Synonym detection exploits redundant information to train several domain-specific synonym classifiers in a semi-supervised fashion.

NELL is an information extraction system that has been running 24x7 for over a year, using coupled semi-supervised learning to populate an ontology from unstructured text found on the web.

After mapping each noun phrase to one or more senses (each with a distinct category type), ConceptResolver performs semi-supervised clustering to find synonymous senses.

For each category, ConceptResolver trains a semi-supervised synonym classifier then uses its predictions to cluster word senses.

Train a semi-supervised classifier to predict synonymy.

used to train a semi-supervised classifier.

However, our evaluation shows that ConceptResolver has higher synonym resolution precision than Resolver, which we attribute to our semi-supervised approach and the known relation schema.

ConceptResolver’s approach lies between these two extremes: we label a small number of synonyms (10 pairs), then use semi-supervised training to learn a similarity function.

ConceptResolver uses a novel algorithm for semi-supervised clustering which is conceptually similar to other work in the area.

We propose to combine a K-Nearest Neighbors (KNN) classifier with a linear Conditional Random Fields (CRF) model under a semi-supervised learning framework to tackle these challenges.

The semi-supervised learning plus the gazetteers alleviate the lack of training data.

Extensive experiments show the advantages of our method over the baselines as well as the effectiveness of KNN and semi-supervised learning.

(2010), which introduces a high-level rule language, called NERL, to build the general and domain specific NER systems; and 2) semi-supervised learning, which aims to use the abundant unlabeled data to compensate for the lack of annotated data.

Indeed, it is the combination of KNN and CRF under a semi-supervised learning framework that differentiates ours from the existing.

It is also demonstrated that integrating KNN classified results into the CRF model and semi-supervised learning considerably boost the performance.

Related work can be roughly divided into three categories: NER on tweets, NER on non-tweets (e.g., news, biological medicine, and clinical notes), and semi-supervised learning for NER.

To achieve this, a KNN classifier with a CRF model is combined to leverage cross tweets information, and the semi-supervised learning is adopted to leverage unlabeled tweets.

2.3 Semi-supervised Learning for NER

We consider a semi-supervised setting for domain adaptation where only unlabeled data is available for the target domain.

As we discussed in the introduction, our goal is to provide a method for domain adaptation based on semi-supervised learning of models with distributed representations.

In this section, we first discuss the shortcomings of domain adaptation with the above-described semi-supervised approach and motivate constraints on inter-domain variability of

For every pair, the semi-supervised methods use labeled data from the source domain and unlabeled data from both domains.

All the methods, supervised and semi-supervised , are based on the model described in Section 2.

This does not seem to have an adverse effect on the accuracy but makes learning very efficient: the average training time for the semi-supervised methods was about 20 minutes on a standard PC.

The danger of this semi-supervised approach in the domain-adaptation setting is that some of the latent variables will correspond to clusters of features specific only to the source domain, and consequently, the classifier relying on this latent variable will be badly affected when tested on the target domain.

Various semi-supervised techniques for domain-adaptation have also been considered, one example being self-training (McClosky et al., 2006).

Semi-supervised leam-ing with distributed representations and its application to domain adaptation has previously been considered in (Huang and Yates, 2009), but no attempt has been made to address problems specific to the domain-adaptation setting.

This paper proposes a novel (semi-)supervised hashing method named Semi-Supervised SimHash (83H) for high—dimensional data similarity search.

2.3 Semi-Supervised Hashing

Semi-Supervised Hashing (SSH) (Wang et al., 2010a) is recently proposed to incorporate prior knowledge for better hashing.

vated by this, some supervised methods are proposed to derive effective hash functions from prior knowledge, i.e., Spectral Hashing (Weiss et al., 2009) and Semi-Supervised Hashing (SSH) (Wang et al., 2010a).

This paper proposes a novel (semi-)supervised hashing method, Semi-Supervised SimHash (S3H), for high-dimensional data similarity search.

In Section 3, we describe our proposed Semi-Supervised SimHash (S3H).

In this section, we present our hashing method, named Semi-Supervised SimHash (83H).

We have proposed a novel supervised hashing method named Semi-Supervised Simhash (83H) for high-dimensional data similarity search.

We conduct experiments on data sets from the NEWS 2010 shared task on transliteration mining and achieve an F-measure of up to 92%, outperforming most of the semi-supervised systems that were submitted.

We evaluated it against the semi-supervised systems of NEWS10 and achieved high F-measure and performed better than most of the semi-supervised systems.

On the WIL data sets, we compare our fully unsupervised system with the semi-supervised systems presented at the NEWSlO (Kumaran et al., 2010).

For English/Arabic, English/Hindi and English/Tamil, our system is better than most of the semi-supervised systems presented at the NEWS 2010 shared task for transliteration mining.

We compare our unsupervised transliteration mining method with the semi-supervised systems presented at the NEWS 2010 shared task on transliteration mining (Kumaran et al., 2010) using four language pairs.

These systems used a manually labelled set of data for initial supervised training, which means that they are semi-supervised systems.

We achieve an F-measure of up to 92% outperforming most of the semi-supervised systems.

Our unsupervised method seems robust as its performance is similar to or better than many of the semi-supervised systems on three language pairs.

We present a simple semi-supervised relation extraction system with large-scale word clustering.

When training on different sizes of data, our semi-supervised approach consistently outperformed a state-of-the-art supervised baseline system.

The cluster based semi-supervised system works by adding an additional layer of lexical features that incorporate word clusters as shown in column 4 of Table 4.

We have described a semi-supervised relation extraction system with large-scale word clustering.

For the semi-supervised system, 70 percent of the rest of the documents were randomly selected as training data and 30 percent as development data.

For the semi-supervised system, each test fold was the same one used in the baseline and the other 4 folds were further split into a training set and a development set in a ratio of 7:3 for selecting clusters.

(2007) propose an objective function for semi-supervised extraction that balances likelihood of labeled instances and constraint violation on unlabeled instances.

Comparison against Supervised CRF Our final set of experiments compares a semi-supervised version of our model against a conditional random field (CRF) model.

For finance, it takes at least 10 annotated documents (corresponding to roughly 130 annotated relation instances) for the CRF to match the semi-supervised model’s performance.

There are three main approaches to CoRe: supervised, semi-supervised (or weakly supervised) and unsupervised.

We use the term semi-supervised for approaches that use some amount of human-labeled coreference pairs.

(2002) used co-training for coreference resolution, a semi-supervised method.

Semi-supervised Learning.

Another line of related work is semi-supervised learning, which combines labeled and unlabeled data to improve the performance of the task of interest (Zhu and Goldberg, 2009).

Among the popular semi-supervised methods (e. g. EM on Nai've Bayes (Nigam et al., 2000), co-training (Blum and Mitchell, 1998), transductive SVMs (Joachims, 1999b), and co-regularization (Sindhwani et al., 2005; Amini et al., 2010)), our approach employs the EM algorithm, extending it to the bilingual case based on maximum entropy.

Type D, C and S denote discriminative, combined and semi-supervised systems, respectively.

We also compare our method with the semi-supervised approaches, the semi-supervised approaches achieved very high accuracies by leveraging on large unlabeled data directly into the systems for joint learning and decoding, while in our method, we only explore the N-gram features to further improve supervised dependency parsing performance.

(2008) proposed a semi-supervised dependency parsing by introducing lexical intermediaries at a coarser level than words themselves via a cluster method.