Figurative Speech and GNN

The human language is a rich tapestry, where literal meanings often serve as mere threads, and figurative speech weaves intricate patterns of deeper understanding. From the piercing "dagger of a stare" to the "river of emotions" that runs through us, metaphors and similes are the very essence of expressive communication. Developing an AI model capable of understanding such nuances is a frontier of natural language processing (NLP). 

The challenge of figurative speech lies in its non-literal nature. Traditional NLP models, which often rely on word embeddings and sequential processing, can struggle to capture the underlying relationships between disparate concepts. For instance, in the phrase "time is a thief," the model must understand the shared attributes of "time" and "thief" – not in a literal sense, but in their shared capacity to take something valuable without consent. A graph-based approach, where words are nodes and their relationships are edges, is a natural fit for this problem.

A proposed model begins with a Relational GNN architecture. RGNNs are designed to handle graphs with different types of edges, making them ideal for representing the complex relationships within a sentence. We can define several edge types: syntactic dependencies (e.g., subject-verb, verb-object), semantic relationships (e.g., synonyms, hypernyms), and even co-occurrence links. This allows the model to learn distinct representations for each type of connection, differentiating between a grammatical link and a conceptual one. For example, the RGNN can be trained to recognize that the "is" in "time is a thief" is a metaphorical link, not a simple copula.

However, a pure RGNN can treat all relationships equally, which is not always the case in figurative speech. Some connections are far more crucial than others. This is where the Graph Attention Network (GAT) convolution comes into play. GATs allow the model to learn the importance, or attention score, of each neighbor's contribution to a node's representation. When a GAT layer is applied, it will learn to assign higher attention to the most salient connections. In our example, the GAT would likely assign a high attention score to the "is" link connecting "time" and "thief," recognizing its pivotal role in establishing the metaphor. It would also give attention to the shared attributes between "thief" (stealing, stealth) and "time" (passing, elapsing), which are crucial for the metaphor's interpretation.

The implementation of this hybrid model in PyTorch would involve building a custom nn.Module. The core would be a message-passing framework where nodes aggregate information from their neighbors. The RGNN layer would first compute node updates based on the different edge types. This would be followed by a GAT layer, which would re-weigh these aggregated features based on their learned attention scores. Finally, the model would output a classification (e.g., metaphorical vs. literal) or a vector representation that captures the figurative meaning. Training would involve a dataset of annotated sentences, where the model learns to identify and interpret the hidden relationships that define figurative language.

By combining the structural power of Relational GNNs with the nuanced feature-weighting of Graph Attention Networks, we can build a robust model for understanding figurative speech. This hybrid GNN, implemented in PyTorch, represents a significant step forward in NLP, moving beyond the literal to truly grasp the creative and expressive power of human language. It is a model that doesn't just read the words, but listens to the metaphors they whisper.

Writer

In an increasingly crowded market of AI writing tools, Writer positions itself as a sophisticated platform for teams and enterprises. Its marketing highlights brand consistency, governance, and a proprietary language model. However, a closer look reveals that this enterprise-first approach results in a platform with a deeply flawed value proposition, especially for the individual writers it claims to serve. For anyone outside a large corporate structure, Writer’s expensive price tag and subpar functionality make it a perplexing and ultimately disappointing tool.

The most glaring issue is the fundamental disconnect between the platform’s features and the needs of a typical user. Writer is built for large-scale operations: establishing comprehensive brand style guides, enforcing corporate terminology, and monitoring content production across entire teams. While these features may be valuable to a global marketing department, they are entirely superfluous for a freelance writer, a small business owner, or a student. The platform’s core functionality, which should be its greatest asset, is a suite of tools designed to solve problems the average user simply does not have. This creates a steep and unnecessary learning curve, forcing a user to navigate a labyrinth of corporate features to access the basic writing assistance they desire.

This focus on enterprise solutions also leads to a concerning lack of quality in its foundational AI capabilities. While the platform boasts a proprietary model, its performance often fails to live up to the promise. User accounts frequently describe an inconsistent AI that generates content that then immediately violates the user's pre-set writing rules, requiring significant manual editing. This creates a paradoxical situation where a tool designed to save time ends up creating more work. Furthermore, the generative AI features come with strict word limits on even the paid Team plan, a significant hurdle for any prolific writer and a clear sign that the platform is not built for high-volume content creation.

Perhaps the most damning critique, however, lies in the platform’s AI detection tool. In an era where authenticity and originality are paramount, Writer offers a free AI detector that independent tests have found to be alarmingly inaccurate. It has been shown to consistently misclassify AI-generated text from other popular models as human-written, sometimes failing to detect a single instance of AI content in a series of tests. This inadequacy is not merely a minor bug; it is a fundamental breakdown of a core feature. A platform that cannot reliably identify AI-generated text undermines its own credibility and the very trust it asks its users to place in its technology.

Ultimately, Writer suffers from an identity crisis. It markets itself broadly to all writers but delivers a product that is only truly suited for a niche, corporate audience. Its critical failures in core writing assistance and its deeply flawed AI detection tool make it a difficult platform to recommend. While its enterprise-grade features may appeal to companies with specific governance needs, its high cost and compromised performance for the individual writer make it a terrible value and an uninspired alternative to more accessible and effective tools on the market.

Goodnotes

Goodnotes has long been heralded as a good choice for note-taking, especially for students and professionals using tablets with styluses. Its reputation is built on a seamless handwriting experience and robust PDF annotation features. However, for many users, this polished facade hides a tool that is often more of a hindrance than a help. From its clumsy user interface to its questionable AI features and a subscription model that feels exploitative, Goodnotes presents a disorganized and often counterproductive experience.

One of the most immediate frustrations for a long-time user is the app’s user experience (UX) and overall design. Goodnotes often feels rigid and unintuitive, with an interface that prioritizes aesthetics over functionality. Simple tasks like rearranging documents or folders become tedious due to a restrictive alphabetical sorting system that doesn't allow for manual placement. The toolbar, with its limited customization and a tendency to shuffle with every update, disrupts muscle memory and forces users to constantly re-adapt. This is particularly annoying for a tool meant to facilitate a fluid and personal workflow. For an app centered on creativity and organization, this lack of control over the workspace is a significant flaw.

Beyond the interface, the app’s smart features and AI-driven tools often miss the mark. While features like handwritten spellcheck are touted as major advancements, they can often be more of a nuisance than a convenience. The autocorrect feature has been known to flag correctly spelled words or misunderstand cursive handwriting, leading to frustrating and often incorrect suggestions. This overcorrection can break the flow of thought and force a user to spend time correcting the tool itself rather than focusing on the content of their notes. In a note-taking application where accuracy and responsiveness are paramount, this kind of flawed functionality undermines the entire user experience.

Moreover, the business model of Goodnotes has become a point of major contention, particularly with the transition from a one-time purchase to a subscription-based service. The move to a paid subscription, especially for features that many felt should have been included in the original purchase, has led to a feeling of being nickel-and-dimed. This monetization strategy, which forces users to pay for what were once standard features, creates a sense of dependency and casts a shadow of distrust over the company’s long-term commitment to its user base. Ultimately, what was once a straightforward and reliable tool has become a frustrating and expensive proposition, riddled with design flaws and functional shortcomings that make it a difficult choice to recommend in an increasingly competitive market.

Grammarly

In the modern age of digital communication, tools designed to improve our writing have become ubiquitous. Grammarly, with its aggressive marketing and seamless integrations, is arguably the most well-known of these. Marketed as an indispensable assistant, it promises to elevate one’s writing from good to great. However, for many writers, students, and professionals, this omnipresent tool has become less of a helpful partner and more of a digital micromanager, burdened with intrusive features, questionable suggestions, and a fundamental misunderstanding of the writing process.

One of the most persistent criticisms of Grammarly is its intrusive and often annoying nature. The tool's browser extensions and desktop applications embed themselves in nearly every text field, from email clients to social media posts. While this widespread integration is a key selling point, it also means that Grammarly is constantly on, flagging every sentence, every word, and every stylistic choice with a persistent, sometimes jarring, visual feedback loop. This constant interruption can be a significant impediment to the creative process, forcing a writer to constantly second-guess their choices before a thought is even fully formed. The flurry of red and blue underlines, coupled with pop-up suggestions, can turn the act of drafting into a tedious and fragmented editing session.

Beyond its disruptive presence, the quality and accuracy of Grammarly’s suggestions are often a major point of contention. The tool is programmed to adhere to a rigid set of grammatical rules and stylistic conventions, which can lead to overcorrection and a lack of contextual understanding. It frequently struggles with complex sentence structures, technical jargon, or unique stylistic flourishes, often offering suggestions that would strip a sentence of its intended meaning or voice. For a writer with a distinct style, Grammarly’s push for a standardized, clear tone can feel like an attempt to flatten their personality and homogenize their work. This is particularly problematic in creative or academic writing, where nuance and stylistic choice are paramount. The tool's "corrections" can sometimes do more harm than good, creating a false sense of security and potentially leading to a blander, less effective final product.

Furthermore, the business model of Grammarly itself can feel manipulative. While the free version offers basic checks, the most advanced and supposedly valuable features—such as tone adjustments, clarity enhancements, and full sentence rewrites—are locked behind a paywall. This constant highlighting of premium suggestions serves as a relentless form of marketing, suggesting that one's writing is fundamentally flawed without the paid version. This creates an environment of dependency and inadequacy, rather than one of empowerment and genuine improvement. Ultimately, Grammarly's blend of intrusive design, flawed suggestions, and commercial pressures transforms it from a potentially useful writing aid into a burdensome and often counterproductive presence in the digital writing landscape.

LLM Research Papers

LLM Research Papers

Topic Modeling

The discipline of topic modeling, a cornerstone of Natural Language Processing, is undergoing a profound transformation in 2025, propelled by the relentless pace of AI innovation. Moving beyond traditional statistical approaches, the cutting edge of research is now deeply intertwined with large language models (LLMs), dynamic analysis, and sophisticated hybrid methodologies, all aimed at extracting more nuanced, coherent, and actionable insights from the ever-expanding universe of unstructured text. The trends observed today are not merely incremental improvements but foundational shifts shaping the future of textual data analysis.

A defining characteristic of contemporary topic modeling research is the deep integration of Large Language Models (LLMs). While models like BERTopic have already demonstrated the power of transformer-based embeddings for semantic understanding, the current focus extends to leveraging LLMs for more intricate stages of the pipeline. This includes utilizing LLMs to refine the very representations of topics, generating highly descriptive and human-interpretable labels that capture subtle thematic distinctions. Furthermore, LLMs are being employed for automatic summarization of documents within identified topics, providing concise overviews that accelerate human comprehension. This LLM-assisted topic modeling paradigm aims to bridge the gap between raw data and actionable intelligence, enhancing both the semantic depth and the interpretability of discovered themes.

The ability to track Dynamic Topic Evolution is another critical frontier. In a world of continuous data streams—from social media conversations to evolving scientific literature and financial reports—understanding how themes emerge, shift, and dissipate over time is paramount. Research in 2025 is yielding advanced systems, such as "DTECT: Dynamic Topic Explorer & Context Tracker," designed to provide end-to-end workflows for temporal topic analysis. These systems integrate LLM-driven labeling, sophisticated trend analysis, and interactive visualizations, moving beyond static snapshots to offer a fluid, adaptive understanding of textual dynamics. This enables real-time monitoring of trends and proactive decision-making in diverse applications.

Hybrid approaches are also gaining significant traction, acknowledging that a one-size-fits-all solution rarely exists in NLP. Researchers are increasingly combining the strengths of established probabilistic models (like LDA) with the semantic power of modern embedding-based techniques. For instance, some methodologies propose using LLM embeddings for initial document representation, followed by more traditional clustering or probabilistic modeling for enhanced interpretability, particularly for longer, more coherent texts where the statistical underpinnings of models like LDA can still offer unique insights into word distributions. This flexibility allows practitioners to tailor their approach to the specific characteristics of their data—whether it's noisy, short-form content or structured, extensive documents—optimizing for both accuracy and interpretability.

Beyond unsupervised topic discovery, the advancements in LLMs are profoundly impacting thematic classification, topic classification, and topic categorization. These related tasks, which involve assigning pre-defined or inferred themes/categories to documents, are benefiting immensely from the contextual understanding and few-shot learning capabilities of LLMs. Instead of relying solely on traditional supervised learning with large labeled datasets, researchers are exploring:

• Zero-shot and Few-shot Classification: LLMs can classify text into categories they haven't been explicitly trained on, or with very few examples, by leveraging their vast pre-trained knowledge. This is revolutionizing how quickly new classification systems can be deployed for emerging themes.

• Prompt Engineering for Categorization: Crafting effective prompts for LLMs allows for highly flexible and adaptable thematic categorization, enabling users to define categories on the fly based on their specific analytical needs.

• Automated Coding for Thematic Analysis: LLMs are being used to assist in qualitative research by automating the coding of text data into themes, significantly reducing the manual effort involved in thematic analysis. While human oversight remains crucial for nuanced interpretation, LLMs can efficiently process large volumes of qualitative data.

• Dynamic Thematic Classification: Just as topics evolve, so do the relevance and definition of thematic categories. Future research is focused on systems that can adapt classification models to changing themes and language use over time, ensuring that categorization remains accurate and relevant in dynamic environments.

Looking beyond 2025, research is delving into the optimization and generalization of neural topic models. Efforts are focused on improving the robustness and performance of these complex architectures, with techniques like "Sharpness-Aware Minimization for Topic Models with High-Quality Document Representations" being explored to enhance model stability and predictive power. Emerging methodologies such as Prompt Topic Models (PTM) are leveraging prompt learning to overcome inherent structural limitations of older models, aiming to boost efficiency and adaptability in topic discovery. The future promises even more sophisticated models capable of handling multimodal data, incorporating visual or auditory cues alongside text to derive richer, more holistic insights, further blurring the lines between unsupervised topic modeling and supervised thematic classification.

Topic modeling and its related classification tasks in 2025 and beyond are characterized by a drive towards greater semantic depth, temporal awareness, and practical applicability. The emphasis is on creating intelligent, adaptable, and interpretable models that can seamlessly integrate into broader AI and machine learning workflows, providing richer, more dynamic insights from the ever-growing deluge of textual information. This evolving landscape promises to unlock unprecedented capabilities for understanding and navigating complex information environments.

BERTopic

The explosion of unstructured text data, from customer reviews to scientific literature, presents both a challenge and an opportunity for extracting meaningful insights. Traditional topic modeling techniques, while foundational, often grapple with the nuances of language and scalability. Enter BERTopic, a cutting-edge Python library that has revolutionized the field by combining the power of transformer models with sophisticated clustering and topic representation methods. It offers a compelling solution for automatically discovering coherent themes within vast text corpora.

At its core, BERTopic operates through a multi-step pipeline designed for semantic understanding. It begins by converting documents into dense, contextualized numerical representations (embeddings) using pre-trained transformer models like BERT or Sentence-Transformers. These embeddings capture the semantic relationships between words and sentences, going beyond simple word counts. Next, it employs a density-based clustering algorithm, typically HDBSCAN, to group semantically similar documents into clusters, which represent the underlying topics. A significant advantage here is BERTopic's ability to automatically determine the optimal number of topics and identify outliers, eliminating the need for manual tuning. Finally, to represent these clusters as interpretable topics, BERTopic utilizes a unique "class-based TF-IDF" (c-TF-IDF) approach, which highlights words that are highly descriptive of a particular topic within its cluster, rather than just frequent words overall.

Implementing BERTopic is remarkably straightforward. The simplicity belies its powerful capabilities. Users can then explore topics, visualize their relationships, and even merge or reduce topics to achieve a desired level of granularity. BERTopic's modular design is a key strength, allowing users to swap out default components (e.g., using a different embedding model, a different clustering algorithm like K-Means, or custom tokenizers) to fine-tune performance for specific datasets or research questions. It also supports advanced features like dynamic topic modeling (tracking topic evolution over time), guided topic modeling (using seed words), and even integration with Large Language Models for enhanced topic labeling.

Despite its many strengths, BERTopic is not without its drawbacks and limitations. The primary concern is computational resource intensity. Generating high-quality transformer embeddings can be memory and computationally expensive, especially for very large datasets or when using larger embedding models. While it can run locally, a machine with substantial RAM and ideally a GPU is recommended for efficient processing. This also means that for extremely massive datasets, cloud-based computing resources might be necessary. Another limitation, inherent to embedding-based models, is that the process can feel somewhat like a "black box" compared to the probabilistic interpretability of LDA, where word-topic distributions are explicitly modeled. Furthermore, while it handles short texts well, for extremely long documents, the underlying transformer models might have token limits, requiring chunking or summarization.

While BERTopic is a powerful tool for semantic topic discovery, it might not always be the optimal choice. For very small datasets where computational resources are severely limited, or when strict probabilistic assumptions about word distributions are paramount, simpler models like LDA or NMF might still be considered. However, for most modern NLP tasks involving unstructured text, especially when semantic understanding, automatic topic discovery, and interpretability are crucial, BERTopic stands out as a leading and highly versatile library. Its continuous development and integration of new AI advancements further solidify its position as a go-to solution for unlocking hidden themes in data.

Affective Computing and Chatbots

The evolution of chatbots from simple rule-based systems to sophisticated AI-driven conversational agents has opened new frontiers in customer service. A particularly transformative advancement is the integration of affective computing, enabling chatbots to detect, interpret, and respond to human emotions. This capability is crucial for effectively resolving customer discontent and transforming potentially negative interactions into positive experiences, thereby enhancing customer satisfaction and loyalty.

Affective computing in chatbots typically involves several layers of analysis. The initial step is emotion detection, which can be achieved through various modalities. Text-based analysis, leveraging Natural Language Processing (NLP) and sentiment analysis, can identify emotional cues from word choice, tone indicators (e.g., excessive capitalization, exclamation marks), and specific phrases. Voice-based chatbots can further analyze prosodic features such as pitch, volume, speech rate, and intonation patterns to infer emotional states like frustration, anger, or urgency. Some advanced systems might even integrate visual cues (if video interaction is involved) like facial expressions.

Implementation Details and Open-Source Tools:

Implementing affective computing in a chatbot involves a pipeline of data processing and model integration. For text-based emotion detection, the process typically starts with collecting conversational data, which is then annotated for emotional states. This labeled data trains machine learning models. Open-source NLP libraries like NLTK, SpaCy, or Hugging Face Transformers (specifically models fine-tuned for sentiment analysis or emotion classification like distilbert-base-uncased-finetuned-sst2-english or cardiffnlp/twitter-roberta-base-emotion) are invaluable. These tools can process incoming text, extract features, and predict emotional labels.

For voice-based emotion detection, audio streams are processed to extract acoustic features (e.g., Mel-frequency cepstral coefficients - MFCCs, pitch contours, energy levels). Open-source toolkits like librosa (for feature extraction) and OpenSMILE (for a wider range of speech features) are commonly used. These features then feed into machine learning models (e.g., SVMs, deep neural networks) trained on speech emotion datasets. The integration of these detection modules with the chatbot's core dialogue management system is crucial, allowing the chatbot to receive emotional signals alongside textual input.

The advent of Large Language Models (LLMs) further revolutionizes affective computing in chatbots. LLMs, such as those accessible via the Gemini API, can process and understand complex human language with remarkable nuance. They can be fine-tuned or prompted to not only detect subtle emotional cues but also to generate empathetic and contextually appropriate responses. Instead of relying solely on pre-defined emotional labels, an LLM can infer underlying sentiment, identify the root cause of discontent, and formulate more human-like, nuanced acknowledgments and de-escalation strategies. For instance, an LLM could analyze a customer's lengthy, frustrated message and summarize their core grievance while simultaneously expressing understanding, making the interaction feel less robotic. They can also generate adaptive communication styles more naturally, adjusting vocabulary, sentence structure, and formality based on the perceived emotional state and the specific context of the conversation.

Once an emotion, particularly discontent or unhappiness, is detected, the chatbot's response strategy shifts from purely informational to emotionally intelligent. There are several distinct ways an affectively aware chatbot can work to resolve a discontented customer:

1. Empathy and Acknowledgment: The immediate and most critical step is to acknowledge the customer's emotional state. Instead of a generic "How can I help you?", an affective chatbot might respond with, "I understand you're feeling frustrated right now," or "I hear your concern, and I'm here to help." This validation of feelings can significantly de-escalate tension and make the customer feel heard and understood, building a foundation of trust.

2. Adaptive Communication Style: The chatbot can dynamically adjust its communication style based on the detected emotion. For an angry customer, it might adopt a calmer, more formal, and direct tone, focusing on problem-solving. For a confused or overwhelmed customer, it might use simpler language, offer step-by-step guidance, and provide more frequent confirmations. This adaptability prevents further irritation and guides the conversation more effectively.

3. Prioritized Problem Resolution: When discontent is detected, the chatbot can prioritize the customer's issue. If it's a simple query, it can expedite the solution. If the issue is complex or requires human intervention, the chatbot can intelligently route the customer to the most appropriate human agent, providing the agent with a summary of the conversation and the detected emotional state. This minimizes repetition for the customer and allows the human agent to approach the interaction with pre-existing context and empathy.

4. Proactive Offerings and Solutions: Based on the emotional context and the nature of the query, the chatbot can proactively offer solutions or compensations. For example, if a customer expresses frustration about a service interruption, the chatbot might not only explain the issue but also immediately offer a small credit or a link to an FAQ that addresses common concerns related to the outage. This preemptive problem-solving can turn a negative experience into a surprisingly positive one.

5. Feedback Loop for Improvement: Affective computing also provides valuable data for continuous improvement. By analyzing patterns of discontent, the system can identify common pain points, refine its emotional detection algorithms, and improve its response strategies over time. This iterative learning ensures that the chatbot becomes increasingly adept at handling difficult customer interactions.

Applying affective computing to chatbots moves them beyond mere utility to genuine customer engagement. By enabling chatbots to understand and respond to emotions, businesses can create more empathetic, efficient, and ultimately, more satisfying customer service experiences, transforming moments of discontent into opportunities for building stronger relationships.

Thing vs Concept

The distinction between a "thing" and a "concept" lies at the heart of how we understand and categorize the world. A "thing" typically refers to a concrete, tangible entity that exists in reality, possessing specific properties and occupying space and time. A tree, a car, a human being – these are things. A "concept," on the other hand, is an abstract idea, a mental construct, or a generalization derived from observed things. "Forest," "transportation," "humanity" – these are concepts. The philosophy underpinning this difference is crucial when designing taxonomies and ontologies, which are structured systems for organizing knowledge.

In the realm of knowledge representation, particularly in domains like data science, artificial intelligence, and information management, deciding when to represent something as a concrete "thing" versus an abstract "concept" is not merely an academic exercise; it has profound practical implications. Taxonomies, which are hierarchical classifications, often start with concrete things and group them under broader concepts. For instance, a "Golden Retriever" (a thing, a specific breed) is classified under "Dog" (a more general concept), which falls under "Canine" (an even broader concept).

Ontologies, which provide a richer representation of knowledge by defining classes, properties, and relationships, demand an even more nuanced approach. Here, the interplay between "things" and "concepts" becomes vital. When constructing an ontology, one must determine whether an entity should be modeled as an individual instance (a "thing") or a class/category (a "concept"). For example, "my car" is a specific instance of a "Car," which is a class. The class "Car" is a concept, while "my car" is a thing.

It makes sense to use abstractions (concepts) when:

1. Generalization is needed: To group similar things, allowing for easier reasoning and querying across diverse instances. For example, treating "Sedan," "SUV," and "Hatchback" as specific types under the abstract concept of "Car."
2. Focus is on properties and relationships common to a group: If you want to define that all "Books" have "Authors" and "Titles," you define these properties on the concept "Book," not on every individual book.
3. Scalability is a concern: Storing properties for every individual thing can be inefficient. Abstractions allow for a more compact and manageable knowledge base.
4. Semantic clarity is paramount: Concepts provide the vocabulary and framework for understanding a domain, ensuring consistency in meaning.

Conversely, it is right to use concrete "things" (instances) when:

1. Specificity is essential: When you need to refer to a particular entity with unique attributes, like "the Eiffel Tower" or "the specific transaction ID 12345."
2. Tracking individual states or histories: If "my car" needs to track its mileage, service history, or current location, it must be represented as a distinct thing.
3. Events or actions involving specific entities: "John bought a book" involves specific individuals ("John," a type of "person") and a specific item ("a book," an instance of the concept "Book").

The "rightness" of using an abstraction versus a concrete instance depends on the granularity required by the system and the questions it needs to answer. Over-abstracting can lead to a loss of valuable detail, making it impossible to query specific instances. Under-abstracting can lead to a bloated, unmanageable knowledge base that struggles with generalization. The challenge in taxonomy and ontology is to find the optimal balance, building robust models that allow for both generalized reasoning and detailed instance tracking, ensuring the structured knowledge reflects the complex interplay between the abstract and the tangible in our world.

Vector Search and SKOS

The digital age is characterized by an explosion of information, demanding sophisticated methods for organization, retrieval, and understanding. In this landscape, two distinct yet potentially complementary approaches have emerged: vector search, rooted in modern machine learning, and SKOS (Simple Knowledge Organization System), a standard from the Semantic Web domain. While one leverages numerical representations for semantic similarity and the other focuses on structured vocabularies, a closer look reveals how they can enhance each other's capabilities in managing complex knowledge.

Vector search, a paradigm shift in information retrieval, moves beyond traditional keyword matching to understand the semantic meaning of data. At its core, vector search transforms various forms of unstructured data – whether text, images, audio, or even complex concepts – into high-dimensional numerical representations called "embeddings." These embeddings are vectors in a multi-dimensional space, where the distance and direction between vectors reflect the semantic similarity of the original data points. Machine learning models, particularly large language models (LLMs) for text, are trained to generate these embeddings, ensuring that semantically similar items are positioned closer together in this vector space.

When a query is made, it too is converted into an embedding. The search then becomes a mathematical problem of finding the "nearest neighbors" in the vector space using distance metrics like cosine similarity or Euclidean distance. This approach enables highly relevant results even when exact keywords are not present, powering applications like semantic search, recommendation engines (e.g., suggesting similar products or content), anomaly detection, and Retrieval Augmented Generation (RAG) systems that ground LLM responses in specific data.

In contrast to the fluidity of vector embeddings, SKOS (Simple Knowledge Organization System) is a World Wide Web Consortium (W3C) recommendation designed to represent and publish knowledge organization systems (KOS) like thesauri, taxonomies, classification schemes, and subject heading systems on the Semantic Web. SKOS provides a formal model for concepts and their relationships, using the Resource Description Framework (RDF) to make these structures machine-readable and interoperable across different applications and domains.

The fundamental building block in SKOS is skos:Concept, which can have preferred labels (skos:prefLabel), alternative labels (skos:altLabel, for synonyms or acronyms), and hidden labels (skos:hiddenLabel). More importantly, SKOS defines standard properties to express semantic relationships between concepts: hierarchical relationships (skos:broader, skos:narrower) and associative relationships (skos:related). It also provides mapping properties (skos:exactMatch, skos:closeMatch, etc.) to link concepts across different schemes. SKOS is widely used by libraries, museums, government agencies, and other institutions to standardize vocabularies, simplify knowledge management, and enhance data interoperability.

While vector search excels at discovering implicit semantic connections and SKOS provides explicit, structured relationships, their combination offers a powerful synergy. Vector search is adept at finding "similar enough" content, but it can sometimes lack precision or struggle with very specific, nuanced relationships that are explicitly defined in a knowledge organization system. This is where SKOS can provide valuable context and constraints.

For instance, a vector search might retrieve documents broadly related to "fruit." However, if a SKOS vocabulary explicitly defines "apple" as a skos:narrower concept of "fruit" and "Granny Smith" as a skos:narrower concept of "apple," this structured knowledge can be used to refine vector search results. Embeddings of SKOS concepts themselves can be created and used in vector databases to find semantically related concepts or to augment search queries with synonyms or broader/narrower terms defined in the vocabulary.

Conversely, vector embeddings can help maintain and enrich SKOS vocabularies. By analyzing text corpora and identifying terms that frequently appear in similar contexts, new skos:related concepts could be suggested for human review. Vector search could also assist in identifying potential skos:altLabel candidates (synonyms) or uncovering implicit hierarchical relationships that could be formalized in the SKOS structure.

In essence, vector search offers a flexible, data-driven approach to semantic understanding, while SKOS provides a robust, human-curated framework for explicit knowledge organization. Integrating these two powerful tools allows for more intelligent, precise, and contextually rich information retrieval systems, bridging the gap between implicit semantic similarity and explicit knowledge structures in the ever-growing digital universe.

Fake News Detection Models

The pervasive spread of misinformation, often termed "fake news," poses a significant threat to informed public discourse and societal stability. In response, artificial intelligence research has accelerated, yielding sophisticated detection models that integrate diverse methodologies such as Graph Neural Networks (GNNs), knowledge graphs, deep learning, causal reasoning, and argumentation theory. As of mid-2025, the field is witnessing a paradigm shift towards more robust, interpretable, and adaptable solutions, particularly in the face of evolving adversarial tactics.

Graph Neural Networks (GNNs) have emerged as powerful tools for modeling the complex propagation patterns of information on social media. Unlike traditional text-based analysis, GNNs leverage the structural relationships between news articles, users, and their interactions. Models like Neighborhood-Order Learning Graph Attention Network (NOL-GAT), developed in early 2025, enhance detection accuracy by allowing each node (e.g., a news article or user) to learn its optimal neighborhood order, efficiently extracting critical information from both close and distant connections. This approach is particularly effective in identifying malicious dissemination patterns, which are often subtle and embedded within vast networks.

Knowledge graphs (KGs) play a crucial role in grounding fake news detection in verifiable facts. By organizing data into a structured network of entities and their relationships, KGs facilitate fact-checking by comparing claims within news content against trusted sources. Recent advancements in 2025 show KGs being integrated with Large Language Models (LLMs) to enable context-rich information retrieval and real-time decision-making, improving the ability to verify nuanced claims. This synergy allows models to not only identify factual inconsistencies but also to understand the semantic context in which those facts are presented.

Deep learning remains at the forefront of content-based fake news detection. Transformer-based architectures, such as BERT and its variants, continue to demonstrate superior performance in analyzing textual and multimodal data. As of early 2025, these models are increasingly being deployed in multimodal settings, integrating text, images, and even audio-visual cues to detect inconsistencies across different formats. Transfer learning and ensemble techniques further enhance their accuracy and adaptability, especially in low-resource languages, a key focus area in 2024-2025 research.

Causal reasoning represents a significant leap towards more explainable and robust detection. By identifying and mitigating spurious correlations that can mislead models, causal intervention techniques aim to achieve "deconfounded reasoning." For instance, a framework proposed in April 2025 for multimodal fake news detection explicitly models confounders arising from cross-modal interactions (e.g., misleading images with factual text). This allows the model to make decisions based on true causal links rather than coincidental associations, enhancing both accuracy and interpretability.

Argumentation theory offers a unique lens through which to analyze the logical structure and fallacies within news narratives. Models leveraging argumentation schemes, as seen in research from early 2025, can move beyond simple fact-checking to assess the validity of the reasoning presented. This involves identifying stereotypical patterns of argumentative reasoning and posing "critical questions" to challenge the validity of claims. This approach not only helps detect misinformation based on faulty logic but also provides explainable reasons for flagging content as suspicious, fostering greater user trust and understanding.

Looking beyond mid-2025, the landscape of fake news detection is continually evolving. A key trend is the development of robust models specifically designed to withstand adversarial attacks, where malicious actors deliberately craft content to bypass detection systems. Techniques like adversarial style augmentation, often leveraging LLMs to generate challenging prompts, are being explored to train detectors that are more resilient to subtle textual manipulations. Furthermore, the integration of Explainable AI (XAI) techniques, such as SHAP and LIME, will become increasingly prevalent to ensure transparency and build trust in these automated systems. The rise of hyper-realistic generative AI models also necessitates continuous innovation in detecting synthetic media and distinguishing AI-generated fake news from authentic content. The future of fake news detection lies in these hybrid, interpretable, and resilient models that can adapt to the ever-more sophisticated tactics of misinformation campaigns.

Best NLP Papers

Best NLP Papers

Speech Recognition and Processing

• Automatic Speech Recognition: A Survey
• End-to-End Speech Recognition
• Deep Speech Processing Techniques
• Deep Learning Techniques for Speech Processing
• Recent Advances in Speech Language Models
• Survey of Speech Recognition Based on Deep Learning
• Towards Controllable Speech Synthesis
• Survey of Deep Learning for Complex Speech Spectrograms
• Text to Speech Synthesis
• Deep learning-based expressive speech synthesis
• Transformers in Speech Processing: A Survey

Perplexity and Language Model Evaluation

In the realm of Natural Language Processing (NLP), perplexity stands as a fundamental metric for evaluating the performance of language models. At its core, perplexity quantifies how well a probability distribution or language model predicts a sample. A lower perplexity score indicates that the model is better at predicting the next word in a sequence, suggesting a more accurate and confident understanding of the underlying language patterns. This seemingly straightforward metric offers both significant advantages and notable drawbacks in the assessment of modern language models, particularly Large Language Models (LLMs).

The good aspects of perplexity are rooted in its mathematical elegance and interpretability. As a measure derived from cross-entropy, perplexity provides a quantitative means to compare different language models on a common ground. It reflects the average branching factor of the model's predictions: if a model has a perplexity of 10, it's akin to saying that, on average, the model is "confused" between 10 equally likely words at each step. This makes it a valuable tool for tracking progress during model training and development, allowing researchers to gauge improvements in a model's ability to capture linguistic regularities. For tasks like speech recognition or machine translation, where predicting the most probable sequence of words is paramount, perplexity can serve as a useful proxy for overall performance. Furthermore, it's a domain-agnostic metric, applicable to any language model regardless of its architecture or the specific language it processes, making it a versatile benchmark.

However, perplexity is also a double-edged sword, carrying significant bad aspects and limitations, especially in the context of human-centric applications and the nuanced outputs of LLMs. One major criticism is that perplexity does not directly correlate with human judgment of text quality. A model might achieve a low perplexity score by accurately predicting common phrases, yet still generate text that is bland, repetitive, or lacks creativity and coherence over longer passages. It prioritizes statistical likelihood over semantic richness or stylistic flair. For example, a model might have low perplexity on a factual dataset but fail to produce engaging or novel creative writing.

Moreover, perplexity is highly sensitive to the training data. If a model is trained on a specific domain, its perplexity on out-of-domain text will likely be very high, even if it performs well within its trained domain. This makes cross-domain comparisons challenging and can obscure a model's true generalization capabilities. It also doesn't account for the "correctness" or "truthfulness" of generated text, only its statistical probability within the learned distribution. In the era of generative AI, where models are expected to produce factual, safe, and unbiased content, perplexity alone is insufficient. It offers no insight into issues like hallucination, factual inaccuracies, or the presence of harmful biases embedded within the generated output.

While perplexity remains a valuable and foundational metric for internal model development and certain predictive tasks, its limitations become starkly apparent when evaluating the complex, nuanced, and often creative outputs of modern LLMs. It serves as a good indicator of a model's fluency and statistical understanding of language, but it falls short in capturing the qualitative aspects of human-like communication, such as creativity, factual accuracy, coherence, and ethical considerations. Therefore, for a holistic assessment of LLMs, perplexity must be complemented by a suite of other metrics, including human evaluation, task-specific performance measures, and robust ethical auditing.

LLM and Multimodal Data

The remarkable evolution of Large Language Models (LLMs) from text-centric powerhouses to sophisticated processors of multimodal data represents a frontier in artificial intelligence. This capability, allowing LLMs to interpret and generate content across various forms like images, audio, and text, is rooted in a series of intricate technical mechanisms that enable the fusion and understanding of disparate information streams.

At the fundamental level, an LLM's ability to handle multimodal data hinges on the concept of embeddings. An LLM inherently operates on numerical representations of data. For text, this involves converting words and sentences into dense vector embeddings where semantic relationships are encoded. To extend this to other modalities, a similar transformation is applied. For visual data, specialized neural networks like Convolutional Neural Networks (CNNs) or more recently, Vision Transformers (ViTs), are employed. These models are designed to extract hierarchical features from images – from basic edges and textures to complex objects and scenes – ultimately compressing this visual information into a fixed-size numerical vector. Similarly, audio data is processed by acoustic models that convert raw sound waves into embeddings that capture phonetic information, speech patterns, or even the emotional tone and environmental context.

The pivotal technical step is the projection of these distinct modal embeddings into a shared latent space. Imagine this as a common conceptual arena where the numerical representations of an image of a cat, the word "cat," and the sound of a cat meowing can all exist in close proximity, indicating their semantic relatedness. This alignment is crucial because it allows the LLM, which is fundamentally designed to process sequences of numerical tokens, to treat inputs from different modalities as part of a coherent whole.

Once all modalities are represented in this unified embedding space, the LLM's core transformer architecture comes into play for information fusion. The transformer's hallmark is its attention mechanism, particularly cross-attention. While self-attention within a single modality allows the model to understand internal relationships (e.g., how words relate to each other in a sentence), cross-attention layers enable the model to learn and attend to relationships between different modalities. For example, when presented with an image and a textual question about it, the cross-attention mechanism allows the LLM to selectively focus on the most relevant visual features in the image that correspond to the words in the question, and vice versa. This dynamic interplay facilitates a deeper, more contextual understanding that transcends the limitations of individual data types.

The training of these multimodal LLMs is a monumental undertaking, requiring colossal datasets where different modalities are meticulously paired (e.g., images with descriptive captions, video frames with transcribed dialogue). These models undergo extensive pre-training on these vast multimodal corpora, allowing them to learn robust alignments and correspondences across modalities. Subsequent fine-tuning on specific downstream tasks, such as visual question answering or text-to-image generation, further refines their ability to perform targeted functions. This iterative process of pre-training and fine-tuning leverages the LLM's inherent capacity to distill complex patterns and knowledge from immense volumes of diverse information.

The technical prowess of multimodal LLMs lies in their ability to standardize diverse data into a common numerical language, fuse these representations through sophisticated attention mechanisms, and learn deep cross-modal correlations from massive datasets. This technical foundation is what propels them beyond mere language processing into a realm of more comprehensive and contextually aware artificial intelligence.

Argumentation, GNN, and Textual Entailment

Argumentation is a fundamental aspect of human communication, and various frameworks have been developed to analyze and construct effective arguments: Aristotelian, Rogerian, Toulmin, Narrative, and Fallacy-based. Furthermore, these frameworks can be enhanced using Graph Neural Networks (GNNs), particularly within the context of textual entailment.

The Aristotelian framework, rooted in classical rhetoric, emphasizes persuasion through a combination of logical reasoning (logos), ethical appeal (ethos), and emotional appeal (pathos). It follows a structured approach, moving from an introduction and statement of the case to providing proof, refuting opposing arguments, and concluding with a strong peroration. This framework is well-suited for persuasive speeches and debates where a clear stance is essential.

In contrast, the Rogerian argument prioritizes finding common ground and reducing conflict. Developed by Carl Rogers, this approach involves understanding the opponent's perspective, acknowledging its validity, and working towards a mutually acceptable solution. Rogerian arguments are effective in situations where parties hold strongly opposing views and compromise is necessary.

The Toulmin model, proposed by Stephen Toulmin, focuses on the practical structure of everyday arguments. It breaks down an argument into six key components: claim, grounds, warrant, backing, qualifier, and rebuttal. This model provides a flexible framework for analyzing and constructing arguments in various contexts, highlighting the importance of evidence, justification, and acknowledging limitations.

Narrative arguments utilize storytelling to persuade, employing elements like plot, characters, setting, and theme. This approach can be particularly powerful in engaging the audience's emotions and conveying complex ideas through relatable narratives. Narrative arguments find applications in fields like law, where stories can shape perceptions of a case, and in marketing, where they forge emotional connections with consumers.

Finally, fallacy-based argumentation centers on identifying and avoiding logical fallacies - flaws in reasoning that weaken or invalidate arguments. By understanding common fallacies such as ad hominem, straw man, and slippery slope, individuals can construct stronger arguments and effectively critique the arguments of others. This framework is crucial for critical thinking and ensuring the validity of claims.

Applying GNNs to Textual Entailment

Textual entailment, the task of determining whether one text (premise) logically entails another (hypothesis), can be enhanced by integrating these argumentation frameworks with Graph Neural Networks (GNNs) and knowledge graphs. GNNs are neural network architectures designed to operate on graph-structured data, making them well-suited for representing the relationships between words, sentences, and concepts within arguments.

Here's how GNNs can be applied:

• Knowledge Graph Construction: A knowledge graph can be constructed to represent relevant background knowledge, concepts, and relationships related to the premise and hypothesis. Entities in the texts can be linked to nodes in the knowledge graph, and relationships between entities can be represented as edges.
• Argument Graph Representation: The premise and hypothesis can be parsed and represented as a graph, where nodes represent words or phrases, and edges represent syntactic or semantic relationships. Argumentation frameworks can inform the design of this graph. For instance, in a Toulmin-based graph, nodes could represent claims, grounds, and warrants, while edges could represent the inferential connections between them.
• GNN-based Reasoning: A GNN can be trained on the constructed graph to learn node representations that capture the semantic and argumentative relationships between the premise and hypothesis. The GNN can propagate information across the graph, allowing it to reason about the entailment relation.
• Entailment Prediction: The learned node representations can be used to predict whether the premise entails the hypothesis. This can be achieved by feeding the representations into a classifier that outputs an entailment probability.

For example, consider the sentence "A woman is playing the piano" entails "A person is playing a musical instrument". A GNN can be constructed where nodes represent "woman", "playing", "piano", "person", "musical instrument" and edges capture relationships like "is-a" (woman is-a person) and "part-of" (piano part-of musical instrument) from a knowledge graph. The GNN can then reason over this graph to infer the entailment relation.

Various argumentation frameworks offer valuable tools for constructing and analyzing arguments, each with its own strengths and applications. GNNs, combined with knowledge graphs, provide a powerful means of implementing these frameworks in computational tasks like textual entailment, enabling more sophisticated and nuanced reasoning over textual data.

Google Enterprise KG and Entity Reconciliation

Enterprise Knowledge Graph

Entity Reconciliation

Enterprise KG Basic vs Advanced

Speech-To-Text Models

• Whisper
• Whisper2
• Deepgram
• Wav2Vec2
• Mozilla DeepSpeech
• Mozilla DeepSpeech2
• SpeechBrain
• AWS Transcribe
• AssemblyAI Universal-1
• AssemblyAI Universal-2
• AssemblyAI Nano

Multiagent Papers

Awesome Multiagent Papers

AI Story Generation

AI Story Generation