Distributionalität

Distributionalität, also known as distributional semantics, is a method in computational linguistics and natural language processing that analyzes the meaning of words based on their contexts. The core idea is that words with similar meanings tend to appear in similar contexts. This approach was popularized by the work of John Rupert Firth, who famously stated, "You shall know a word by the company it keeps."

In distributional semantics, the meaning of a word is represented as a vector in a high-dimensional space, where each dimension corresponds to a context feature, such as other words in the same sentence or document. The similarity between words can then be measured using distance metrics, such as cosine similarity, in this vector space.

Distributional models can be trained using large corpora of text, and they have been shown to capture a wide range of semantic relationships, including synonymy, antonymy, and hypernymy. These models can also be used for various downstream tasks, such as word sense disambiguation, machine translation, and information retrieval.

One of the main advantages of distributional semantics is its ability to handle rare and out-of-vocabulary words, as their meanings can be inferred from their contexts. However, it also has some limitations, such as the difficulty of capturing fine-grained semantic distinctions and the need for large amounts of training data.

In recent years, distributional semantics has been superseded by more advanced techniques, such as contextualized word embeddings and transformer-based models, which take into account the entire context in which a word appears. Nevertheless, distributional semantics remains an important foundation in the field of natural language processing.