The Art of Understanding Linguistic Ambiguity: NLP’s Greatest Challenge

Have you ever stopped to think about how complex language is? As humans, we use language every day to communicate with one another, but even we struggle with the ambiguity and multiple meanings that can arise from the words we use. Now, imagine trying to teach a computer to understand language. This is the goal of Natural Language Processing (NLP), a field that focuses on developing algorithms and technologies that can help machines understand human language. This blog post delves into one of the most significant challenges in NLP, Linguistic Ambiguity. We’ll explore how NLP systems deal with three of the most common forms of linguistic ambiguity and discuss what improvements can still be made in this fascinating field.

Linguistic Ambiguity

When it comes to NLP, one of the biggest challenges is dealing with linguistic ambiguity. To help understand and tackle this problem, NLP researchers have identified three primary levels of ambiguity that modern NLP systems need to address:

• Lexical
• Syntactic
• Semantic

However, these levels aren’t always clear-cut and can overlap with one another. For example, an ambiguous sound or word choice (lexical) can also impact the sentence structure (syntactic) and ultimately affect the meaning of the sentence (semantic). While NLP systems have made significant strides in handling these forms of ambiguity, there is still much to be done to overcome the challenges of the more complex levels of ambiguity like pragmatics and discourse.

1. Lexical Ambiguity

If you’ve ever read a sentence and wondered what the author really meant, this is a common problem caused by lexical ambiguity. This occurs when words or phrases have multiple meanings. There are two main types of lexical ambiguity:

• Homonymy: Involves two words that look or sound the same but have different meanings. For example, bank’ means either a financial institution or the land beside a river. You might also come across homophones (words with the same sound but different spellings) and homographs (words with the same spelling but different meanings).
• Polysemy: Involves one word that has multiple meanings. For example, ‘to serve’ means different things depending on the context, ‘to serve time in prison’ or ‘to serve a cup of tea’.

2. Syntactic Ambiguity

Syntactic ambiguity, also known as amphibology, arises from confusing sentence structures rather than individual words. This can occur when a group of words in a sentence has multiple interpretations.

For example, “Mary saw John with a telescope”. Here the prepositional phrase “with a telescope” could either mean that Mary saw John using a telescope, or that John was carrying a telescope when Mary saw him.

3. Semantic Ambiguity

Semantic ambiguity occurs when a sentence has more than one interpretation due to a lack of context. This happens frequently with pronouns.

For example, in the sentence “My mother and my sister were sad after she shouted at her”, we can’t tell who “she” and “her” are referring to without more information.

Sometimes, the lack of context makes things even more confusing.

For example, “She loves me”, which could refer to anyone without any additional information.

This ambiguity happens the most with irony, sarcasm, and metaphors. These linguistic devices are hard to understand for machines, as they require knowledge and context beyond the surface-level meaning of the word and sentence.

Ambiguity in NLP

Natural language processing is a field that aims to make computers understand and generate human language. However, one of the biggest challenges that Language Models face is ambiguity. This is usually down to a lack of context, which leads to errors in applications such as question-answering systems, information retrieval, or language translation. While techniques like Word-sense disambiguation (WSD) systems and knowledge bases have been used to address ambiguity, the lack of context is still a major issue.

1. Lexical Ambiguity in NLP

Lexical ambiguity can cause headaches for natural language processing systems. In the past, it was tough for computers to differentiate between homonym words like “bear” without context. However, with the use of modern contextual embeddings, which consider the surrounding words, these same words with different meanings will have different embeddings. This is a significant advancement for NLP tasks such as Entity Linking, where it’s essential to disambiguate among personal names or places.

For example, in the sentence “Paris Hilton visited Paris”. By using contextual embeddings, we can help computers understand the multiple meanings of a word, just like we do as humans.

2. Syntactic Ambiguity in NLP

Syntactic ambiguity is another challenge that NLP faces. One way to understand sentence structures is with Dependency Parsing. This process examines the relationship between phrases, based on the assumption that there is a direct relationship, or dependency, between linguistic units within sentences. The relationship between two words is assigned a dependency tag to every input token.

A popular way to perform dependency tagging is with the use of SpaCy, a widely used Python library for NLP. However, the accuracy of the tagging depends on the model’s training data.

For example, the sentence “Mary saw John with a telescope” is ambiguous in terms of which noun the word “telescope” is referring to. Here, SpaCy’s English language model labels “telescope” as a complement of “Mary,” which is incorrect.

This demonstrates the complexity of syntactic ambiguity in NLP, and how it can affect model performance. Models rely heavily on the quality of their training data, which can lead to confirmation bias due to statistical relevance.

3. Semantic Ambiguity in NLP

In NLP, deducing semantic information has become more accurate with the use of:

• Contextual embeddings: Aim to capture word semantics in different contexts
• Attention mechanism: Focuses on different parts of a sentence based on their relevance

These advances have also improved the task of co-reference resolution. This involves identifying all expressions in a text that refer to the same real-world entity. Specifically, this deals with understanding which nouns in a sentence a pronoun refers to.

For example, in the sentence “John said he will come to the party”, co-reference resolution would identify that “he” refers to John, and therefore, the sentence means that John will come to the party.

In some cases, the pronoun may have similar similarities with multiple nouns, but the attention mechanism can help identify which noun the pronoun is actually referring to.

Chat GPT

Large Language Models (LLMs) have become the dominant force in NLP, and the current trend is to align their performance with user intentions. In 2022, OpenAI released two models of GPT-3. Firstly, InstructGPT, in which human feedback was essential for improving performance. Later, ChatGPT4 was released, which was trained to interact with users and receive feedback.

These models appear to prioritize giving an answer over none at all, which can be helpful in cases of poorly formulated or incomprehensible questions. The models have been used for writing, automatizing, code generation, common knowledge question, and reasoning.

GPT-3 is able to generate content such as writing essays, poetry, jokes, and other types of writing by partially copying what it has seen during its training. Despite this, the text it generates often shows high levels of creativity and quality that is comparable to that of humans. As a result, GPT-3 can be used effectively for a variety of writing tasks, such as creating web content for SEO, writing sales pitches, coming up with taglines for new products, and drafting congratulatory letters for colleagues.

Chat GPT has been a significant achievement in solving the issue of the reliable generation of human-like language for interaction and dialog. Although there is still a lot to be done, especially with regard to ethical interaction, Chat GPT is revolutionary for NLP technologies. This advancement is a major step in demonstrating the potential of models that behave in a ‘human-aligned’ manner.

Experimenting with Linguistic Ambiguity

To delve deeper into the potential of Chat GPT, employees from dezzai’s AI department conducted experiments to evaluate ChatGPT’s capabilities in dealing with ambiguity at different linguistic levels. They created new tests to assess ambiguity at the lexical, syntactic, and semantic levels, and input ambiguous sentences via prompting to analyze ChatGPT outputs. The results showed that ChatGPT performed well in detecting ambiguity at the semantic level but struggled with syntactic ambiguity. The model had some bright spots, such as co-reference resolution, but also revealed weaknesses such as gender bias over grammar in some non-ambiguous situations. The lack of systematicity was also identified as an issue. Overall, ChatGPT performed well but still has room for improvement when it comes to dealing with linguistic ambiguity.

Find out more about their analysis of the linguistic weaknesses and strengths of the ChatGPT model related to linguistic ambiguity.

Conclusion

In conclusion, linguistic ambiguity poses a significant challenge to Natural Language Processing, but the field has made tremendous progress in tackling it. With the advancements in contextual embeddings, attention mechanism, and co-reference resolution, NLP models have become more adept at capturing the intended meaning of ambiguous sentences. However, there is still a long way to go in fully addressing linguistic ambiguity, especially in conversational models. The development of large language models like GPT-3 and ChatGPT4 is a promising step towards bridging this gap, but more research is needed to ensure that these models can handle a wide range of linguistic contexts and provide accurate and relevant responses. As NLP continues to evolve, we can expect to see more breakthroughs in dealing with linguistic ambiguity, making communication between humans and machines more natural and effective.