Information Extraction

How information extraction works

A traditional IE pipeline runs in stages. Text is first tokenized and tagged, then entities are identified through named entity recognition: the people, companies, dates, money amounts, and products in the text. Relation extraction links those entities (Acme Corp employs Jane Doe; Acme Corp acquired Beta Inc), and event extraction captures who did what to whom and when. Coreference resolution stitches together mentions that refer to the same thing, so that "the company," "Acme," and "it" collapse to one entity. The results are commonly stored as entity-relation-entity or subject-predicate-object triples.

The classic approach used hand-written rules or statistical sequence models such as CRFs and HMMs, each tuned per domain and per document type. Modern systems increasingly hand the job to large language models, which can read a varied corpus and emit structured output, often JSON against a target schema, without a template built for every source. This flexibility comes with a cost covered below: models can misread a number or assert a fact the source never contained.

Information extraction vs. information retrieval

The two are easy to conflate because both operate over text corpora, but they solve opposite halves of a problem. Information retrieval finds: given a query, it returns whole relevant documents or passages, the way a search engine surfaces pages. Information extraction structures: given a document, it pulls specific facts out of the prose and writes them into fields. Retrieval hands you the press release; extraction hands you the row that says Acme raised $50M.

The distinction matters in practice because a competitive-intelligence stack usually needs both. Retrieval (and its modern relative, retrieval-augmented generation) narrows a large corpus to the documents worth reading. Extraction then converts those documents into the structured records that power alerts, comparisons, and timelines. Named entity recognition sits inside extraction as one of its subtasks, since identifying the entities is a prerequisite for extracting the relations and events that connect them.

How competitive-intelligence teams use extraction

In a CI workflow, extraction is the step that makes monitoring queryable. Scraping and crawling collect the raw text of a competitor's website, pricing page, careers page, and newsroom; extraction interprets that text into facts a system can act on: a funding round, a new executive hire, a pricing tier change, a product launch, or a named partnership. Those facts become structured signal: they can trigger a real-time alert, populate a dashboard, or append to a searchable competitor timeline.

Because the source formats are varied and inconsistent, spanning a terse press release, a rambling blog post, or a job description buried in boilerplate, CI tooling increasingly leans on LLM-based extraction rather than a template per site. The tradeoff is that a competitor timeline is only as trustworthy as the extraction behind it, which is why mature tooling links each extracted fact back to its source document and, where possible, attaches a confidence signal so an analyst can verify before acting.

Common failure modes and limits

Extraction errors fall into two broad classes: missing facts the text does contain, and asserting facts it does not. Rule-based systems tend toward the first, since brittle patterns silently skip anything phrased unexpectedly. LLM-based extraction is more prone to the second: models can misread a numeric value (turning a $5M seed into $50M), attach a fact to the wrong entity when coreference is ambiguous, or hallucinate a detail such as a date that never appeared in the source. For competitive intelligence, where a fabricated funding figure or an invented hire can misdirect a real decision, these are not cosmetic bugs.

The practical guardrails are source-linking, so every extracted claim is traceable to the exact passage it came from, and confidence scoring or human review on high-stakes fields. Precision and recall, the metrics MUC standardized decades ago, remain the honest way to measure an extraction system: how many extracted facts are correct, and how many of the true facts were caught.

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