The article "From Words to Vectors: What Happens in Between?" explores the process of converting textual data into numerical representations, focusing on techniques such as TF-IDF and vector space models. It discusses how these representations enable text classification tasks and provides a practical overview of the underlying concepts. The piece serves as a guide for readers interested in the mechanics of text preprocessing and feature extraction for machine learning.
By Nikhil Dasari
Enterprise Document Intelligence [Vol. 1 #2] Why the same vector search that handles synonyms and paraphrase silently fails on negation, exact identifiers, and your company’s acronyms, and what to use when it does.
By angela shi
An end-to-end classical NLP experiment on Kaggle’s Spooky Author Identification task: from Vowpal Wabbit and TF-IDF/NB-SVM baselines to a tuned stacked ensemble, with a compact representation survey of Bag-of-Words, BM25, Word2Vec, and FastText for context. The post How Far Can Classical NLP Go?
By Nahid Ahmadvand
The paper introduces a framework to assess how linearly encoded linguistic relations are within language model embeddings. Using a constrained linear approximation on related and unrelated word pairs, the authors evaluate an extended BATS dataset across inflectional, derivational, lexicographic, and encyclopedic relations in GloVe, RoBERTa, and ModernBERT. Results show near-perfect linear encoding for inflectional and derivational relations, higher errors for lexicographic and encyclopedic relations—especially in one-to-many and many-to-many cases—and that RoBERTa and ModernBERT encode relations more linearly than GloVe.
By Vasudevan Nedumpozhimana, Fathima Thekkekara, John Kelleher
The paper introduces an inductive method for learning concept embeddings from cross‑linguistic colexification networks combined with short textual descriptions. Unlike previous transductive approaches, this framework can embed unseen concepts using only a brief definition. The resulting embeddings correlate with cross‑linguistic similarity measures and generalize well to new concepts.
By Arne Rubehn
Enterprise Document Intelligence [Vol. 1 #7B] - Retrieval is filtering on structured tables: keywords first, TOC second, embeddings last The post Finding the right anchors for RAG: keyword, embedding, and TOC signals in parallel appeared first on Towards Data Science .
By angela shi
What exactly does word2vec learn, and how? Answering this question amounts to understanding representation learning in a minimal yet interesting language modeling task.
arXiv:2608.27813v1 Announce Type: new
Abstract: Structural probes were introduced by Hewitt and Manning to reconstruct syntactic trees from a neural language model's latent representations. They are...
By Juan Pablo Vigneaux, Mary Kennedy, Khalil Iskarous, Robert Frank, Matilde Marcolli
Enterprise Document Intelligence [Vol. 1 #7B] - Retrieval is filtering on structured tables: keywords first, TOC second, embeddings last The post Anchor Detection for RAG: Parallel Detectors, Then One LLM Call at the End appeared first on Towards Data Science .
By angela shi
The paper investigates whether embedding spaces capture objective physical measurements such as mass, distance, time, and volume. It finds that these embeddings only weakly model such measurements and instead exhibit peculiar patterns. Further analysis shows that superficial string similarity heavily influences the representation of physical measurements, and recalibrating similarity does not significantly improve alignment.
By Juri Opitz, Andrianos Michail
arXiv:2607. 23675v1 Announce Type: cross Abstract: The embedding of word sequences into continuous vector spaces has been one of the most important developments in Natural Language Processing in the recent past.
By Salomon Kabongo
The article explains that enterprise document intelligence can be categorized into three distinct corpus types, each requiring a specific architecture. It outlines how to determine the shape of a document collection through three key questions. The piece also discusses the costs associated with building a system for the incorrect corpus type.
By angela shi