Previous studies have shown that linguistic features of a word such as possession, genitive or other grammatical cases can be employed in word representations of a named entity recognition (NER) tagger to improve the performance for morphologically rich languages. However, these taggers require external morphological disambiguation (MD) tools to function which are hard to obtain or non-existent for many languages. In this work, we propose a model which alleviates the need for such disambiguators by jointly learning NER and MD taggers in languages for which one can provide a list of candidate morphological analyses. We show that this can be done independent of the morphological annotation schemes, which differ among languages. Our experiments employing three different model architectures that join these two tasks show that joint learning improves NER performance. Furthermore, the morphological disambiguator's performance is shown to be competitive.
 In this paper we introduce a multilingual Named Entity Recognition (NER) system that uses statistical modeling techniques. The system identifies and classifies NEs in the Hungarian and English languages by applying AdaBoostM1 and the C4.5 decision tree learning algorithm. We focused on building as large a feature set as possible, and used a split and recombine technique to fully exploit its potentials. This methodology provided an opportunity to train several independent decision tree classifiers based on different subsets of features and combine their decisions in a majority voting scheme. The corpus made for the CoNLL 2003 conference and a segment of Szeged Corpus was used for training and validation purposes. Both of them consist entirely of newswire articles. Our system remains portable across languages without requiring any major modification and slightly outperforms the best system of CoNLL 2003, and achieved a 94.77 F measure for Hungarian. The real value of our approach lies in its different basis compared to other top performing models for English, which makes our system extremely successful when used in combination with CoNLL modells. Models for many natural language tasks benefit from the flexibility to use overlapping, non-independent features. For example, the need for labeled data can be drastically reduced by taking advantage of domain knowledge in the form of word lists, part-of-speech tags, character n-grams, and capitalization patterns. While it is difficult to capture such inter-dependent features with a generative probabilistic model, conditionally-trained models, such as conditional maximum entropy models, handle them well. There has been significant work with such models for greedy sequence modeling in NLP (Ratnaparkhi, 1996; , 1998). Domain adaptation is an important problem in named entity recognition (NER). NER classifiers usually lose accuracy in the domain transfer due to the different data distribution between the source and the target domains. The major reason for performance degrading is that each entity type often has lots of domain-specific term representations in the different domains. The existing approaches usually need an amount of labeled target domain data for tuning the original model. However, it is a labor-intensive and time-consuming task to build annotated training data set for every target domain. We present a domain adaptation method with latent semantic association (LaSA). This method effectively overcomes the data distribution difference without leveraging any labeled target domain data. LaSA model is constructed to capture latent semantic association among words from the unlabeled corpus. It groups words into a set of concepts according to the related context snippets. In the domain transfer, the original term spaces of both domains are projected to a concept space using LaSA model at first, then the original NER model is tuned based on the semantic association features. Experimental results on English and Chinese corpus show that LaSA-based domain adaptation significantly enhances the performance of NER. This thesis describes a novel statistical named-entity (i.e. “proper name”) recognition system known as “MENE” (Maximum Entropy Named Entity). Named entity (N.E.) recognition is a form of information extraction in which we seek to classify every word in a document as being a person-name, organization, location, date, time, monetary value, percentage, or “none of the above”. The task has particular significance for Internet search engines, machine translation, the automatic indexing of documents, and as a foundation for work on more complex information extraction tasks. Two of the most significant problems facing the constructor of a named entity system are the questions of portability and system performance. A practical N.E. system will need to be ported frequently to new bodies of text and even to new languages. The challenge is to build a system which can be ported with minimal expense (in particular minimal programming by a computational linguist) while maintaining a high degree of accuracy in the new domains or languages. MENE attempts to address these issues through the use of maximum entropy probabilistic modeling. It utilizes a very flexible object-based architecture which allows it to make use of a broad range of knowledge sources in making its tagging decisions. In the DARPA-sponsored MUC-7 named entity evaluation, the system displayed an accuracy rate which was well-above the median, demonstrating that it can achieve the performance goal. In addition, we demonstrate that the system can be used as a post-processing tool to enhance the output of a hand-coded named entity recognizer through experiments in which MENE improved on the performance of N.E. systems from three different sites. Furthermore, when all three external recognizers are combined under MENE, we are able to achieve very strong results which, in some cases, appear to be competitive with human performance. Finally, we demonstrate the trans-lingual portability of the system. We ported the system to two Japanese-language named entity tasks, one of which involved a new named entity category, “artifact”. Our results on these tasks were competitive with the best systems built by native Japanese speakers despite the fact that the author speaks no Japanese. Domain adaptation is an important problem in natural language processing (NLP) due to the lack of labeled data in novel domains. In this paper, we study the domain adaptation problem from the instance weighting perspective. We formally analyze and characterize the domain adaptation problem from a distributional view, and show that there are two distinct needs for adaptation, corresponding to the different distributions of instances and classification functions in the source and the target domains. We then propose a general instance weighting framework for domain adaptation. Our empirical results on three NLP tasks show that incorporating and exploiting more information from the target domain through instance weighting is effective. Bootstrapping is the process of improving the performance of a trained classifier by iteratively adding data that is labeled by the classifier itself to the training set, and retraining the classifier. It is often used in situations where labeled training data is scarce but unlabeled data is abundant. In this paper, we consider the problem of domain adaptation: the situation where training data may not be scarce, but belongs to a different domain from the target application domain. As the distribution of unlabeled data is different from the training data, standard bootstrapping often has difficulty selecting informative data to add to the training set. We propose an effective domain adaptive bootstrapping algorithm that selects unlabeled target domain data that are informative about the target domain and easy to automatically label correctly. We call these instances bridges, as they are used to bridge the source domain to the target domain. We show that the method outperforms supervised, transductive and bootstrapping algorithms on the named entity recognition task. Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling, a simple Monte Carlo method used to perform approximate inference in factored probabilistic models. By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, CMMs, and CRFs, it is possible to incorporate non-local structure while preserving tractable inference. We use this technique to augment an existing CRF-based information extraction system with long-distance dependency models, enforcing label consistency and extraction template consistency constraints. This technique results in an error reduction of up to 9 over state-of-the-art systems on two established information extraction tasks. SRI International participated in the MUC-6 evaluation using the latest version of SRI's FASTUS system [1]. The FASTUS system was originally developed for participation in the MUC-4 evaluation [3] in 1992, and the performance of FASTUS in MUC-4 helped demonstrate the viability of finite state technologies in constrained natural-language understanding tasks. The system has undergone significant revision since MUC-4, and it is safe to say that the current system does not share a single line of code with the original. The fundamental ideas behind FASTUS, however, are retained in the current system: an architecture consisting of cascaded finite state transducers, each providing an additional level of analysis of the input, together with merging of the final results.