Semi-Supervised Sequence Labeling with Self-Learned Features

—Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of labeled words. To tackle this issue, we propose a semisupervised approach to improve the sequence labeling procedure in IE through a class of algorithms with self-learned features (SLF). A supervised classifier can be trained with annotated text sequences and used to classify each word in a large set of unannotated sentences. By averaging predicted labels over all cases in the unlabeled corpus, SLF training builds class label distribution patterns for each word (or word attribute) in the dictionary and re-trains the current model iteratively adding these distributions as extra word features. Basic SLF models how likely a word could be assigned to target class types. Several extensions are proposed, such as learning words’ class boundary distributions. SLF exhibits robust and scalable behaviour and is easy t...

• Type: Conference
• Year: 2009
• Where: ICDM
• Authors: Yanjun Qi, Pavel Kuksa, Ronan Collobert, Kunihiko Sadamasa, Koray Kavukcuoglu, Jason Weston
• Keywords: Basic SLF Models, Data Mining, ICDM 2009, Information Extraction, Tasks Assigning Labels