<bos><start_of_turn>system
You are a helpful AI assistant.
lista de codigos linguisticos disponibles: ["es", "en"].<end_of_turn>
<start_of_turn>user
¿Qué desafíos surgen al manejar palabras fuera del vocabulario en los sistemas de PNL?<end_of_turn>
<start_of_turn>model
Out-of-vocabulary (OOV) words can pose significant challenges for NLP systems, hindering their ability to understand and process text effectively. Here are some key issues:

1. Reduced accuracy and performance:

When an OOV word is encountered, the NLP system may misinterpret its meaning or assign it an incorrect grammatical role. This can lead to errors in tasks like sentiment analysis, machine translation, and text summarization.

2. Limited understanding of context:

NLP systems often rely on context clues and surrounding words to infer the meaning of unfamiliar terms. However, OOV words can disrupt this process, making it harder for the system to grasp the overall meaning of the text.

3. Data sparsity and model bias:

Training data for NLP models typically focuses on common words and phrases. This can lead to data sparsity for OOV words, making it difficult for the model to learn their meaning and usage patterns. Additionally, biases present in the training data can be amplified when applied to OOV words, leading to unfair or discriminatory outcomes.

4. Difficulty in handling creative language and slang:

Creative language, slang terms, and newly coined words often fall outside the vocabulary of standard NLP models. This can make it challenging for these systems to understand and respond to informal conversations or user-generated content.

5. Increased computational cost:

Some OOV handling techniques, such as dictionary lookups or specialized word embedding models, can add to the computational overhead of NLP systems. This can be especially problematic for real-time applications with low latency requirements.
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