The inherent characteristics of human language, especially its ambiguity, ongoing change, and lack of clearly defined norms, present major obstacles for natural language processing (NLP).
Challenges of NLP
Ambiguity
Natural language is characterized by ambiguity, which poses a significant challenge to NLP systems:
A term might have more than one meaning, which is known as lexical ambiguity. The term “bank” can be used to describe a financial organisation or the bank of a river, for instance. In a similar vein, “Java” might refer to a programming language, an island, coffee, a dance, or a bird. Dual lexical elements can be found in even everyday terms like “can” and “fish”. One of the most important parts of semantic analysis in NLP is differentiating between various senses.
Because the meaning distinctions are frequently more subtle, polysemy the situation where a word has various but related senses presents even more challenges than homonymy, which is the situation where two words have the same form. To ascertain a polysemous word’s intended sense, NLP must conduct Word Sense Disambiguation (WSD).
Syntactic ambiguity, also known as structural ambiguity, occurs when a sentence’s grammatical structure leaves room for several interpretations. One of the most well-known examples is “Put the block in the box on the table,” where “on the table” may alter “box” or “put”. To demonstrate how the prepositional phrase’s attachment alters the meaning, the sentences “I ate pizza with friends” and “I ate pizza with olives” might be contrasted. Prepositional phrase sequences can result in an exponential rise in the number of potential analyses. One of the main challenges in parsing is resolving attachment ambiguities, such as prepositional phrase (PP) attachment.
- When the scope of operators such as quantifiers, modals, or negations can be applied to several sentence portions, this is known as scopal ambiguity.
- Uncertainty regarding the intended reference of pronouns or other referring expressions is known as referential ambiguity. This relates to anaphora resolution, which is the process of determining the referent of anaphoric statements.
- Because real language is inherently ambiguous, NLP systems need to be able to make decisions about disambiguation at multiple levels, such as word sense, word category, syntactic structure, and semantic scope.
Evolution
There are also constant difficulties because human language is an evolutionary process:
- Language is constantly evolving. People are always coming up with new words and ways to use words that already exist. Because of its productivity, an NLP system would soon become obsolete even if it ever had a comprehensive dictionary of the language. Thus, lexical acquisition the process of picking up new words and their characteristics is essential to statistical natural language processing.
- Since language change is typically slow, analyzing usage patterns and the strength of links between them frequently calls for statistical rather than categorical observations.
The absence of strict rules
Lastly, natural language differs greatly from artificial languages in that it lacks clearly defined rules:
Although natural languages have a grammar or structure, these are frequently intricate and have exceptions. Because “all grammars leak,” it’s not always possible to characterize well-formed utterances in a way that distinguishes them from poorly formed ones. In order to satisfy their communication needs, people are always bending and stretching the “rules.”
In contrast to artificial languages like computer languages, which are meant to remove lexical and structural ambiguities, natural languages have a loosely defined syntax. Tasks like tokenization, which divides text into understandable units, are made more difficult by this loose notion. For artificial languages, tokenization is well-established; but, in natural languages, where a single character might have multiple functions, it is more difficult. Text preparation becomes even more difficult depending on whether a language has an alphabetic, syllabic, or logographic writing system.
These basic characteristics of natural language ambiguity, evolution, and the absence of strict rules make it extremely difficult to analyze natural language and call for advanced methods in order for computers to comprehend and produce human language.