Articles

This paper presents a named entity classifica- tion system that utilises both orthographic and contextual information. The random subspace method was employed to generate and refine at- tribute models. Supervised and unsupervised learning techniques used in the recombination of models to produce the final results. 1...

We present an approach to named entity recog- nition that uses support vector machines to cap- ture transition probabilities in a lattice. The support vector machines are trained with hun- dreds of thousands of features drawn from the CoNLL-2003 Shared Task training data. Mar- gin outputs are converted to estimated …...

We discuss two named-entity recognition mod- els which use characters and character � -grams either exclusively or as an important part of their data representation. The first model is a character-level HMM with minimal con- text information, and the second model is a maximum-entropy conditional markov model with substantially richer …...

In this approach to named entity recognition, a recurrent neural network, known as Long Short-Term Memory, is applied. The network is trained to perform 2 passes on each sentence, outputting its decisions on the second pass. The first pass is used to acquire information for dis- ambiguation during the second …...

Named Entity Recognition (NER) systems need to integrate a wide variety of information for optimal performance. This paper demonstrates that a maximum entropy tagger can effectively encode such information and identify named entities with very high accuracy. The tagger uses features which can be obtained for a vari- ety of …...

This paper presents a classifier-combination experimental framework for named entity recognition in which four diverse classi- fiers (robust linear classifier, maximum en- tropy, transformation-based learning, and hid- den Markov model) are combined under differ- ent conditions. When no gazetteer or other ad- ditional training resources are used, the com- bined …...

In this paper, we describe a system that applies maximum entropy (ME) models to the task of named entity recognition (NER). Starting with an annotated corpus and a set of features which are easily obtainable for almost any language, we first build a baseline NE recognizer which is then used …...

As part of our work on automatically build- ing knowledge structures from text, we apply machine learning to determine which clauses from multiple narratives describing similar sit- uations should be grouped together as descrip- tions of the same type of occurrence. Our approach to the problem uses textual similar- ity …...

In this paper we demonstrate methods of im- proving both the recall and the precision of au- tomatic methods for extraction of hyponymy (IS A) relations from free text. By applying la- tent semantic analysis (LSA) to filter extracted hyponymy relations we reduce the rate of er- ror of our …...

This paper defines a general form for class- based probabilistic language models and pro- poses an efficient algorithm for clustering based on this. Our evaluation experiments re- vealed that our method decreased computation time drastically, while retaining accuracy. 1...

Combining a naive Bayes classifier with the EM algorithm is one of the promising ap- proaches for making use of unlabeled data for disambiguation tasks when using local con- text features including word sense disambigua- tion and spelling correction. However, the use of unlabeled data via the basic EM algorithm …...

We describe an approach to tagging a monolin- gual dictionary with linguistic features. In par- ticular, we annotate the dictionary entries with parts of speech, number, and tense information. The algorithm uses a bilingual corpus as well as a statistical lexicon to find candidate train- ing examples for specific feature …...

The purpose of this work is to investigate the use of machine learning approaches for confi- dence estimation within a statistical machine translation application. Specifically, we at- tempt to learn probabilities of correctness for various model predictions, based on the native probabilites (i.e. the probabilites given by the original model) …...

This paper introduces PhraseNet, a context- sensitive lexical semantic knowledge base sys- tem. Based on the supposition that seman- tic proximity is not simply a relation between two words in isolation, but rather a relation between them in their context, English nouns and verbs, along with contexts they appear in, …...

This paper reports on experiments in clas- sifying the semantic role annotations as- signed to prepositional phrases in both the PENN TREEBANK and FRAMENET. In both cases, experiments are done to see how the prepositions can be classified given the dataset’s role inventory, using standard word-sense disambiguation fea- tures. In …...

Previous work has argued that memory-based learning is better than abstraction-based learn- ing for a set of language learning tasks. In this paper, we first attempt to generalize these re- sults to a new set of language learning tasks from the area of spoken dialog systems and to a different …...

We cluster verbs into lexical semantic classes, using a general set of noisy features that cap- ture syntactic and semantic properties of the verbs. The feature set was previously shown to work well in a supervised learning setting, us- ing known English verb classes. In moving to a scenario of …...

Statistical machine learning algorithms have been successfully applied to many natural lan- guage processing (NLP) problems. Compared to manually constructed systems, statistical NLP systems are often easier to develop and maintain since only annotated training text is required. From annotated data, the underlying statistical algorithm can build a model so …...

This paper investigates booststrapping part-of- speech taggers using co-training, in which two taggers are iteratively re-trained on each other’s output. Since the output of the taggers is noisy, there is a question of which newly labelled ex- amples to add to the training set. We investi- gate selecting examples by …...

In this paper, we improve an unsuper- vised learning method using the Expectation- Maximization (EM) algorithm proposed by Nigam et al. for text classification problems in order to apply it to word sense disambigua- tion (WSD) problems. The improved method stops the EM algorithm at the optimum itera- tion number. …...