Communicating and understanding each other is one of the most important human abilities.
As humans, in fact, we can easily assign the correct meaning to the ambiguous words in a text, while, at the same time, being able to abstract, summarise and enrich its content with new information that we learned somewhere else.
On the contrary, machines rely on formal languages which do not leave space to ambiguity hence being easy to parse and understand.
Therefore, to fill the gap between humans and machines and enabling the latter to better communicate with and comprehend its sentient counterpart, in the modern era of computer-science's much effort has been put into developing Natural Language Processing (NLP) approaches which aim at understanding and handling the ambiguity of the human language.
At the core of NLP lies the task of correctly interpreting the meaning of each word in a given text, hence disambiguating its content exactly as a human would do.
Researchers in the Word Sense Disambiguation (WSD) field address exactly this issue by leveraging either knowledge bases, i.e. graphs where nodes are concept and edges are semantic relations among them, or manually-annotated datasets for training machine learning algorithms. One common obstacle is the knowledge acquisition bottleneck problem, id est, retrieving or generating semantically-annotated data which are necessary to build both semantic graphs or training sets is a complex task.
This phenomenon is even more serious when considering languages other than English where resources to generate human-annotated data are scarce and ready-made datasets are completely absent. With the advent of deep learning this issue became even more serious as more complex models need larger datasets in order to learn meaningful patterns to solve the task.
Another critical issue in WSD, as well as in other machine-learning-related fields, is the domain adaptation problem, id est, performing the same task in different application domains.
This is particularly hard when dealing with word senses, as, in fact, they are governed by a Zipfian distribution; hence, by slightly changing the application domain, a sense might become very frequent even though it is very rare in the general domain.
For example the geometric sense of plane is very frequent in a corpus made of math books, while it is very rare in a general domain dataset.
In this thesis we address both these problems. Inter alia, we focus on relieving the burden of human annotations in Word Sense Disambiguation thus enabling the automatic construction of high-quality sense-annotated dataset not only for English, but especially for other languages where sense-annotated data are not available at all.
Furthermore, recognising in word-sense distribution one of the main pitfalls for WSD approaches, we also alleviate the dependency on most frequent sense information by automatically inducing the word-sense distribution in a given text of raw sentences.
In the following we propose a language-independent and automatic approach to generating semantic annotations given a collection of sentences, and then introduce two methods for the automatic inference of word-sense distributions.
Finally, we combine the two kind of approaches to build a semantically-annotated dataset that reflect the sense distribution which we automatically infer from the target text
