You have studied that entities have associations such as “a hotel has a price”, “a hotel has a rating”, “ginger is a plant” etc. Let’s study some common types of associations
Aboutness
When machine are analysing text, we not only want to know the type of semantic associations ‘is-a’ and ‘is-in’ but also want to know what is the word or sentence about. Take, for example, the example that we took at the start of the session:
`Croatia fought hard before succumbing to France's deadly attack;lost the final 2 goals to 4`
In the above text ,if we want machine to detect the game of football(if could be about other sports such as hockey as well , but let’s keep thing simple and assume it’s about football),then we need to formally define the notion of aboutness.
We can for example, detect that the game is football by defining semantic associations such as “Croatia” is a”country”, “France” is a “country”, “Finals”is-a”tournament stage”, “goals” is -a”scoring parameter”and so on .By defining such relationships, we can probably infer that the text is talking about football by going through the enormous schema.But you can imagine the kind of search this simple sentence would require. Even if we search through the schema, it doesn’t mean we’ll be able to decide that the game is football.
This leads us to define another semantic association-‘aboutness’. Let’s understand about ‘aboutness’ in the following lecture.
Thus, to understand the “aboutness” of atext basically means to identify the ‘topics‘ being talked about in the text. What makes this problem hard is that the same world (e.g. China) can be used in multiple topics such as politics, the Olympic games, trading etc.
You will study the idea of ‘aboutness’ and topics in detail in third session on topic modelling. For now ,let’s study some nomenlaturess used to classify types of associations between terms and concepts.
The five kinds of relationship between different word can be grouped as foll