\chapter{NATURAL LANGUAGE PROCESSING} \label{c1}
Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and human (natural) languages.

\section{Introduction to Natural Language Processing}
The primary goal of NLP is to enable machines to understand, interpret, generate, and respond to textual or spoken language in a way that is meaningful and contextually appropriate. NLP combines techniques from linguistics (the study of language structure), computer science (algorithms and data structures), and machine learning (statistical pattern recognition) to process and analyze vast amounts of natural language data. The scope of NLP spans a wide array of tasks such as speech recognition, language translation, information retrieval, sentiment analysis, and question answering. Modern NLP systems often rely on large-scale datasets and deep learning architectures such as recurrent neural networks (RNNs), transformers, and pre-trained language models like BERT and GPT. The development of NLP technologies has made significant progress due to the availability of big data, powerful computational resources, and improved learning algorithms{\cite{Reference9}}.

\subsection{NLP vs. Traditional Text Processing}
Traditional text processing methods, such as rule-based systems and pattern matching using regular expressions, are designed to manipulate strings and symbols without a true understanding of language semantics or syntax. These methods are effective in well-defined, narrow domains but fail to generalize across diverse and ambiguous inputs.

In contrast, NLP systems model language statistically and contextually. They learn the structure and meaning of language from large corpora of text using supervised or unsupervised learning techniques. For example, while traditional methods may extract keywords from a sentence, NLP can perform tasks like syntactic parsing, named entity recognition, and semantic role labeling, providing a richer and more flexible understanding. A significant advantage of NLP over traditional techniques lies in its ability to deal with unseen data, learn linguistic patterns automatically, and adapt to changes in vocabulary and usage.

\subsection{Challenges in NLP}
Despite substantial advances, NLP still faces many challenges due to the intricacies and irregularities of natural language:

\begin{itemize}
  \item \textbf{Lexical Ambiguity:} A single word may have multiple meanings depending on the context. For example, the word ``bark'' can refer to a dog's sound or the outer layer of a tree.
  
  \item \textbf{Syntactic Ambiguity:} A sentence may have multiple grammatical structures. For instance, ``Visiting relatives can be boring'' can mean either that the act of visiting relatives is boring or that relatives who visit are boring.
  
  \item \textbf{Semantic Ambiguity:} The same phrase can imply different meanings. For example, ``He saw the man with the telescope'' could mean that the man had the telescope, or that the speaker used a telescope to see the man.
  
  \item \textbf{Contextual Understanding:} Understanding language requires maintaining coherence across multiple sentences or turns in a conversation. Pronouns, ellipsis, and idiomatic expressions require models to retain and apply contextual knowledge.
  
  \item \textbf{Resource Scarcity:} Many languages and dialects lack large annotated corpora or linguistic tools, making it difficult to build effective NLP models for these languages.
  
  \item \textbf{Multilingualism and Code-Mixing:} Real-world text often involves multiple languages used simultaneously, especially in multilingual societies and social media platforms. Handling such inputs requires models that are language-agnostic or capable of dynamic adaptation.
  
  \item \textbf{Noise and Informality:} Online data, such as social media or SMS texts, is often noisy, with spelling errors, slang, abbreviations, and irregular grammar. This increases the complexity of language modeling and pre-processing.
\end{itemize}

Understanding and addressing these challenges is crucial to building robust and generalizable NLP systems. Research continues to evolve in areas such as contextual embeddings, transfer learning, and multilingual modeling to address these limitations.


\section{Basic NLP Tasks}

Natural Language Processing begins with foundational tasks that prepare and analyze raw text. These basic tasks form the building blocks for more complex NLP applications. They help convert unstructured text into structured forms that machines can understand and manipulate efficiently. The Text Preprocessing Pipeline is shown in Figure~\ref{Figure80}.
\begin{figure}[h]
 	\begin{center}
 		\includegraphics[height=5cm, width=12cm]{Figure80}
 		\caption{Text Preprocessing Pipeline}
 		\label{Figure80}
 	\end{center}
 	 \end{figure}

\subsection{Tokenization and Normalization}

\textbf{Tokenization} is the process of breaking down text into smaller units called tokens. Tokens can be words, subwords, characters, or sentences depending on the granularity required by the application.

For example, the sentence: \\
\textit{"The cat’s toy is missing."} \\
can be tokenized as: \texttt{["The", "cat", "'s", "toy", "is", "missing", "."]}

Modern tokenizers also account for punctuation, contractions, and special characters. Subword tokenization (used in BERT, GPT) helps handle out-of-vocabulary words.

\textbf{Normalization} refers to converting tokens to a standardized format:
\begin{itemize}
    \item \textbf{Lowercasing}: Convert all characters to lowercase (e.g., "Apple" → "apple")
    \item \textbf{Removing punctuation, numbers, and symbols}
    \item \textbf{Lemmatization}: Reduce words to their dictionary base forms using grammar-aware rules (e.g., "better" → "good")
    \item \textbf{Stemming}: Remove affixes from words using heuristic rules (e.g., "studies" → "studi")
\end{itemize}

Lemmatization is typically more accurate than stemming, though computationally more expensive.

\subsection{Stop Word Removal and Stemming}

\textbf{Stop words} are common words (e.g., “the”, “is”, “in”) that may be removed in information retrieval and text classification tasks to reduce dimensionality and noise.

\textbf{Stemming} involves chopping off prefixes or suffixes to obtain the root word. For example:
\begin{itemize}
    \item “playing” → “play”
    \item “studies” → “studi”
\end{itemize}
Popular stemmers include the Porter Stemmer and Snowball Stemmer. While stemming may lead to non-lexical forms, it is fast and useful for shallow NLP tasks. For grammatically correct output, \textbf{lemmatization} is preferred.

\subsection{Part-of-Speech (POS) Tagging}

Part-of-Speech tagging is the process of assigning grammatical categories to each word in a sentence (e.g., noun, verb, adjective, etc.).

\noindent
\textbf{Example:} \\
\textit{"Dogs bark loudly"} → \texttt{Dogs/NN bark/VB loudly/RB}

POS tagging helps disambiguate word roles and is essential for syntactic parsing and named entity recognition. Statistical models such as Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs) are commonly used. The tagging problem can be formulated as:
\[
T^* = \arg\max_T P(T|W) = \arg\max_T P(W|T) \cdot P(T)
\]
Where:
\begin{itemize}
    \item \( T = (t_1, ..., t_n) \): tag sequence
    \item \( W = (w_1, ..., w_n) \): word sequence
\end{itemize}

\subsection{Syntactic Parsing (Dependency and Constituency Parsing)}

\textbf{Parsing} determines the syntactic structure of a sentence. It helps understand how words relate to each other grammatically.

\noindent
\textbf{Dependency Parsing} models relationships as directed links between words (head → dependent).  
\textbf{Constituency Parsing} divides sentences into nested sub-phrases (NP, VP, etc.) to form a hierarchical tree.

\textbf{Example:} \textit{"The dog chased the cat"}  
\begin{itemize}
    \item Dependency: \texttt{chased → dog (nsubj), chased → cat (dobj)}
    \item Constituency: \texttt{[S [NP The dog] [VP chased [NP the cat]]]}
\end{itemize}

Parsing is essential for downstream tasks like question answering and machine translation. Algorithms include CKY parsing, shift-reduce parsers, and neural transition-based parsers.


\section{Word Representations}

Word representation is a fundamental step in NLP, where words or phrases from the vocabulary are mapped to numerical vectors so that machine learning algorithms can process them. These representations capture the syntactic and semantic properties of language. Early approaches were simple and sparse, but modern techniques use dense, distributed embeddings learned from data.

\subsection{One-Hot Encoding and Bag of Words (BoW)}

\textbf{One-hot encoding} represents each word in a vocabulary of size \( V \) as a binary vector of length \( V \), with a 1 at the index corresponding to the word and 0s elsewhere. For example, for a vocabulary \texttt{[cat, dog, fish]}, the word "dog" would be:
\[
\text{dog} = [0, 1, 0]
\]

\textbf{Limitations:}
\begin{itemize}
    \item High dimensionality and sparsity
    \item No notion of similarity between words (e.g., “king” and “queen” are orthogonal)
\end{itemize}

\noindent
\textbf{Bag of Words (BoW)} represents a document as a frequency distribution over the vocabulary. Word order is ignored.

Example:
\textit{"the dog chased the cat"} → \texttt{[the: 2, dog: 1, chased: 1, cat: 1]}

BoW vectors can be improved using weighting schemes like TF-IDF.

\subsection{Term Frequency–Inverse Document Frequency (TF-IDF)}

TF-IDF is a statistical measure used to evaluate how important a word is to a document in a corpus. It reduces the weight of commonly occurring terms and highlights rare but meaningful words.

Let:
\begin{itemize}
    \item \( f(w, d) \): frequency of word \( w \) in document \( d \)
    \item \( df(w) \): number of documents in which \( w \) appears
    \item \( N \): total number of documents
\end{itemize}

Then:
\[
TF(w, d) = \frac{f(w, d)}{\sum_{w'} f(w', d)} \quad
IDF(w) = \log \left( \frac{N}{df(w)} \right) \quad
TFIDF(w, d) = TF(w, d) \times IDF(w)
\]

TF-IDF improves retrieval and text classification but still lacks semantic similarity.

\subsection{Word Embeddings}

The Word Embedding Space Visualization is shown in Figure~\ref{Figure81}. Word embeddings are dense vector representations that capture the meaning of words based on context. They place semantically similar words closer together in a continuous vector space. Popular embedding techniques:
\begin{itemize}
    \item Word2Vec
    \item GloVe
    \item FastText
\end{itemize}

\begin{figure}[h]
 	\begin{center}
 		\includegraphics[height=8cm, width=10cm]{Figure81}
 		\caption{Word Embedding Space Visualization}
 		\label{Figure81}
 	\end{center}
 	 \end{figure}

\subsubsection{Word2Vec (CBOW and Skip-gram)}

Word2Vec trains shallow neural networks to learn word representations based on neighboring words.

\noindent
\textbf{CBOW (Continuous Bag of Words):} Predicts the target word based on surrounding context.

\noindent
\textbf{Skip-gram:} Predicts context words from a given target word.

The Skip-gram objective:
\[
J = -\sum_{t=1}^{T} \sum_{-c \leq j \leq c, j \neq 0} \log P(w_{t+j} | w_t)
\]
Where \( c \) is the window size. \textbf{Optimization:} Techniques like negative sampling or hierarchical softmax are used to reduce computation.

\subsubsection{GloVe (Global Vectors for Word Representation)}

GloVe builds word vectors using global co-occurrence counts. Let \( X_{ij} \) be the number of times word \( j \) occurs in the context of word \( i \). Then the loss function is:
\[
J = \sum_{i,j=1}^{V} f(X_{ij}) \left(w_i^T \tilde{w}_j + b_i + \tilde{b}_j - \log X_{ij} \right)^2
\]
Where:
\begin{itemize}
    \item \( w_i, \tilde{w}_j \): word and context vectors
    \item \( b_i, \tilde{b}_j \): bias terms
    \item \( f(X) \): weighting function to reduce the impact of large counts
\end{itemize}

GloVe captures both global statistics and local context, improving semantic representation.

\subsubsection{Comparison and Visualization of Embeddings}

\textbf{Comparison:}
\begin{itemize}
    \item Word2Vec focuses on local context through sliding windows.
    \item GloVe leverages global word co-occurrence statistics.
    \item FastText (not detailed here) includes subword information for rare words.
\end{itemize}

\noindent
\textbf{Visualization:} High-dimensional embeddings can be visualized using dimensionality reduction techniques such as:
\begin{itemize}
    \item \textbf{PCA (Principal Component Analysis)}
    \item \textbf{t-SNE (t-distributed Stochastic Neighbor Embedding)}
\end{itemize}

These visualizations reveal clusters of semantically related words and syntactic analogies (e.g., \texttt{king - man + woman} $\approx$ \texttt{queen}).



\section{Deep Learning for NLP}

Deep learning has significantly transformed the field of Natural Language Processing (NLP), enabling the modeling of complex linguistic structures and semantics with minimal human intervention. Unlike traditional methods that rely heavily on manual feature engineering, deep learning models automatically extract and learn features from raw text data, capturing hierarchical and long-range dependencies effectively.

\subsection{Sequence Models and RNNs}
Sequential data such as sentences and paragraphs exhibit temporal or positional dependencies between words. Recurrent Neural Networks (RNNs) are designed to handle such sequences by maintaining a hidden state that captures information from previous steps. An RNN computes the hidden state and output at time step \( t \) as:
\[
    h_t = \sigma(W_{xh}x_t + W_{hh}h_{t-1} + b_h) \\
    y_t = W_{hy}h_t + b_y
\]
Where:
\begin{itemize}
    \item \( x_t \): input at time \( t \)
    \item \( h_t \): hidden state at time \( t \)
    \item \( y_t \): output at time \( t \)
    \item \( \sigma \): non-linear activation function (e.g., tanh or ReLU)
\end{itemize}

RNNs are useful for language modeling, next-word prediction, and named entity recognition, but they struggle with long sequences due to vanishing gradients.

\subsection{Encoder-Decoder Architecture}
The encoder-decoder framework is designed for sequence-to-sequence tasks such as machine translation and summarization. The encoder processes the input sequence into a fixed-length context vector, while the decoder generates the output sequence based on this vector. Let the input sequence be \( X = (x_1, x_2, ..., x_T) \) and the target sequence \( Y = (y_1, y_2, ..., y_{T'}) \). The encoder computes:
\[
    h_t = \text{RNN}_{\text{enc}}(x_t, h_{t-1})
\]
The final hidden state \( h_T \) is passed to the decoder:
\[
    s_t = \text{RNN}_{\text{dec}}(y_{t-1}, s_{t-1}, h_T)
\]
The decoder outputs \( y_t \) at each step. Although effective, this architecture suffers from bottlenecks when encoding long sequences into a single context vector. The Encoder-Decoder Architecture is shown in Figure~\ref{Figure82}.

\begin{figure}[h]
 	\begin{center}
 		\includegraphics[height=3cm, width=12cm]{Figure82}
 		\caption{Encoder-Decoder Architecture}
 		\label{Figure82}
 	\end{center}
 	 \end{figure}


\subsection{Attention Mechanism}
Attention mechanisms address the limitations of encoder-decoder models by allowing the decoder to focus on different parts of the input sequence at each step. Given encoder hidden states \( h_1, ..., h_T \) and decoder state \( s_t \), attention weights are computed as:
\[
    \alpha_{t,i} = \frac{\exp(e_{t,i})}{\sum_j \exp(e_{t,j})}, \quad \text{where } e_{t,i} = a(s_{t-1}, h_i)
\]
\[
    c_t = \sum_{i=1}^{T} \alpha_{t,i} h_i
\]
\( c_t \) is the context vector at step \( t \), and \( a(\cdot) \) is a scoring function (e.g., dot-product or feed-forward network). Attention enables better handling of long sequences and improves model interpretability. It is foundational to the Transformer architecture and state-of-the-art NLP models. The Attention Mechanism in NLP is shown in Figure~\ref{Figure83}.

\begin{figure}[h]
 	\begin{center}
 		\includegraphics[height=6cm, width=10cm]{Figure83}
 		\caption{Attention Mechanism in NLP}
 		\label{Figure83}
 	\end{center}
 	 \end{figure}



\section{Transformer-Based Models}

Transformer-based models have revolutionized Natural Language Processing (NLP) by introducing a parallelizable architecture that relies entirely on attention mechanisms, removing the need for recurrence. The Transformer architecture forms the foundation for state-of-the-art models such as BERT, GPT, and T5.

\subsection{The Transformer Architecture: Self-Attention and Multi-head Attention}

The core idea of the Transformer is the \textbf{self-attention mechanism}, which computes the relevance of each word to every other word in the sequence.  Given an input sequence represented as a matrix \( X \in \mathbb{R}^{n \times d} \), the model computes:
\[
Q = XW^Q, \quad K = XW^K, \quad V = XW^V
\]
where \( W^Q, W^K, W^V \) are learned projection matrices.

\noindent
The \textbf{Scaled Dot-Product Attention} is computed as:
\[
\text{Attention}(Q, K, V) = \text{softmax}\left( \frac{QK^T}{\sqrt{d_k}} \right)V
\]

\noindent
\textbf{Multi-head Attention} runs multiple self-attention mechanisms (heads) in parallel:
\[
\text{MultiHead}(Q, K, V) = \text{Concat}(\text{head}_1, ..., \text{head}_h)W^O
\]
Each head computes:
\[
\text{head}_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)
\]

In addition to attention, the Transformer block includes:
\begin{itemize}
    \item \textbf{Position-wise Feedforward Layer:}
    \[
    \text{FFN}(x) = \max(0, xW_1 + b_1)W_2 + b_2
    \]
    \item \textbf{Positional Encoding:} Injects position information using sine and cosine functions.
\end{itemize}

\subsection{BERT: Bidirectional Encoder Representations from Transformers}

BERT is a deep bidirectional Transformer encoder pre-trained using:
\begin{itemize}
    \item \textbf{Masked Language Modeling (MLM):} Randomly masks words and trains the model to predict them.
    \item \textbf{Next Sentence Prediction (NSP):} Trains the model to predict if one sentence follows another.
\end{itemize}

BERT representations are context-sensitive and bidirectional, enabling strong performance on tasks such as QA, NER, and sentiment analysis.

\subsection{GPT: Generative Pretrained Transformer}

GPT is based on the decoder portion of the Transformer architecture. It is trained using:
\begin{itemize}
    \item \textbf{Left-to-right language modeling:}
    \[
    P(w_t | w_1, w_2, ..., w_{t-1})
    \]
\end{itemize}

GPT excels in generative tasks such as text completion, story generation, and dialogue systems. Variants include GPT-2, GPT-3, and GPT-4 with increased model capacity.

\subsection{Comparison between BERT and GPT}

\begin{itemize}
    \item \textbf{BERT:} Encoder-only; bidirectional; strong in understanding tasks.
    \item \textbf{GPT:} Decoder-only; unidirectional; excels in generative tasks.
    \item BERT uses MLM + NSP; GPT uses autoregressive LM.
\end{itemize}

\subsection{Fine-Tuning and Transfer Learning in NLP}

Both BERT and GPT are pretrained on large corpora and then fine-tuned on downstream tasks using task-specific labeled data. \textbf{Transfer learning} in NLP involves:
\begin{itemize}
    \item Using pretrained embeddings or models as initial layers.
    \item Fine-tuning entire model or just output layers.
\end{itemize}

This process significantly reduces labeled data requirements and improves generalization.



\section{Applications of NLP}

Natural Language Processing has a wide range of applications across industries including healthcare, finance, education, and customer service. NLP systems enable machines to interpret, understand, generate, and respond to human language. With the advent of deep learning and transformer models, the capabilities of NLP systems have expanded dramatically in recent years. The Applications of NLP are shown in Figure~\ref{Figure84}.

\begin{figure}[h]
 	\begin{center}
 		\includegraphics[height=6cm, width=10cm]{Figure84}
 		\caption{Applications of NLP}
 		\label{Figure84}
 	\end{center}
 	 \end{figure}


\subsection{Machine Translation}

Machine Translation (MT) refers to the automatic conversion of text from one language to another. Traditionally, MT relied on rule-based or phrase-based statistical methods. These systems struggled with syntactic and semantic alignment, especially for morphologically rich or low-resource languages. Modern MT uses neural architectures like sequence-to-sequence (Seq2Seq) models, attention mechanisms, and Transformers. In this setup, the encoder processes the source sentence into a hidden representation, while the decoder generates the target sentence token-by-token. Mathematically, the translation probability is modeled as:
\[
P(Y|X) = \prod_{t=1}^{T'} P(y_t | y_1, \dots, y_{t-1}, X)
\]

State-of-the-art systems like Google’s Neural Machine Translation (GNMT) and OpenAI’s Whisper support multi-lingual and low-resource translation with near-human quality.

\subsection{Question Answering}

Question Answering (QA) systems retrieve or generate answers from a given context. There are two main types:
\begin{itemize}
    \item \textbf{Extractive QA:} Identifies the answer span within the given context (e.g., SQuAD dataset).
    \item \textbf{Abstractive QA:} Generates answers in natural language, potentially requiring reasoning.
\end{itemize}

Transformer-based models like BERT are fine-tuned to predict the start and end tokens of the answer in the context. For example:

\noindent
\textbf{Context:} "The Nile is the longest river in Africa."\\

\noindent
\textbf{Question:} "Which is the longest river in Africa?"\\

\noindent
\textbf{Answer:} "The Nile"

The model uses attention to understand the relationship between the question and relevant parts of the context. QA is widely used in search engines, customer support bots, and educational platforms.

\subsection{Sentiment Analysis}

Sentiment analysis determines the sentiment expressed in a given text—positive, negative, or neutral. It is widely used for:
\begin{itemize}
    \item Brand monitoring and market analysis
    \item Social media monitoring
    \item Customer service automation
\end{itemize}

Traditional methods relied on bag-of-words with classifiers like Naïve Bayes. Deep learning models like CNNs, LSTMs, and BERT fine-tuned for sentiment classification have vastly improved accuracy. The classification task can be represented as:
\[
\hat{y} = \arg\max_{c \in C} P(c | x)
\]
Where \( x \) is the input text, and \( C \) is the set of sentiment labels.

\subsection{Text Summarization}

Text summarization aims to condense large documents into shorter versions while preserving essential content.

\noindent
\textbf{Extractive Summarization} identifies important sentences or phrases based on statistical or learning-based features.\\

\noindent
\textbf{Abstractive Summarization} generates new text using encoder-decoder or transformer models to paraphrase and synthesize key content. Models like BART (Bidirectional and Auto-Regressive Transformers) and T5 (Text-To-Text Transfer Transformer) achieve state-of-the-art performance on summarization benchmarks such as CNN/DailyMail. Evaluation metrics include ROUGE (Recall-Oriented Understudy for Gisting Evaluation) which compares the generated summary with reference summaries.

\subsection{Named Entity Recognition (NER)}

NER systems identify and classify entities in text into predefined categories such as:
\begin{itemize}
    \item \textbf{Person (PER)}
    \item \textbf{Organization (ORG)}
    \item \textbf{Location (LOC)}
    \item \textbf{Date/Time (DATE)}
\end{itemize}

Example: \\
\textbf{Sentence:} "Apple Inc. was founded by Steve Jobs in California."\\
\textbf{NER Output:} [Apple Inc. → ORG], [Steve Jobs → PERSON], [California → LOCATION]. NER is typically formulated as a sequence labeling problem using models like BiLSTM-CRF or token-level classification with Transformers. It is critical for information extraction, document indexing, and knowledge graph construction.



\section{Challenges and Future Directions in NLP}

Despite tremendous progress, Natural Language Processing still faces several fundamental challenges, especially in handling ambiguity, bias, multilingualism, and domain adaptation. As models grow in scale and capability, it is equally important to address their limitations and future directions responsibly.

\subsection{Handling Ambiguity and Context}

Language is inherently ambiguous and context-sensitive. The same word or phrase can have different meanings depending on usage. Example: “He saw the man with the telescope.” – Who has the telescope?. Traditional NLP systems struggle with such ambiguity, while modern transformer-based models improve disambiguation by attending to larger contexts. However, even these models sometimes fail in nuanced scenarios such as sarcasm or metaphorical expressions. Advancing contextual awareness remains a key research focus.

\subsection{Multilingual NLP}

Multilingual NLP aims to build systems that understand and generate text in multiple languages. Challenges include:
\begin{itemize}
    \item Lack of training data for low-resource languages.
    \item Linguistic diversity (syntax, morphology, writing systems).
    \item Code-switching, where speakers mix languages in one sentence.
\end{itemize}

Models like mBERT and XLM-R attempt cross-lingual generalization using shared embeddings and aligned pretraining. However, quality remains uneven across languages.

\subsection{Ethics and Bias in Language Models}

Large language models often inherit biases from their training data, leading to unethical or harmful outputs. Common issues include:
\begin{itemize}
    \item Gender, racial, and cultural stereotypes.
    \item Offensive or toxic content generation.
    \item Privacy concerns from memorizing sensitive data.
\end{itemize}

Bias mitigation techniques include data filtering, adversarial training, differential privacy, and human-in-the-loop moderation. Ethical evaluation frameworks and transparency are becoming essential in NLP research and deployment.

\subsection{Trends}

Emerging trends point to a future where NLP is not limited to text:
\begin{itemize}
    \item \textbf{Multimodal NLP:} Integrating text with images, audio, and video (e.g., CLIP, Flamingo, GPT-4V).
    \item \textbf{Zero-shot and Few-shot Learning:} Leveraging large pretrained models (e.g., GPT-3) that can generalize to new tasks with little or no task-specific training data.
    \item \textbf{Interactive and Explainable NLP:} Building systems that can reason, justify decisions, and engage in dialogue.
    \item \textbf{Low-resource Adaptation:} Training robust models for rare languages and specialized domains (e.g., legal, biomedical).
\end{itemize}

These directions will shape the next generation of intelligent, safe, and universally accessible NLP systems.





















