Other NLP Projects

Applied NLP projects covering text classification, entity extraction, and subword-level analysis, with emphasis on linguistic structure, evaluation, and practical modeling trade-offs.

Overview

This page summarizes a set of smaller-scale NLP projects and experiments developed across different contexts (academic, exploratory, and applied work). While diverse in scope, all projects focus on linguistic structure, representation, and supervised or weakly supervised learning.

They complement larger, standalone projects by demonstrating breadth of NLP techniques and hands-on experimentation.

Text classification

Work on text classification covered multiple problem settings:

• Binary and multi-class classification
  (e.g., topic detection, sentiment polarity, domain classification)
• Multi-label classification
  where documents may belong to several categories simultaneously
• Feature-based and neural approaches:
  • bag-of-words / TF–IDF
  • n-grams
  • neural embeddings (CNN/RNN-based encoders, later transformer-based)

Focus areas included:

• feature engineering vs. representation learning,
• class imbalance handling,
• model evaluation beyond accuracy (precision/recall/F1).

Named Entity Extraction

Several projects explored entity extraction and sequence labeling, including:

• rule-based and statistical approaches for named entity recognition (NER),
• extraction of domain-specific entities (non-standard entity sets),
• token-level labeling using BIO-style schemes.

Key aspects:

• tokenization and normalization challenges,
• ambiguity and boundary detection,
• error analysis at the span level rather than token level.

Syllable identification and subword analysis

Another line of work focused on subword-level linguistic structure, including:

• syllable identification in words,
• analysis of phonotactic and orthographic patterns,
• rule-based and data-driven heuristics for syllabification.

This work highlighted:

• the importance of linguistic constraints,
• limitations of purely statistical approaches on small datasets,
• usefulness of hybrid rule + ML solutions.

Common themes across projects

Across these NLP projects, recurring themes include:

• careful problem formulation,
• explicit handling of linguistic structure (tokens, subwords, entities),
• strong emphasis on evaluation and error analysis,
• preference for interpretable baselines before complex models.