Final answer:
The accuracy of a classification model can be improved using features like frequency count of terms, part of speech tagging, dependency grammar, and vector notation of sentences, which add nuanced dimensions for the algorithm to analyze.
Step-by-step explanation:
The features that can be used for accuracy improvement of a classification model include: Frequency count of terms: This involves counting how often each term appears within the dataset. By identifying the most common terms, the model can focus on the most prevalent features that may be indicative of a particular class.
Part of speech tag: Assigning parts of speech (such as nouns, verbs, adjectives) to each word in a text can help the model to make use of grammatical structures in order to improve its predictive ability. Dependency grammar: Understanding how words in a sentence are related to each other through a dependency grammar can provide contextual clues that enhance classification accuracy.
Vector notation of sentence: Representing sentences as vectors in a high-dimensional space (often via methods such as Word2Vec or GloVe) allows the model to understand semantics and use the geometric relationships between words for better classification. All these features add different dimensions of analysis that can improve the performance of a classification model by providing more nuanced data points for the algorithm to learn from.