Cascaded approach has been used for a long time to conduct sub-tasks in order to accomplish a major task. We put cascaded approach in a probabilistic framework and analyze possible reasons for cascaded errors. To reduce the occurrence of cascaded errors, we need to add a constraint when performing joint training. We suggest a pseudo Conditional Random Field (pseudo-CRF) approach that models two sub-tasks as two Conditional Random Fields (CRFs). We then present the formulation in the context of a linear chain CRF for solving problems on sequence data. In conducting joint training for a pseudo-CRF, we reuse all existing well-developed efficient inference algorithms for a linear chain CRF, which would otherwise require the use of approximate inference algorithms or simulations that involve long computational time. Our experimental results show an interesting fact that a jointly trained CRF model in a pseudo-CRF may perform worse than a separately trained CRF on a sub-task. However the overall system performance of a pseudo-CRF would outperform that of a cascaded approach. We implement the implicit constraint in the form of a soft constraint such that users can define the penalty cost for violating the constraint. In order to work on large-scale datasets, we further suggest a parallel implementation of the pseudo-CRF approach, which can be implemented on a multi-core CPU or GPU on a graphics card that supports multi-threading. Our experimental results show that it can achieve a 12 times increase in speedup.

In recent years, conditional random fields (CRFs) have shown good performance in named entity recognition tasks. However, a direct application of it to biomedical named entity recognition incurs a very high training cost. In this paper, we evaluate two alternatives to training a CRF with a traditional single-phase maximum likelihood training method. One is to use an online training method and the other is to divide the named entity recognition task into two tasks. For the cascaded method, we propose to include a "margin" in the model that leads to better recognition results. Both methods give better performance with substantial decrease in training time. In particular, the cascaded method outperforms the best system in the JNLPBA shared task.

We propose a cascaded approach for extracting biomedical named entities from text documents using a unified model. Previous works often ignore the high computational cost incurred by a single-phase approach. We alleviate this problem by dividing the named entity extraction task into a segmentation task and a classification task, reducing the computational cost by an order of magnitude. A unified model, which we term "maximum-entropy margin-based" (MEMB), is used in both tasks. The MEMB model considers the error between a correct and an incorrect output during training and helps improve the performance of extracting sparse entity types that occur in biomedical literature. We report experimental evaluations on the GENIA corpus available from the BioNLP/NLPBA (2004) shared task, which demonstrate the state-of-the-art performance achieved by the proposed approach.

This article introduces a named entity matching model that makes use of both semantic and phonetic evidence. The matching of semantic and phonetic information is captured by a unified framework via a bipartite graph model. By considering various technical challenges of the problem, including order insensitivity and partial matching, this approach is less rigid than existing approaches and highly robust. One major component is a phonetic matching model which exploits similarity at the phoneme level. Two learning algorithms for learning the similarity information of basic phonemic matching units based on training examples are investigated. By applying the proposed named entity matching model, a mining system is developed for discovering new named entity translations from daily Web news. The system is able to discover new name translations that cannot be found in the existing bilingual dictionary.

We develop an unsupervised framework which can collaboratively extract information from multiple Web pages, as well as conduct feature mining tasks in a unified model. Our model allows tight interactions of the two tasks removing the unnecessary boundary between the two tasks. It is beneficial for both tasks since the decisions for information extraction and feature mining can be done in a coherent manner assigning solutions optimizing the quality of both tasks and at the same time eliminating the potential conflicts. Our approach is designed based on an undirected graphical model which can model the inter-dependence between the neighbouring tokens within the same Web page, as well as tokens in different Web pages. Multiple Web pages are considered under this model and the information can be extracted collectively. This design also leads to another characteristic of our framework in that it can conduct mining across Web pages simultaneously. We demonstrate the efficacy of our model by applying it to the important product feature mining application. Extensive experiments on real-world data have been conducted to evaluate our framework.