arXiv:2610. 30518v1 Announce Type: new Abstract: We present Protegrity-PII, a new PII decision model for structured and unstructured text.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.
We present Protegrity-PII, a new PII decision model for structured and unstructured text. Protegrity-PII achieves a typed recall of 0.915 with a precision of 0.907 on a new benchmark we propose, PII-S...
arXiv:2410.17355v4 Announce Type: replace Abstract: Due to their capacity to acquire world knowledge from large corpora, pre-trained language models (PLMs) are extensively used in ultra-fine entity t...
arXiv:2608. 08636v1 Announce Type: cross Abstract: Scientific named entity recognition (SciNER) plays a crucial role in information extraction and knowledge discovery from scientific texts.
SciNLP is a new benchmark dataset for full‑text entity and relation extraction in the NLP domain, comprising 60 manually annotated papers with 6,429 entities and 1,649 relations. It is the first dataset to provide full‑text annotations of entities and their relationships specifically for NLP literature. Experiments show that models trained on SciNLP outperform baselines on certain tasks, and the dataset enabled the automatic construction of a fine‑grained knowledge graph with an average node degree of 3.3.
arXiv:2610.17113v1 Announce Type: cross Abstract: Information extraction models often perform well on the datasets they are trained on, but transferring to new domains and annotation guidelines remai...
The paper evaluates open‑source large language models (LLMs) for both flat and nested named entity recognition (NER) tasks, comparing them to traditional encoder‑based models and decoder‑based LLMs using in‑context learning. It shows that with parameter‑efficient fine‑tuning and structured output formats (e.g., inline brackets or XML), LLMs can match or exceed the performance of conventional NER systems, and that their NER abilities arise from instruction following rather than memorization. Additionally, fine‑tuning for NER minimally affects, and can even improve, the LLMs’ general capabilities on other benchmarks.