5 citations · 5 across the 1 of their papers we have counts for
7 papers
Intent Features for Rich Natural Language Understanding
Brian Lester, Sagnik Ray Choudhury, Rashmi Prasad +1
Complex natural language understanding modules in dialog systems have a richer understanding of user utterances, and thus are critical in providing a better user experience. Howeve…
iobes: A Library for Span-Level Processing
Brian Lester
Many tasks in natural language processing, such as named entity recognition and slot-filling, involve identifying and labeling specific spans of text. In order to leverage common m…
Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers
Brian Lester, Daniel Pressel, Amy Hemmeter +2
Current state-of-the-art models for named entity recognition (NER) are neural models with a conditional random field (CRF) as the final layer. Entities are represented as per-token…
Multiple Word Embeddings for Increased Diversity of Representation
Brian Lester, Daniel Pressel, Amy Hemmeter +2
Most state-of-the-art models in natural language processing (NLP) are neural models built on top of large, pre-trained, contextual language models that generate representations of…
Leader: Prefixing a Length for Faster Word Vector Serialization
Brian Lester
Two competing file formats have become the de facto standards for distributing pre-trained word embeddings. Both are named after the most popular pre-trained embeddings that are di…
Computationally Efficient NER Taggers with Combined Embeddings and Constrained Decoding
Brian Lester, Daniel Pressel, Amy Hemmeter +1
Current State-of-the-Art models in Named Entity Recognition (NER) are neural models with a Conditional Random Field (CRF) as the final network layer, and pre-trained "contextual em…