  Words in some natural languages can have a composite structure. Elements of
this structure include the root (that could also be composite), prefixes and
suffixes with which various nuances and relations to other words can be
expressed. Thus, in order to build a proper word representation one must take
into account its internal structure. From a corpus of texts we extract a set of
frequent subwords and from the latter set we select patterns, i.e. subwords
which encapsulate information on character $n$-gram regularities. The selection
is made using the pattern-based Conditional Random Field model with $l_1$
regularization. Further, for every word we construct a new sequence over an
alphabet of patterns. The new alphabet's symbols confine a local statistical
context stronger than the characters, therefore they allow better
representations in ${\mathbb{R}}^n$ and are better building blocks for word
representation. In the task of subword-aware language modeling, pattern-based
models outperform character-based analogues by 2-20 perplexity points. Also, a
recurrent neural network in which a word is represented as a sum of embeddings
of its patterns is on par with a competitive and significantly more
sophisticated character-based convolutional architecture.
