NLP aims to bridge the gap between human language and machine understanding, allowing computers to process and respond to natural language input.

Sentiment Analysis, Machine Translation, and Named Entity Recognition are all essential tasks in NLP, involving understanding and manipulating natural language.

Semantic Parsing aims to extract the underlying meaning and structure from natural language text, enabling machines to understand the intent and semantics of the input.

Dependency Parsing, Constituency Parsing, and Semantic Role Labeling are all widely used approaches to Semantic Parsing, each focusing on different aspects of the sentence structure and meaning.

Word Embeddings are vector representations of words that encode their semantic and syntactic information, allowing for efficient processing and analysis of natural language.

Abstractive Summarization involves generating a concise and informative summary of a text by extracting key information and expressing it in a new, coherent form.

Syntax plays a crucial role in NLP by defining the structure, word order, parts of speech, and semantic roles in a sentence, facilitating the understanding of its meaning.

NLP finds various applications in healthcare, including Medical Diagnosis by analyzing patient data, Drug Discovery by extracting information from scientific literature, and Patient Record Summarization for efficient medical record management.

Semantic Parsing faces several challenges, including ambiguity in natural language, limited availability of labeled data for training, and the computational complexity of parsing algorithms.

The CoNLL-2007 dataset is a commonly used benchmark for Semantic Parsing, consisting of annotated sentences with their corresponding semantic representations.

Coreference Resolution aims to identify and link different mentions of the same entity within a text, helping to establish the relationships between different parts of the discourse.

Machine Translation involves translating text from one language to another, and different approaches include Rule-based, Statistical, and Neural Machine Translation, each with its own strengths and limitations.

Contextual Embeddings represent words based on their context, allowing for more nuanced understanding of word meaning and enabling tasks like sentiment analysis and question answering.

Extractive Question Answering involves extracting the answer to a question directly from a given text, while Generative Question Answering generates the answer from scratch.

Pragmatics in NLP focuses on understanding the context and speaker's intent in communication, considering factors like tone, body language, and shared knowledge.