2507744
Exploiting Domain Structure for Named Entity Recognition
Named Entity Recognition (NER) is a fundamental task in text mining and natural language understanding. Current approaches to NER (mostly based on supervised learning) perform well on domains similar to the training domain, but they tend to adapt poorly to slightly different domains. We present several strategies for exploiting the domain structure in the training data to learn a more robust named entity recognizer that can perform well on a new domain. First, we propose a simple yet effective way to automatically rank features based on their generalizabilities across domains. We then train a classifier with strong emphasis on the most generalizable features. This emphasis is imposed by putting a rank-based prior on a logistic regression model. We further propose a domain-aware cross validation strategy to help choose an appropriate parameter for the rank-based prior. We evaluated the proposed method with a task of recognizing named entities (genes) in biology text involving three species. The experiment results show that the new domain-aware approach outperforms a state-of-the-art baseline method in adapting to new domains, especially when there is a great difference between the new domain and the training domain.
8322277
Accurately extracting coherent relevant passages using hidden Markov models
In this paper, we present a principled method for accurately extracting coherent relevant passages of variable lengths using HMMs. We show that with appropriate parameter estimation, the HMM method outperforms a number of strong baseline methods on two data sets.
1349350
Extraction of coherent relevant passages using hidden Markov models
In information retrieval, retrieving relevant passages, as opposed to whole documents, not only directly benefits the end user by filtering out the irrelevant information within a long relevant document, but also improves retrieval accuracy in general. A critical problem in passage retrieval is to extract coherent relevant passages accurately from a document, which we refer to as passage extraction. While much work has been done on passage retrieval, the passage extraction problem has not been seriously studied. Most existing work tends to rely on presegmenting documents into fixed-length passages which are unlikely optimal because the length of a relevant passage is presumably highly sensitive to both the query and document.In this article, we present a new method for accurately detecting coherent relevant passages of variable lengths using hidden Markov models (HMMs). The HMM-based method naturally captures the topical boundaries between passages relevant and nonrelevant to the query. Pseudo-feedback mechanisms can be naturally incorporated into such an HMM-based framework to improve parameter estimation. We show that with appropriate parameter estimation, the HMM method outperforms a number of strong baseline methods on two datasets. We further show how the HMM method can be applied on top of any basic passage extraction method to improve passage boundaries.
2667365
Self-stabilizing Asynchronous Phase Synchronization in General Graphs
The phase synchronization problem requires each node to infinitely transfer from one phase to the next one under the restriction that at most two consecutive phases can appear among all nodes. In this paper, we propose a self-stabilizing algorithm under the parallel execution model to solve this problem for semi-uniform systems of general graph topologies. The proposed algorithm is memory-efficient; its space complexity per node is O(logΔ + logK) bits, where Δ is the maximum degree of the system and K > 1 is the number of phases.
6293007
A revisit to block and recursive least squares for parameter estimation
In this paper, the classical least squares (LS) and recursive least squares (RLS) for parameter estimation have been re-examined in the light of the present day computing capabilities. It has been demonstrated that for linear time-invariant systems, the performance of blockwise least squares (BLS) is always superior to that of RLS. In the context of parameter estimation for dynamic systems, the current computational capability of personal computers are more than adequate for BLS. However, for time-varying systems with abrupt parameter changes, standard blockwise LS may no longer be suitable due to its inefficiency in discarding ''old'' data. To deal with this limitation, a novel sliding window blockwise least squares approach with automatically adjustable window length triggered by a change detection scheme is proposed. Two types of sliding windows, rectangular and exponential, have been investigated. The performance of the proposed algorithm has been illustrated by comparing with the standard RLS and an exponentially weighted RLS (EWRLS) using two examples. The simulation results have conclusively shown that: (1) BLS has better performance than RLS; (2) the proposed variable-length sliding window blockwise least squares (VLSWBLS) algorithm can outperform RLS with forgetting factors; (3) the scheme has both good tracking ability for abrupt parameter changes and can ensure the high accuracy of parameter estimate at the steady-state; and (4) the computational burden of VLSWBLS is completely manageable with the current computer technology. Even though the idea presented here is straightforward, it has significant implications to virtually all areas of application where RLS schemes are used.