Text understanding for computers

A long-standing challenge for computers communicating with humans is to pass the Turing test, i.e., to communicate in such a way that it is impossible for humans to determine whether they are talking to a computer or another human being. The field of natural language understanding — which studies automatic means of capturing the semantics of textual content — plays a central part in this long-term goal of artificial intelligence research. Natural language understanding can itself be understood at different levels. In this thesis, we make contributions to automatic understanding of text at the level of words, short texts, and full documents.
Understanding texts at the word level, means understanding how words relate to each other semantically. For example, do two words or phrases mean approximately the same thing? Does a particular word still mean the same thing it used to, say, 50 years ago? Or, as is the question central to the first part of this thesis, can we automatically detect which words people used in different periods in time to refer to a particular concept?
The question we ask ourselves in the second part of this thesis is: can we automatically determine, from the word-level up, if two sentences have a similar meaning?
Finally, in the third part of this thesis, document-level text understanding is the focus of our interest. In particular, we study multiple approaches to the reading comprehension task, where a computer reads a document and answers questions about it.