhaps word sequences, etc.) in each document are indicative of class.
I have good performance of a (cross-validated) classifier trained on the embeddings compared to one compared on the other statistic, but I am still unsure of how to connect the results of the classifier to any features of a given document. Is there a standard way to do this? My first inclination was to simply pass the co-learned word embeddings through the document classifier in order to see which words inhabited which classifier-partitioned regions of the embedding space. The document classes output on word embeddings are very consistent across cross validation splits, which is encouraging, although I don't know how to turn these effective labels into a statement to the effect of "Document X got label Y because of such and such properties of words A, B and C in the document".
Another idea is to look at similarities between word vectors and document vectors. The ordering of similar word vectors is pretty stable across random seeds and hyperparameters, but the output of this sort of labeling does not correspond at all to the output from the previous method.
Thanks for help in advance.
Edit: Here are some clarifying points. The tokens in the "documents" are ordered, and they are measured from a discrete-valued process whose states, I suspect, get their "meaning" from context in the sequence, much like words. There are only a handful of classes, usually between 3 and 5. The documents are given unique tags and the classes are not used for learning the embedding. The embeddings have rather dimension, always < 100, which are learned over many epochs, since I am only worried about overfitting when the classifier is learned, not the embeddings. For now, I'm using a multinomial logistic regressor for classification, but I'm not married to it. On that note, I've also tried using the normalized regressor coefficients as vector in the embedding space to which I can compare words, documents, etc.
...ANSWER
Answered 2021-May-18 at 16:20That's a very small dataset (100 docs) and vocabulary (100 words) compared to much published work of Doc2Vec
, which has usually used tens-of-thousands or millions of distinct documents.
That each doc is thousands of words and you're using PV-DM mode that mixes both doc-to-word and word-to-word contexts for training helps a bit. I'd still expect you might need to use a smaller-than-defualt dimensionaity (vector_size<<100), & more training epochs - but if it does seem to be working for you, great.
You don't mention how many classes you have, nor what classifier algorithm you're using, nor whether known classes are being mixed into the (often unsupervised) Doc2Vec
training mode.
If you're only using known classes as the doc-tags, and your "a few" classes is, say, only 3, then to some extent you only have 3 unique "documents", which you're training on in fragments. Using only "a few" unique doctags might be prematurely hiding variety on the data that could be useful to a downstream classifier.
On the other hand, if you're giving each doc a unique ID - the original 'Paragraph Vectors' paper approach, and then you're feeding those to a downstream classifier, that can be OK alone, but may also benefit from adding the known-classes as extra tags, in addition to the per-doc IDs. (And perhaps if you have many classes, those may be OK as the only doc-tags. It can be worth comparing each approach.)
I haven't seen specific work on making Doc2Vec
models explainable, other than the observation that when you are using a mode which co-trains both doc- and word- vectors, the doc-vectors & word-vectors have the same sort of useful similarities/neighborhoods/orientations as word-vectors alone tend to have.
You could simply try creating synthetic documents, or tampering with real documents' words via targeted removal/addition of candidate words, or blended mixes of documents with strong/correct classifier predictions, to see how much that changes either (a) their doc-vector, & the nearest other doc-vectors or class-vectors; or (b) the predictions/relative-confidences of any downstream classifier.
(A wishlist feature for Doc2Vec
for a while has been to synthesize a pseudo-document from a doc-vector. See this issue for details, including a link to one partial implementation. While the mere ranked list of such words would be nonsense in natural language, it might give doc-vectors a certain "vividness".)
Whn you're not using real natural language, some useful things to keep in mind:
- if your 'texts' are really unordered bags-of-tokens, then
window
may not really be an interesting parameter. Setting it to a very-large number can make sense (to essentially put all words in each others' windows), but may not be practical/appropriate given your large docs. Or, trying PV-DBOW instead - potentially even mixing known-classes & word-tokens in eithertags
orwords
. - the default
ns_exponent=0.75
is inherited from word2vec & natural-language corpora, & at least one research paper (linked from the class documentation) suggests that for other applications, especially recommender systems, very different values may help.
QUESTION
this is my first MySQL Python program. I don't know why the script crashes, but I know it crashes when it is added to the database. The script function is designed to retrieve information from websites and add this information to the database. This feature will be used over and over again. Could someone help me? Sorry for linguistic errors "Google translate"
My code:
...ANSWER
Answered 2021-May-17 at 10:30you are trying to add to MySQL bs4 tag:
QUESTION
Please note this is not duplicate of other similar questions asked, as they don't involved usage of tailwind css which is unique case.
I did create .gitattributes
file and added below entry
ANSWER
Answered 2021-May-05 at 11:45but it didn't fix the issue
Yes. What you need to do is, asking GitHub Linguist for NOT seeing your CSS files (that is what you did now), or regarding your CSS files as some other types for example JavaScript.
Is tailwind css creating some hidden files which is shooting up CSS usage ?
Yes. While opening your repository, I found a 3.4MB "large" file src/styles/app.css
which is probably the trouble maker.
Your comment said, "add to .gitignore and check". Please think twice:
- Is your code run without this file? (or another question, is this file generated during code deploying which means your code can still deploys without this file?)
- It is not enough for you to just add that file in
.gitignore
: Without other operation such as removing it from your Git repository (either via deleting it thengit add
, or usegit filter-branch
orbfg
to remove the file from entire Git repository), it will be "useless" since that this file has already tracked by Git.
QUESTION
I have code like this In one file I have
...ANSWER
Answered 2021-Apr-13 at 14:00I believe the cleanest way to handle this is to specifically narrow the type of category
to keyof NormativeCaseType
. What that does is restrict the values to strings that are the property names.
QUESTION
There is a program developed for linguistic research (http://people.csail.mit.edu/mcollins/code.html). When I try to run the parser using Git bash terminal on Windows, I get the error:
...ANSWER
Answered 2021-Apr-06 at 15:45As indicated by file
, your program is a Linux application so obviously you can't run it on Windows. See
- Why does a linux compiled program not work on Windows
- Why won't Windows EXE files work on Linux?
- Why do you need to recompile C/C++ for each OS?
Mingw is not an environment for running Linux executables, it's just a compiler that compiles POSIX code into native Windows binaries. Neither is Cygwin, which is a reimplementation of POSIX system calls in Windows, and Cygwin binaries are also native Windows binaries with a dependency on Cygwin DLLs. Read this if you want to know their differences. Bash is a shell and isn't a platform to execute files either. Only the runtime platform (OS or something like JVM or .NET CLR VM) can run programs and a shell is just a tool to interact with the OS
So you must run Linux programs in a Linux environment like a Linux machine or WSL1/2. Since the program is 32-bit, you can only run it in Linux or WSL2
Since you have the source code you can also compile the code with mingw or cygwin and run on Windows
QUESTION
I was wondering whether spacy
has some APIs to do phrase* extraction as one would do when using word2phrase
or the Phrases
class from gensim
. Thank you.
PS. Phrases are also called collocations in Linguistics.
...ANSWER
Answered 2021-Apr-01 at 06:15I am wondering if you have you seen PyTextRank or spacycaKE extension to SpaCy?
Both can help with phrase extraction which is not possible directly with SpaCy.
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