The need for linguistic analysis has been increasing steadily over the past couple of decades. As the interest and the number of research questions increased, so did the demand for amount of language data and its observations. Unfortunately, one cannot spend his time meticulously searching for words and constructs in question, as such endeavor might take a lifetime or two to complete. Everyone should agree that human time should be spent more preciously, so we employ computers and automated systems to collect and analyze linguistic data en masse.

Let us define a corpus to be a vast (or smaller) collection of authentic text, such as literature, transcripts of real-life conversations, newspapers, etc. Corpus linguistics will be defined as a discipline of linguistics, which relies on inquiries and results based on such a corpus. This allows researchers to process large amounts of data that could not have been parsed before, simply due to limitations in both the human time and resources. Katya is an example of such a corpus that focuses on features that will allow researchers to do what no other existing and commercial corpora provide.

There are many existing corpora, one of our main interest is the Russian National Corpus (RNC). It has Russian texts and Russian observations, with some parallel corpora included. We will focus on the Russian part of the corpus. RNC allows a user to search for a word or a grammatical/lexical structure, where it will return a paginated list of results. The search options of RNC are very powerful, as they allow for a vast array of options: non-contiguous matches, lemmas, parts of speech, etc. However, there are several severe limitations, which work against even the advanced users.

The first issue is that RNC's database of texts and corpus data is dictated and set by the organization that manages it. This means that the data that is returned and processed cannot be modified or tuned by the end-user. This introduces multiple issues, as the data could be biased depending on the type of data RNC stores and the format they return their results in.

Secondly, the results themselves are not complete. Even if a search term has over five hundred results, RNC does not allow the user to export all of the results, only allowing a CSV export of couple of hundred of search results. To circumvent this, one should go through each page and manually copy the results. This defeats the whole purpose of using a corpus, if the corpus does not return all the results.

Thirdly, if RNC lacks a specific feature or an analysis component, it is impossible to make RNC return such a result, as the infrastructure of RNC is proprietary, meaning each feature request should be submitted directly to the organization maintaining RNC. An open-source corpus alternative would be able to allow users to inspect how such a corpus operates and allow interesting developers to submit features, which would complete corpus features based on demand.

Katya is a liberated corpus that is focused on addressing the issues above. The big difference of Katya is that it allows users to provide their own web link, which will serve as a source of data that can be queried later. Katya accomplishes this by web scraping user link. More on that in the next section. Katya is capable of exporting complete search results. Another advantage is that Katya's code and infrastructure is public, such that anyone could learn how it works for educational purposes and other developers could contribute to the platform.

Katya allows users to have their own accounts on the system, where each user is able to add their own source to their account, such that their queries are isolated to their own selection. This allows a researcher to make results only include specific works and texts.

To secure users' accounts and data, state-of-the-art security policies are enacted within Katya. Every inbound and outbound connection within Katya's infrastructure is authenticated and encrypted. Many security measures are deployed, however, their exhaustive description is out of scope of this paper.

Anyone with an account can access Katya at katya.sandyuraz.com. On first opening it will prompt for a username and password. For developers, the backbone of Katya is located at katya-api.sandyuraz.com, which allows for a direct REST API access. The source code of the website and the source code of the analysis backend are hosted at github.com/thecsw/katya and github.com/thecsw/katya-dev, respectively.

A user is able to submit their own links as data sources and assign friendlier labels to them. Upon submission, a user can trigger a crawl. Katya will start crawling process by recursively selecting links from any found web pages, following the links, and exhaustively extracting all human-readable text (filtered from scripts and linguistically-irrelevant data). Scrapy is the web scraping module that allows us to do so, while Beautiful Soup combined with Spacy perform the tokenization and text classification, which is later permanently stored in Katya's central database.

A note that a source that has been scraped at least once will stay in the system permanently. If two users wish to add the same web link, Katya will intelligently link those two users to the same source, without the need of crawling it superfluously. When a user "deletes" a source, it is actually retained within the system, awaiting for any possible future linkages with other users. This makes Katya extremely efficient in memory storage and compute units required.

The figure below shows the interface that is presented to the user, where the user is able to add new sources, update/delete existing ones, enable/disable them in queries, and trigger crawls.

The main functionality is focused on performing fast and reliable linguistic queries across all sources that are enabled by the user. Katya currently allows to search the corpus based on four input types: literals, tags, lemmas, and shapes.

Literal search will return in text that matches the input absolutely, this means it matches on a character-by-character basis (ignoring casing). As an example, let me search for Russian "general" appearances in Dostoevsky's "Brothers Karamazov"

Tags search allows the user to search by parts-of-speech. Imagine we wanted to find an occurrence of seven continuous nouns, we would search for a tag string noun noun noun noun noun noun noun{.verbatim}, which would faithfully show us

Similar searches for tags can be done with keywords noun{.verbatim}, propn{.verbatim}, adj{.verbatim}, punct{.verbatim}, adv{.verbatim}, cconj{.verbatim}, adp{.verbatim}, and verb{.verbatim}.

Lemma search is similar to the literal one, with the main difference is that the search will go over non-conjugated standard forms of words. This is a powerful tool for finding all forms in which a word can be used in

This is an experimental search type that I do not believe any of corpora offer. This search type allows you to specify the length of words you would want to get in the results. For example, a shape of Xxxxx{.verbatim} will match Apple{.verbatim}.

There is a big interest in learning the distribution of word frequencies, such as what words appear the most or the least in any given work/source. Russian National Corpus provides this functionality, however, it only returns fewer than five hundred most frequent lemmas. Katya has the separate functionality, where a user can select one of their active sources and get a full CSV file with every lemma found across all texts associated with the source. Here is an example of the word frequency analysis of Brothers Karamazov

It is an interesting observation that excluding all the names, the top words stand for "brother", "money", "God", and "to kill". Funnily enough, this list succinctly summarizes the work as well.

Implemented in ./analysis/word_frequency.go

This feature allows us to analyze how a specific word can relate to its meaning within a given context. Given a word, simply find most occuring words that are some interval N words away. Implemented in ./analysis/word_relations.go

First version of Katya has been released and is available at katya.sandyuraz.com. Thanks to the design of Katya, it is easily extensible, such that we can add additional modules at no cost.

One of big improvements that will follow is advanced text cleanup. During scraping, our parsers might extract noisy contents, such as website's headers, footers, menus, etc. Advanced algorithms, such as Longest Common Subsequence

NOTE: This has been implemented in ./analysis/clean_repetetive.go

can help us to remove duplicated text from scraped texts. One other big feature missing is the ability to create accounts.