25 years of Hašek

Hašek is a Croatian on-line spellchecker that continuously operates since March 21, 1994, nowadays at the address https://ispravi.me/. In 25 years of functioning Hašek processed nearly 30 million texts, which build a corpus of more than 7 billion tokens. By comparison, all books ever published in Croatian form a corpus with less than 20 billion tokens. As a WWW-embedded tool, Hašek took advantage of many web-based services including learning. Thanks to Hašek’s learning capability, its dictionary increased from initial 100 thousand to more than 2 million word-types. Another aspect of learning was the creating and regular updating of the Croatian n-gram system. Unlike Google, whose n-gram systems are based on the WaC (Web as Corpus) approach and cut-off criteria, Croatian n-grams were extracted from processed texts by a lexical criterion: each n-gram constituent must be proven by the spellchecker as valid in Croatian spelling. The difference in approaches made Croatian n-gram system comparable in size to the largest Google n-gram systems. Unfortunately, the advantages of on-line spellchecking for rapid breakthroughs into much more sophisticated language technology areas were not recognized by Croatian decision makers, with some consequences mentioned in the paper

Hierarchical Character-Word Models for Language Identification

Social media messages' brevity and unconventional spelling pose a challenge to language identification. We introduce a hierarchical model that learns character and contextualized word-level representations for language identification. Our method performs well against strong base- lines, and can also reveal code-switching

Multilayer Network of Language: a Unified Framework for Structural Analysis of Linguistic Subsystems

Recently, the focus of complex networks research has shifted from the analysis of isolated properties of a system toward a more realistic modeling of multiple phenomena - multilayer networks. Motivated by the prosperity of multilayer approach in social, transport or trade systems, we propose the introduction of multilayer networks for language. The multilayer network of language is a unified framework for modeling linguistic subsystems and their structural properties enabling the exploration of their mutual interactions. Various aspects of natural language systems can be represented as complex networks, whose vertices depict linguistic units, while links model their relations. The multilayer network of language is defined by three aspects: the network construction principle, the linguistic subsystem and the language of interest. More precisely, we construct a word-level (syntax, co-occurrence and its shuffled counterpart) and a subword level (syllables and graphemes) network layers, from five variations of original text (in the modeled language). The obtained results suggest that there are substantial differences between the networks structures of different language subsystems, which are hidden during the exploration of an isolated layer. The word-level layers share structural properties regardless of the language (e.g. Croatian or English), while the syllabic subword level expresses more language dependent structural properties. The preserved weighted overlap quantifies the similarity of word-level layers in weighted and directed networks. Moreover, the analysis of motifs reveals a close topological structure of the syntactic and syllabic layers for both languages. The findings corroborate that the multilayer network framework is a powerful, consistent and systematic approach to model several linguistic subsystems simultaneously and hence to provide a more unified view on language