We propose the task of unsupervised morphological paradigm completion.Given only raw text and a lemma list, the task consists of generating the morphological paradigms, i.e., all inflected forms, of the lemmas.From a natural language processing (NLP) perspective, this is a challenging unsupervised task, and high-performing systems have the potential to improve tools for low-resource languages or to assist linguistic annotators.From a cognitive science perspective, this can shed light on how children acquire morphological knowledge.We further introduce a system for the task, which generates morphological paradigms via the following steps: (i) EDIT TREE retrieval, (ii) additional lemma retrieval, (iii) paradigm size discovery, and (iv) inflection generation.We perform an evaluation on 14 typologically diverse languages.Our system outperforms trivial baselines with ease and, for some languages, even obtains a higher accuracy than minimally supervised systems. 1 Sé vigilante y confirma las otras cosas que están para morir , porque no he hallado tus obras bien acabadas delante de Dios .Acuérdate , pues , de lo que has recibido y oído ; guárdalo y arrepiéntete , pues si no velas vendré sobre ti como ladrón y no sabrás a qué hora vendré sobre ti .El vencedor será vestido de vestiduras blancas , y no borraré su nombre del libro de la vida , y confesaré su nombre delante de mi Padre y delante de sus ángeles .El que tiene oído , oiga lo que el Espíritu dice a las iglesias .

Sememes are minimum semantic units of concepts in human languages, such that each word sense is composed of one or multiple sememes.Words are usually manually annotated with their sememes by linguists, and form linguistic commonsense knowledge bases widely used in various NLP tasks.Recently, the lexical sememe prediction task has been introduced.It consists of automatically recommending sememes for words, which is expected to improve annotation efficiency and consistency.However, existing methods of lexical sememe prediction typically rely on the external context of words to represent the meaning, which usually fails to deal with low-frequency and out-ofvocabulary words.To address this issue for Chinese, we propose a novel framework to take advantage of both internal character information and external context information of words.We experiment on HowNet, a Chinese sememe knowledge base, and demonstrate that our framework outperforms state-of-the-art baselines by a large margin, and maintains a robust performance even for low-frequency words.i