Final answer:
The query focuses on two types of linguistic ambiguities: lexical, where a word has multiple meanings, and structural, where sentence structure creates ambiguity. Natural language processing aims to reduce these ambiguities using semantics, syntax, and deeper philosophical context. The Sapir-Whorf hypothesis further emphasizes the relationship between language, thought, and interpretation.
Step-by-step explanation:
The challenge you've described in natural language processing relates to lexical and structural ambiguity. Lexical ambiguity arises when a single word has multiple meanings, which can depend on the context in which it is used. An example is the word 'bank', which could mean the edge of a river or a financial institution.
Structural ambiguity, on the other hand, refers to the ambiguity that emerges from the structure of a sentence and how its elements are related. For instance, 'I saw the man with a telescope' can mean either that I used a telescope to see the man or that the man had a telescope with him. This type of ambiguity makes the process of understanding context crucial in natural language processing.
To successfully determine the meaning of words in a specific context, natural language processing systems use techniques such as definite descriptions and predicates to reduce ambiguity. They also rely on semantics and syntax as part of the language's grammar.
Understanding these aspects of language through the lens of philosophical discussions such as structuralism and post-structuralism, as explained by Ferdinand de Saussure and Derrida, adds depth to our approach in handling ambiguities.
Moreover, the Sapir-Whorf hypothesis adds to our understanding of language influence on thought, suggesting that the absence of certain words in a language affects how experiences are interpreted. In essence, natural language processing not only captures the phonetic and grammatical contours of language but must also delve into the conceptual and philosophical underpinnings to reliably deduce meaning.