Classifier chains have recently been proposed as an appealing method for tackling the multi-label classification task. In addition to several empirical studies showing its state-of-the-art performance, especially when being used in its ensemble variant, there are also some first results on theoretical properties of classifier chains. Continuing along this line, we analyze the influence of a potential pitfall of the learning process, namely the discrepancy between the feature spaces used in training and testing: While true class labels are used as supplementary attributes for training the binary models along the chain, the same models need to rely on estimations of these labels at prediction time. We elucidate under which circumstances the attribute noise thus created can affect the overall prediction performance. As a result of our findings, we propose two modifications of classifier chains that are meant to overcome this problem. Experimentally, we show that our variants are indeed able to produce better results in cases where the original chaining process is likely to fail.
, for instance, involves the assignment of a categorical label to each element of a sequence of observed values. A typical example is part of speech tagging: Given a sentence (or even a whole document) as an input, the task is to assign a part of speech to each individual word. Obviously, there is a strong dependency between the labels in a given sequence. Therefore, to make an optimal prediction of the label for a specific word, it is important to take the context of this label into consideration, i.e., the (predicted) labels of nearby words. To this end, quite a number of have been developed and applied to this task @cite_19 ; examples of such algorithms include hidden Markov models, conditional random fields, as well as methods such as SEARN @cite_13 and HC-Search @cite_9 @cite_0 , which combine search (in the output space) and learning.
@cite_0 @cite_19 @cite_9 @cite_13
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In several natural language tasks, labeled sequences are available in separate domains (say, languages), but the goal is to label sequences with mixed domain (such as code-switched text). Or, we may have available models for labeling whole passages (say, with sentiments), which we would like to exploit toward better position-specific label inference (say, target-dependent sentiment annotation). A key characteristic shared across such tasks is that different positions in a primary instance can benefit from different experts' trained from auxiliary data, but labeled primary instances are scarce, and labeling the best expert for each position entails unacceptable cognitive burden. We propose GITNet, a unified position-sensitive multi-task recurrent neural network (RNN) architecture for such applications. Auxiliary and primary tasks need not share training instances. Auxiliary RNNs are trained over auxiliary instances. A primary instance is also submitted to each auxiliary RNN, but their state sequences are gated and merged into a novel composite state sequence tailored to the primary inference task. Our approach is in sharp contrast to recent multi-task networks like the cross-stitch and sluice network, which do not control state transfer at such fine granularity. We demonstrate the superiority of GIRNet using three applications: sentiment classification of code-switched passages, part-of-speech tagging of code-switched text, and target position-sensitive annotation of sentiment in monolingual passages. In all cases, we establish new state-of-the-art performance beyond recent competitive baselines. Abstract A system for part-of-speech tagging is described. It is based on a hidden Markov model which can be trained using a corpus of untagged text. Several techniques are introduced to achieve robustness while maintaining high performance. Word equivalence classes are used to reduce the overall number of parameters in the model, alleviating the problem of obtaining reliable estimates for individual words. The context for category prediction is extended selectively via predefined networks, rather than using a uniformly higher-order conditioning which requires exponentially more parameters with increasing context. The networks are embedded in a first-order model and network structure is developed by analysis of erros, and also via linguistic considerations. To compensate for incomplete dictionary coverage, the categories of unknown words are predicted using both local context and suffix information to aid in disambiguation. An evaluation was performed using the Brown corpus and different dictionary arrangements were investigated. The techniques result in a model that correctly tags approximately 96 of the text. The flexibility of the methods is illustrated by their use in a tagging program for French. In part of speech tagging by Hidden Markov Model, a statistical model is used to assign grammatical categories to words in a text. Early work in the field relied on a corpus which had been tagged by a human annotator to train the model. More recently, (1992) suggest that training can be achieved with a minimal lexicon and a limited amount of a priori information about probabilities, by using an Baum-Welch re-estimation to automatically refine the model. In this paper, I report two experiments designed to determine how much manual training information is needed. The first experiment suggests that initial biasing of either lexical or transition probabilities is essential to achieve a good accuracy. The second experiment reveals that there are three distinct patterns of Baum-Welch reestimation. In two of the patterns, the re-estimation ultimately reduces the accuracy of the tagging rather than improving it. The pattern which is applicable can be predicted from the quality of the initial model and the similarity between the tagged training corpus (if any) and the corpus to be tagged. Heuristics for deciding how to use re-estimation in an effective manner are given. The conclusions are broadly in agreement with those of Merialdo (1994), but give greater detail about the contributions of different parts of the model. There is rich knowledge encoded in online web data. For example, punctuation and entity tags in Wikipedia data define some word boundaries in a sentence. In this paper we adopt partial-label learning with conditional random fields to make use of this valuable knowledge for semi-supervised Chinese word segmentation. The basic idea of partial-label learning is to optimize a cost function that marginalizes the probability mass in the constrained space that encodes this knowledge. By integrating some domain adaptation techniques, such as EasyAdapt, our result reaches an F-measure of 95.98 on the CTB-6 corpus, a significant improvement from both the supervised baseline and a previous proposed approach, namely constrained decode.