Human languages use a wide range of grammatical categories to constrain which
words or phrases can fill certain slots in grammatical patterns and to express
additional meanings, such as tense or aspect, through morpho-syntactic means.
These grammatical categories, which are most often language-specific and
changing over time, are difficult to define and learn. This paper raises the
question how these categories can be acquired and where they have come from. We
explore a usage-based approach. This means that categories and grammatical
constructions are selected and aligned by their success in language
interactions. We report on a multi-agent experiment in which agents are endowed
with mechanisms for understanding and producing utterances as well as
mechanisms for expanding their inventories using a meta-level learning process
based on pro- and anti-unification. We show that a categorial type network
which has scores based on the success in a language interaction leads to the
spontaneous formation of grammatical categories in tandem with the formation of
grammatical patterns.

通过一系列代理实验证明，基于习惯和互动的选取和对齐方式，能够推动语言交互更自然的语法范畴与语法模式的形成。

基于语用的语法类别学习

Usage-based learning of grammatical categories

We introduced the contemporary Amharic corpus, which is automatically tagged
for morpho-syntactic information. Texts are collected from 25,199 documents
from different domains and about 24 million orthographic words are tokenized.
Since it is partly a web corpus, we made some automatic spelling error
correction. We have also modified the existing morphological analyzer,
HornMorpho, to use it for the automatic tagging.

我们介绍了当代阿姆哈拉语语料库，该语料库自动标记词形句法信息，500 种不同领域的 25199 个文档收集了约 2400 万个拼写单词。由于它是部分网络语料库，因此我们进行了一些自动拼写错误更正。我们还修改了现有的形态分析器 HornMorpho，用于标记自动标记。