Conditional Random Fields (CRFs) have proven to perform well on natural language processing tasks like name transliteration, concept tagging or grapheme-to-phoneme (g2p) conversio...
This paper describes a new approach to modeling duration for LVCSR using SCARF, a toolkit for speech recognition with segmental conditional random fields. We utilize SCARF’s abi...
Conditional Random Fields (CRFs) are a state-of-the-art approach to natural language processing tasks like grapheme-tophoneme (g2p) conversion which is used to produce pronunciati...
Patrick Lehnen, Stefan Hahn, Andreas Guta, Hermann...
We focus in this paper on the named entity recognition task in spoken data. The proposed approach investigates the use of various contexts of the words to improve recognition. Exp...
One of the most common functions of smart environments is to monitor and assist older adults with their activities of daily living. Activity recognition is a key component in this...
Ehsan Nazerfard, Barnan Das, Lawrence B. Holder, D...
We present novel kernels based on structured and unstructured features for reranking the N-best hypotheses of conditional random fields (CRFs) applied to entity extraction. The fo...
Truc-Vien T. Nguyen, Alessandro Moschitti, Giusepp...
This paper analyzes the contribution of semantic roles to TimeML event recognition and classification. For that purpose, an approach using conditional random fields with a variety...
Extracting titles from a PDFs full text is an important task in information retrieval to identify PDFs. Existing approaches apply complicated and expensive (in terms of calculating...
This paper describes an online handwritten Japanese character string recognition system based on conditional random fields, which integrates the information of character recogniti...
Conditional random fields (CRFs) have been quite successful in various machine learning tasks. However, as larger and larger data become acceptable for the current computational ma...