Assignment 3: Representing Meaning - A Computational Exploration of Semantic Space

Overview

"You shall know a word by the company it keeps." - J.R. Firth, 1957

In this assignment, you will embark on a deep exploration of how machines represent meaning. Working with 250,000 Wikipedia articles, you will implement and compare methods spanning five decades of computational linguistics and natural language processing - from classical statistical techniques to modern large language models.

This assignment asks fundamental questions: What does it mean to "understand" the meaning of a document? How do different computational approaches capture semantic relationships? What aspects of human semantic knowledge can be modeled through distributional representations? Which methods best capture the conceptual structure of human knowledge as encoded in Wikipedia?

You will implement ~10 different embedding methods, perform sophisticated quantitative and qualitative analyses, create beautiful interactive visualizations, and connect your findings to theories of meaning in cognitive science and linguistics. This is a substantial, 2-week assignment that will deepen your understanding of how we represent and compute with meaning.

Dataset

You can automatically download the dataset from Dropbox if it doesn't already exist in your working directory. The following code will handle downloading the dataset, checking if it's present, and loading it into your notebook:

import os
import urllib.request
import pickle
Define the file name and URL
dataset_url = 'https://www.dropbox.com/s/v4juxkc5v2rd0xr/wikipedia.pkl?dl=1'
dataset_path = 'wikipedia.pkl'
Download the dataset if it doesn't exist
if not os.path.exists(dataset_path):
    print("Downloading dataset...")
    urllib.request.urlretrieve(dataseturl, datasetpath)
    print("Download complete.")
Load the dataset
with open(dataset_path, 'rb') as f:
    wikipedia = pickle.load(f)

• 'title': The title of the article (string).
• 'text': The full text of the article (string).
• 'id': A unique identifier for the article (string).
• 'url': The link to the Wikipedia page (string).

Learning Objectives

• Understand the evolution of semantic representation from classical to modern NLP
• Implement and compare traditional, neural, and LLM-based embedding methods
• Develop expertise in clustering evaluation and unsupervised learning
• Connect computational methods to cognitive theories of semantic memory
• Create publication-quality visualizations of high-dimensional semantic spaces
• Think critically about what different methods capture (and miss) about meaning

Part 1: Implementing the Embedding Zoo (40 points)

Task 1.1: Classical Statistical Methods (8 points)

Implement two foundational approaches from classical NLP:

• Use TF-IDF followed by truncated SVD (dimensionality reduction)
• Implementation: sklearn.feature_extraction.text.TfidfVectorizer + sklearn.decomposition.TruncatedSVD
• Reduce to 300 dimensions
• Resources: Original LSA paper (Deerwester et al., 1990)

• Classic probabilistic topic model
• Implementation: sklearn.decomposition.LatentDirichletAllocation
• Use 50-100 topics (experiment with different values)
• Resources: Original LDA paper (Blei et al., 2003)

• What do the LSA dimensions represent? What about LDA topics?
• How interpretable are the resulting representations?
• Do these methods capture syntactic or semantic relationships better?

Task 1.2: Static Word Embeddings (8 points)

Implement three influential neural word embedding methods, aggregating word vectors to create document embeddings:

• Use pre-trained word2vec-google-news-300 from gensim
• Aggregate word vectors (try: mean, TF-IDF weighted mean, max pooling)
• Resources: Efficient Estimation of Word Representations (Mikolov et al., 2013)

• Use pre-trained glove-wiki-gigaword-300 from gensim
• Aggregate word vectors using same methods as Word2Vec
• Resources: GloVe: Global Vectors (Pennington et al., 2014)

• Use pre-trained fasttext-wiki-news-subwords-300 from gensim
• Aggregate word vectors; FastText handles OOV words through subword embeddings
• Resources: Enriching Word Vectors with Subword Information (Bojanowski et al., 2017)

• For each method, experiment with different aggregation strategies
• Compare simple averaging vs. TF-IDF weighted averaging
• Handle articles longer than typical context windows appropriately

Task 1.3: Contextualized Embeddings (8 points)

Implement transformer-based embeddings that capture context:

• Use bert-base-uncased from Hugging Face Transformers
• Extract embeddings from the [CLS] token or mean pool over all tokens
• Handle long documents with sliding windows or truncation
• Resources: BERT: Pre-training of Deep Bidirectional Transformers (Devlin et al., 2019)

• Use gpt2 from Hugging Face Transformers
• Extract embeddings from final token or mean pool
• Compare different pooling strategies
• Resources: Language Models are Unsupervised Multitask Learners (Radford et al., 2019)

• How do contextualized embeddings differ from static ones?
• What information do [CLS] tokens capture?
• How does truncation affect representation quality?

Task 1.4: Modern Sentence/Document Embeddings (8 points)

Implement state-of-the-art embedding methods designed specifically for sentences and documents:

• Use all-MiniLM-L6-v2 or all-mpnet-base-v2 from sentence-transformers
• These are fine-tuned specifically for semantic similarity
• Resources: Sentence-BERT (Reimers | Gurevych, 2019)

• Use Llama 3 8B via llm2vec or similar embedding adapter
• Alternative: Use Llama 3 hidden states directly
• Resources: LLM2Vec paper

• Why are sentence transformers better for semantic similarity?
• How do LLM embeddings compare to smaller, specialized models?
• What's the trade-off between model size and embedding quality?

Task 1.5: Modern Topic Models (8 points)

Implement neural topic models that combine traditional topic modeling with modern embeddings:

• Uses BERT embeddings + UMAP + HDBSCAN for topic discovery
• Implementation: bertopic library
• Analyze discovered topics and their coherence
• Resources: BERTopic documentation

• Jointly learns document and word embeddings with topic vectors
• Implementation: top2vec library
• Compare topic quality with BERTopic and classical LDA
• Resources: Top2Vec paper (Angelov, 2020)

• How do neural topic models differ from LDA?
• Are the discovered topics more coherent? More diverse?
• Can you validate topics against known Wikipedia categories?

• Implementation of all 10+ embedding methods
• For each method: clear documentation of hyperparameters chosen
• Analysis of computational cost (time, memory) for each method
• Embeddings saved in a consistent format for downstream analysis

Part 2: Sophisticated Evaluation and Analysis (30 points)

Task 2.1: Clustering with Multiple Algorithms (10 points)

For each embedding method, apply multiple clustering algorithms:

• Determine optimal k using: elbow method, silhouette analysis, gap statistic
• Compare different initialization strategies (k-means++, random)
• Implementation: sklearn.cluster.KMeans

• Use agglomerative clustering with different linkage methods (ward, average, complete)
• Create dendrograms to visualize cluster relationships
• Implementation: sklearn.cluster.AgglomerativeClustering, scipy.cluster.hierarchy

• Automatically discover clusters without specifying k
• Handle noise and outliers
• Implementation: sklearn.cluster.DBSCAN, hdbscan

• Systematic comparison of clustering results across methods
• Justification for final clustering choices for each embedding type
• Analysis: Do different embeddings suggest different optimal numbers of clusters?

Task 2.2: Quantitative Evaluation Metrics (8 points)

Implement comprehensive metrics to evaluate embedding and clustering quality:

• Silhouette Score: measures cluster cohesion and separation
• Davies-Bouldin Index: ratio of within-cluster to between-cluster distances
• Calinski-Harabasz Index: ratio of between-cluster to within-cluster variance
• Dunn Index: ratio of minimum inter-cluster to maximum intra-cluster distance

• Intrinsic dimensionality: estimate effective dimensionality using PCA explained variance
• Local structure preservation: compare nearest neighbors before/after embedding
• Global structure preservation: correlation between distance matrices
• Isotropy: measure how uniformly embeddings fill the space

• Topic coherence (for topic models): PMI, Cv, Umass coherence scores
• Within-cluster semantic similarity: average cosine similarity within clusters
• Between-cluster semantic distance: separation of cluster centroids

• Table comparing all embedding methods across all metrics
• Statistical significance testing (e.g., bootstrap confidence intervals)
• Radar/spider plots showing relative strengths of different methods
• Discussion: Which metrics matter most for this task?

Task 2.3: Qualitative Analysis (6 points)

Go beyond numbers to understand what each method captures:

• For each embedding method, examine the top clusters
• Sample representative articles from each cluster
• Use Llama 3 via Ollama to generate descriptive labels for clusters
• Compare cluster interpretability across methods

• Identify articles that are consistently mis-clustered across methods
• Find articles where different methods disagree strongly
• Analyze: what makes these articles difficult?

• Use nearest neighbor search to find similar articles
• Compare: do different methods find different "neighbors"?
• Analyze specific examples (e.g., "Machine Learning" article - what's nearby?)

• Test if embeddings support analogies (e.g., "Paris:France :: London:?")
• Compare methods on capturing different relationship types (geographic, categorical, temporal)

• Cluster labels for top 20 clusters from best-performing methods
• Case studies of interesting articles/clusters
• Analysis of what different embedding types capture (syntax vs. semantics, topics vs. style, etc.)

Task 2.4: Cross-Method Comparison (6 points)

Directly compare embedding methods:

• Compute correlation between distance matrices of different methods
• Use Mantel test for significance
• Create similarity matrix: which methods produce most similar embeddings?

• Where do different methods agree on cluster assignments?
• Use co-association matrix to find robust clusters
• Identify method-specific vs. universal structure

• Plot quality metrics vs. computational cost (time, memory, CO2)
• Identify the "Pareto frontier" of methods
• Recommendation: which method for which use case?

• Comprehensive comparison table/visualization
• Discussion of method families (classical vs. neural vs. LLM)
• Recommendations with justification

Part 3: Advanced Clustering and Visualization (15 points)

Task 3.1: Multi-Level Clustering (5 points)

Explore hierarchical structure in the Wikipedia knowledge space:

• Create multi-level hierarchies (e.g., Science → Physics → Quantum Mechanics)
• Use recursive clustering or hierarchical agglomerative methods
• Validate against Wikipedia's actual category structure (if available)

• Create informative dendrograms showing cluster relationships
• Color-code by major categories
• Interactive dendrograms with Plotly

• Multi-level cluster hierarchy for best-performing method
• Visualization of cluster relationships
• Analysis: Does the automatic hierarchy match human organization?

Task 3.2: Interactive Visualization (10 points)

Create publication-quality, interactive visualizations:

• Apply both UMAP and t-SNE to reduce embeddings to 2D and 3D
• Compare: how do the visualizations differ? Which preserves structure better?
• Experiment with hyperparameters (perplexity for t-SNE, n_neighbors for UMAP)

For each major embedding method, create:

1. 3D Interactive Scatter Plot
   • Each point is an article
   • Color by cluster assignment
   • Size by article length or importance (e.g., page views)
   • Hover: show article title, cluster label, and snippet
   • Enable rotation, zoom, selection
2. 2D Hexbin Density Plot
   • Show density of articles in embedding space
   • Overlay cluster boundaries
   • Interactive region selection
3. Cluster Comparison View
   • Side-by-side comparison of same data in different embedding spaces
   • Linked selections (select in one, highlight in others)
   • Show how cluster assignments change

• Embedding space "map" with topic regions labeled
• Trajectory visualization (if exploring temporal data)
• Network graph of nearest neighbors
• Confusion matrix of cluster assignments across methods

• At least 5 high-quality interactive Plotly visualizations
• Comparison of t-SNE vs. UMAP for this dataset
• Insights discovered through visualization
• Embedded visualizations in notebook with clear captions

Part 4: Cognitive Science Connection (10 points)

Task 4.1: Distributional Semantics Theory (4 points)

Connect your computational work to theories of meaning:

• Explain the distributional hypothesis and its cognitive plausibility
• Discuss: How do computational embeddings relate to human semantic memory?
• Compare to theories: semantic networks, feature-based semantics, prototype theory

• Compare embedding similarities to human similarity judgments
  • Use datasets like SimLex-999, WordSim-353, or create your own
  • Compute correlation between embedding cosine similarity and human ratings
• Compare cluster structure to human categorization
  • Do the discovered clusters match human category boundaries?

• Essay-style section (2-3 pages) connecting your work to cognitive science
• Quantitative comparison to human judgments
• Discussion: What do embeddings capture about human semantics? What do they miss?

Task 4.2: What Is Meaning? (6 points)

Critically analyze what different methods capture:

• What notion of "meaning" does each method operationalize?
• Distinguish: sense vs. reference, intension vs. extension, connotation vs. denotation
• Discuss: Can meaning be reduced to distribution? What's missing?

• Show concrete examples where methods disagree
• Analyze: When is LSA better than BERT? When is LDA better than BERTopic?
• Discuss: Compositionality - how well do methods handle phrases and documents?

• What biases are baked into different embedding methods?
• How do corpus choice and size affect representations?
• Discuss the "Chinese Room" argument in the context of embeddings

• Thoughtful analysis (2-3 pages) of what "meaning" means in your models
• Case studies showing different methods' strengths/weaknesses
• Reflection on the limits of distributional semantics

Part 5: Advanced Extensions and Applications (5 points)

Choose at least ONE of the following extensions:

Option A: Cross-Lingual Embeddings

• Use multilingual models (mBERT, XLM-R, LaBSE)
• Compare same concepts across languages
• Test: Do "Machine Learning" in English and "Apprentissage Automatique" in French cluster together?

• Evaluate cross-lingual alignment quality
• Identify language-specific vs. universal concepts
• Application: cross-lingual information retrieval

Option B: Temporal Analysis

• If dataset has timestamps, analyze how topics evolve over time
• Use dynamic topic models or time-sliced embeddings
• Track: How has "AI" or "Climate Change" evolved in Wikipedia?

• Visualize concept drift over time
• Identify emerging vs. declining topics
• Connect to real-world events

Option C: Practical Applications

Implement at least one real-world application:

• Given a query, find most relevant articles using embedding similarity
• Compare search quality across embedding methods
• Evaluation: NDCG, precision@k, user study

• "Articles similar to this one" feature
• Diversity-aware recommendations (not all from same cluster)
• Evaluation: coverage, diversity, serendipity

• Use LLMs to generate cluster descriptions
• Create Wikipedia "portals" automatically
• Evaluation: human judgment of quality

• Extract relationships between articles from embedding space
• Build graph: nodes = articles, edges = strong semantic similarity
• Analysis: community detection, centrality measures

• Full implementation of chosen extension
• Evaluation demonstrating value
• Discussion of how it enhances understanding of semantic representations

Submission Guidelines

GitHub Classroom Submission

This assignment is submitted via GitHub Classroom. Follow these steps:

1. Accept the assignment: Click the assignment link provided in Canvas or by your instructor
   • Repository: github.com/ContextLab/embeddings-llm-course
   • This creates your own private repository for the assignment
2. Clone your repository:

   git clone https://github.com/ContextLab/embeddings-llm-course-YOUR_USERNAME.git

1. Complete your work:
   • Work in Google Colab, Jupyter, or your preferred environment
   • Save your notebook to the repository
2. Commit and push your changes:

   git add .
   git commit -m "Complete Wikipedia embeddings assignment"
   git push

1. Verify submission: Check that your latest commit appears in your GitHub repository before the deadline

Notebook Requirements

Submit a Google Colaboratory notebook (or Jupyter notebook) that includes:

Technical Requirements

1. Reproducibility
   • All code necessary to download datasets and models
   • Clear installation instructions for required packages
   • Random seeds set for reproducibility
   • Must run in Google Colab with GPU runtime (T4 or better)
   • Estimated runtime: 2-4 hours for full notebook
2. Organization
   • Clear section headers matching assignment parts
   • Table of contents with navigation links
   • Markdown cells explaining approach, decisions, and insights
   • Code comments for complex operations
   • Summary sections after each major part
3. Outputs
   • All visualizations embedded in notebook
   • Tables and metrics clearly formatted
   • Long outputs (model training) can be summarized
   • Save embeddings to files to avoid recomputation
4. Writing Quality
   • Clear, concise explanations
   • Proper citations for papers and methods
   • Academic writing style for analysis sections
   • Proofread for grammar and clarity

What to Submit

1. Primary Deliverable: Google Colab notebook link (ensure sharing is enabled)
2. Optional: Saved embeddings and models (via Google Drive link)
3. Optional: Standalone HTML export of notebook with all outputs

Collaboration Policy

• You may discuss high-level approaches with classmates
• You may use GenAI assistance (ChatGPT, Claude, GitHub Copilot)
  • Must document what assistance you used and how
  • Must understand and be able to explain all code
• All analysis, writing, and insights must be your own
• Cite any external code or ideas used

Grading Rubric (100 points total)

Part 1: Implementation (40 points)

• Full credit requires all methods working with reasonable quality
• Partial credit for methods with minor issues or limited analysis
• Bonus points (up to +5) for additional methods or particularly elegant implementations

Part 2: Evaluation and Analysis (30 points)

• Quality of analysis matters more than quantity
• Must go beyond surface-level observations
• Statistical significance testing required for comparisons

Part 3: Visualization (15 points)

• Visualizations must be publication-quality
• Interactivity should enhance understanding
• Captions and explanations required
• Bonus points (up to +3) for particularly creative or insightful visualizations

Part 4: Cognitive Science Connection (10 points)

• Must engage seriously with cognitive science literature
• Superficial treatment will not receive full credit
• Depth and nuance valued over length

Part 5: Advanced Extensions (5 points)

• Choose extension that interests you
• Quality over quantity
• Bonus points (up to +5) for multiple high-quality extensions

Overall Quality (Holistic Assessment)

Maximum Total: 110 points (100 base + 10 possible bonus)

Resources and References

Key Papers (from Syllabus Weeks 3-4)

• Deerwester et al. (1990). Indexing by Latent Semantic Analysis
• Blei, Ng, | Jordan (2003). Latent Dirichlet Allocation
• Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space
• Pennington, Socher, | Manning (2014). GloVe: Global Vectors for Word Representation

• Bojanowski et al. (2017). Enriching Word Vectors with Subword Information
• Devlin et al. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Radford et al. (2019). Language Models are Unsupervised Multitask Learners
• Reimers | Gurevych (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

• Angelov (2020). Top2Vec: Distributed Representations of Topics
• Grootendorst (2022). BERTopic: Neural Topic Modeling with a Class-based TF-IDF Procedure

• van der Maaten | Hinton (2008). Visualizing Data using t-SNE
• McInnes, Healy, | Melville (2018). UMAP: Uniform Manifold Approximation and Projection

• Lenci (2008). Distributional semantics in linguistic and cognitive research
• Landauer | Dumais (1997). A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition

Tutorials and Documentation

• Scikit-learn Clustering
• Gensim Word Embeddings
• Hugging Face Transformers
• Sentence Transformers
• BERTopic Documentation
• Plotly Python
• UMAP Documentation

• Jay Alammar's Illustrated Word2Vec
• Jay Alammar's Illustrated BERT
• Topic Modeling with BERTopic (walkthrough)
• Understanding UMAP

Datasets for Human Judgments

• SimLex-999 - Semantic similarity ratings
• WordSim-353 - Word similarity dataset
• MEN Dataset - Semantic relatedness

Tips and Best Practices

Computational Efficiency

• Start with a subset (e.g., 10K articles) for development
• Once code works, scale to full 250K dataset
• Cache embeddings to avoid recomputation
• Use batch processing for transformer models

• Request GPU runtime in Colab (Runtime → Change runtime type)
• Monitor GPU memory usage with nvidia-smi
• Use mixed precision (fp16) for large models
• Clear cache between models: torch.cuda.empty_cache()

• Budget ~30 minutes per embedding method for full dataset
• Topic models (BERTopic, Top2Vec) are slowest (1-2 hours)
• Clustering is fast (<5 minutes for most methods)
• Visualization can be slow for 250K points; consider downsampling

Implementation Tips

• Transformers have max length (512 for BERT, 1024 for GPT-2)
• Strategies:
  • Truncate to first N tokens (simple but loses information)
  • Sliding window with averaging (more complete but slower)
  • Hierarchical: split document, embed chunks, aggregate
  • Use models with longer context (Longformer, BigBird)

• Simple mean: np.mean(word_vectors, axis=0)
• TF-IDF weighted: weight by term importance
• Max pooling: np.max(word_vectors, axis=0)
• Try multiple strategies and compare

• For clustering: use elbow method, silhouette scores
• For UMAP: nneighbors=15, mindist=0.1 are good defaults
• For t-SNE: perplexity=30-50 for large datasets
• Document your choices and justify them

Analysis Tips

• Don't just report one number - use confidence intervals
• Compare methods with statistical tests (paired t-test, Wilcoxon)
• Use bootstrap resampling for uncertainty estimates
• Consider multiple random initializations for clustering

• Use colorblind-friendly palettes (viridis, plasma, colorbrewer)
• Label axes and include legends
• Make plots large enough to read
• Interactive > static for exploratory analysis
• Include captions explaining what to look for

• Look at actual examples, not just metrics
• Sample from best AND worst clusters
• Find interesting edge cases
• Use LLMs to help interpret, but verify their interpretations

Common Pitfalls to Avoid

• Forget to normalize embeddings before clustering (for cosine similarity)
• Use too many clusters (overfitting) or too few (underfitting)
• Trust metrics blindly - always inspect examples
• Cherry-pick results that fit expectations
• Plagiarize or use GenAI output without understanding

• Start simple, then add complexity
• Validate assumptions with examples
• Compare multiple approaches
• Document limitations and failures
• Be honest about what works and what doesn't

Using GenAI Effectively

• Explaining error messages
• Suggesting library functions for specific tasks
• Helping debug code
• Generating boilerplate code
• Explaining concepts from papers

• Generating analysis without understanding
• Writing interpretation sections wholesale
• Implementing methods you don't understand
• Avoiding learning the underlying concepts

Expected Timeline

This is a 2-week intensive assignment (Weeks 3-4 of the course). With GenAI assistance and focused implementation, here's a suggested timeline:

Week 1: Implementation of All Methods, Initial Clustering

• Set up environment, download dataset
• Implement all classical methods (LSA, LDA)
• Implement all static embeddings (Word2Vec, GloVe, FastText)
• Implement contextualized embeddings (BERT, GPT-2)
• Implement modern embeddings (Sentence-BERT, Llama)
• Implement topic models (BERTopic, Top2Vec)
• Generate embeddings for full dataset
• Apply clustering algorithms (K-Means, Hierarchical, DBSCAN/HDBSCAN)
• Compute initial quantitative metrics

Week 2: Analysis, Visualization, Cognitive Science Connection, and Extensions

• Perform qualitative analysis and error analysis
• Cross-method comparison and interpretation
• Create multi-level clustering hierarchies
• Create interactive Plotly visualizations (3D scatter, 2D hexbin, cluster comparisons)
• Implement dimensionality reduction (t-SNE and UMAP)
• Write cognitive science connection and theoretical analysis
• Implement advanced extension (Option A, B, or C)
• Polish all outputs and finalize documentation

Frequently Asked Questions

Q: Do I really need to implement all 10+ methods? A: Yes. The comparison is the core of the assignment. However, if you have significant technical difficulties with one method, document the issue and move on.

Q: Can I use a smaller subset of the data? A: For development, yes. For final submission, use the full 250K articles (or justify why you used fewer).

Q: How do I handle out-of-memory errors? A: Use batch processing, reduce batch size, use smaller models, or process in chunks. Ask for help if stuck.

Q: Can I use different Wikipedia data? A: Prefer the provided dataset for comparability, but you can use different data if you have a good reason (must justify).

Q: How much analysis is enough? A: Quality > quantity. Deep analysis of a few interesting findings beats superficial treatment of many.

Q: Can I work with a partner? A: Check course policy. Generally, collaboration is allowed but each person must submit their own work.

Q: How important are the visualizations? A: Very important. They're worth 15 points and central to understanding the results. Invest time here.

Q: What if my results are "bad" (low metrics, unclear clusters)? A: Document what you found! Negative results are still results. Analyze why and what it means.

Q: Can I use commercial APIs (OpenAI, Anthropic)? A: Prefer open-source models, but if you have credits, you can use APIs for comparison.

Getting Help

• Office Hours: Best place for debugging and conceptual questions
• Course Forum: Great for sharing tips and common issues
• GenAI: Useful for coding help, but understand before using
• Library Documentation: Always check official docs first
• Papers: When in doubt, read the original paper

Learning Goals: What You'll Take Away

By completing this assignment, you will:

1. Technical Skills
   • Mastery of diverse embedding techniques
   • Experience with unsupervised learning and clustering
   • Expertise in evaluation metrics and analysis
   • Ability to create publication-quality visualizations
2. Conceptual Understanding
   • Deep understanding of distributional semantics
   • Knowledge of trade-offs between different approaches
   • Appreciation for the evolution of NLP methods
   • Critical thinking about what "meaning" means computationally
3. Research Skills
   • Ability to design and conduct comparative experiments
   • Statistical rigor in evaluation
   • Clear communication of findings
   • Connection between theory and practice
4. Practical Knowledge
   • Experience working with large-scale NLP datasets
   • Understanding of computational constraints
   • Ability to debug and troubleshoot ML pipelines
   • Portfolio-worthy project for job applications

Final Words

This assignment is designed to be challenging, extensive, and deeply educational. It's not meant to be completed in a weekend. Take your time, explore the methods, think deeply about the results, and enjoy the process of discovering how machines represent meaning.

The goal isn't just to get high metrics or pretty plots - it's to develop a sophisticated understanding of how different computational approaches capture semantic information, and to think critically about what these methods reveal (and obscure) about language and meaning.

Don't hesitate to be creative, try additional ideas, and follow interesting threads you discover. The best projects will show intellectual curiosity, rigorous analysis, and genuine insight.

Good luck, and enjoy exploring the semantic space!