# Getting started

After installing and starting snaut and you can start working with the semantic spaces by opening your Web browser and going to http://localhost:5005.

The first thing you need to do is to load a semantic space which you want to use. If there is no space loaded at the moment, a window listing available semantic spaces (the ones you put in the data folder) should appear automatically. You should select the semantic space from the dropdown menu and click at the Load button.

When the space is loaded, its name will apprear in the left upper corner of the screen. At this point, you can start working with the semantic space.

The interface is organized into four menus - each allows to explore the semantic space in a different way. You can read more about these menus here. The simplest way in which you can start working with the semantic space is to pick a few words or phrases and type them in the form in the Neighbours tab (each word/phrase in a separate line; words in the phrase separated by space) and click on the Calculate button. You should see small tables showing words that are most similar to the words or phrases you typed in according to the semantic space.

To add more spaces to the menu you need to drop a semantic space file in the data folder. It will then appear in the space loading menu. If you are unsure about where you should drop the files you can click on the Change button and the target folder path will be displayed along with other information. You can customize location of the spaces by changing the semantic_spaces setting in the configuration file (see here for more details).

# Words and phrases

The interface allows to work with single words or with phrases composed of multiple words. In fact you can even think about a whole document as a very long phrese. If you enter a multi-word phrase to the input field it will be represented by the model as a sum of the vectors of all its words. If any of the words used in the phrase is not present in the loaded semantic space, snaut will not be able to compute the vector for this phrase and, as the result, it will ignore the whole phrase.

The phrases need to be entered as a list of words separated with spaces (interpuction must be removed).

### Neighbours

This menu allows to look up nearest neighbours of a set of words. For example, in order to check what are the words with a smallest distance to brain and dinosaur, type in brain and dinosaur into the input box on separate lines and press Calculate. You can choose the metric that is used to compute distance in the space.

You can also try to enter phrases composed of multiple words, for instance compare behavior, research and behavior research.

### Matrix

If you need to obtain measurements for a large number of words, you can use the matrix menu.

The words for which you need the scores should be entered in the input form on the left. Each word or phrase should be entered in a separate line. Next, in the dropdown menu you can choose what kind of comparison do you want to make. The available options are:

• distances between all pairs of words in the list
• distances between the words in the list and all other words in the loaded semantic space
• distances between all pairs of words between the left input field and the right input field

When you click on Calculate, snaut will compute the scores and, after this is finished, it will initialize download of a file with the results. The file is in a CSV format: it contains a table in a plain text with columns separated with commas.

You can read in the list of words to the text field from a file on your disc by clicking on the Load from a file button below the target input field. The Check availability button can be used check whether all words specified in the input field are present in the semantic space. Keep in mind that, if some of the words are not in the space, snaut will ignore them when computing the semantic distance measures.

### Pairwise

You can use this menu to investigate distances between individual pairs of words/documents. Each pair should be entered on a separate row in the input field and elements of the pair should be speparated with a colon (':') . After clicking on the Calculate button, snaut will do the calculation and a download of a CSV file with the result will be initialized.

For instance, in order to calculate the distance between pairs of words: home and window, car and wheel, fast car and slow car. You should enter:

home : window
car : wheel
car : cloud
fast car : slow car


Similarily to the Matrix interface you can load the list of pairs from a text file or check the availability of the words in the semantic space.

### Analogy

snaut implements an offset method described by Mikolov, Yih, & Zweig (2013). The analogy interface allows you to perform algebraic operations using vector semantic space and capture some regularities in the language.

The classical example involves the computation king - man + woman which results in a vector very close to queen.

The computation can be performed by entering the words vectors of which you want to have positive or negative contribution in the calculation. For instance, in order to calculate king - man + woman you need to enter king, woman in the field positive vectors and man in the field negative vectors.

# Configuration

snaut comes with a set of options that should work well for most usecases running on a local computer. Nevertheless, you may want to tweak some of the available options. This can be done by adjusting the settings in config.ini, which resides in the snaut main folder.

The configuration file is divided in two sections: server and semantic_space. The server section has the following options:

• host - this allows to specify on which IP address snaut is supposed to listen for requests. Two useful values are 127.0.0.1 (default; listen only for requests coming from the local machine) and 0.0.0.0 (listen for requests from any IP address; if set anyone will be able to communicate with the interface running on this computer)
• port - port on which snaut will listen

The semantic_space allows to configure settings directly related to how snaut handles the semantic spaces, using the following options:

• semspaces_dir - a directory in which available semantic spaces are located ( default ./data/)
• preload_space - if yes load a semantic space on startup (default: yes)
• preload_space_file - if preload_space is set to yes the space in this path will be preloaded (default: xxx)
• preload_space_format - the format of the space that will be preloaded
• prenormalize - when loading a space normalize all vectors to have length 1. This speeds up computation of cosine distances but does not allow to compute the other metrics (default: no)
• matrix_size_limit - a limit on the size of the computation that can be performed using snaut, in general this setting specifies the number of distance value which can be computed in each request, if set to -1 no limit will be enforced (default: -1)
• allow_space_change - if set to yesallow the user to change the loaded semantic space using the web interface (using the Change button in the semantic space menu), if no snaut only the preloaded space can be used (default: yes)

# Usage as a Web-server

To run snaut as a webserver on a Linux system you need to follow the following steps:

1) Install gunicorn. In Ubuntu:

  sudo apt-get install gunicorn


2) Clone the snaut repository and install dependencies:

  git clone https://github.com/pmandera/snaut.git


3) Install dependencies:

  cd snaut
sudo pip install -r requirements.txt


4) Install CoffeeScript and compile coffee files to javascript. In Ubuntu:

  sudo apt-get install coffeescript
coffee -c -o snaut/static/js/snaut snaut/coffee


You need to keep in mind that computing semantic distances often involves performing operations over large matrices and can be computationally expensive: you need to make sure that you want to allow external users to make such extensive use of your computational resources.

If you intend to expose the space in the server mode you will need to make some adjustments in the config.ini file.

You need to disallow changing the semantic space loaded by the server by allow_space_change to no. In oreder for snaut to load a semantic space when it is loaded set preload_space to yes and select the space by setting and preload_space_file and preload_space_format.

You probably also do not want your users to be able to compute huge matrices including billions of cells. In order to prevent the users from doing that set the matrix_size_limit to a reasonable value (in our experience values like 1,000,000 work pretty well).

You may also want to adjust the documentation that is shown to the user. You can do so by changing the doc_dir setting. You can use the adjusted documentation from ./doc/server/ or use your custom documentation directory.

6) Start snaut. For example to start snaut on port 9000, with 4 working instances run:

  gunicorn \
--workers 5 \
--bind 127.0.0.1:15008\
--pid snaut.pid \
--log-file snaut.log \
snaut.wsgi:app


7) It is recommended to run gunicorn behind a reverse proxy. For more information see gunicorn documentation

# File formats

## CSV

snaut works with CSV and Matrix Market file formats.

Values in the CSV file should be separated by spaces and contain words in the first column and vector values in the following columns. snaut treats lines starting with '#' as comments. Optionally, you can provide additional information about the space in the comments opening the file:

• If the first line contains a comment starting with 'TITLE: ', snaut treat it as a title of the space, that will be displayed in the status field of the interface.
• The following lines are treated as the description of the space and will be visible after clicking on More info.

If the first uncommented line contains two integer values, it will be treated as an information about the number of rows and columns in the file.

If the filename ends with *.gz, snaut will assume that the file is compressed using gzip.

The CSV format in snaut is compatible with a non-binary output of the Google word2vec tool.

The CSV files in this files can also be easily read in to R and used for example with the LSAfun package. To read in the space into a matrix, run:

space <- as.matrix(read.table('space.w2v.gz', sep=' ', row.names=1))

# for gzipped files:
space <- as.matrix(read.table(gzfile('space.w2v.gz'), sep=' ', row.names=1))


## Semantic space market

In the case of semantic spaces which contain a large number of 0.0 values, such as those created when counting word co-occurrences without a dimensionality reduction step, the CSV format is not practical. For such cases it is better to use the Matrix Market format which handles sparse matrices more efficiently. For more details see here

The space based on the Matrix Market format consist of the following files:

• data.mtx - matrix with word vectors as rows
• row-labels - a text with one word on a line, in an order corresponding to row vectors in the data.mtx file

Optionally, you can provide a README.md file which contains a title of the space in the first line and a description in the following lines. The title should be separated from the description with one blank line.

The files should be included in one folder or zipped in one file to which you need to point snaut.