Articles

It is possible to reduce the bulk of phrase- tables for Statistical Machine Translation us- ing a technique based on the significance testing of phrase pair co-occurrence in the parallel corpus. The savings can be quite substantial (up to 90%) and cause no reduc- tion in BLEU score. In some …...

The Conference on Computational Natural Language Learning features a shared task, in which participants train and test their learn- ing systems on the same data sets. In 2007, as in 2006, the shared task has been devoted to dependency parsing, this year with both a multilingual track and a domain …...

We use a generative history-based model to predict the most likely derivation of a de- pendency parse. Our probabilistic model is based on Incremental Sigmoid Belief Net- works, a recently proposed class of la- tent variable models for structure predic- tion. Their ability to automatically in- duce features results in …...

We describe a two-stage optimization of the MaltParser system for the ten languages in the multilingual track of the CoNLL 2007 shared task on dependency parsing. The first stage consists in tuning a single-parser system for each language by optimizing pa- rameters of the parsing algorithm, the fea- ture model, …...

This paper describes ETK (Ensemble of Transformation-based Keys) a new algo- rithm for inducing search keys for name filtering. ETK has the low computational cost and ability to filter by phonetic sim- ilarity characteristic of phonetic keys such as Soundex, but is adaptable to alternative similarity models. The accuracy of …...

We present a probabilistic model of di- achronic phonology in which individual word forms undergo stochastic edits along the branches of a phylogenetic tree. Our ap- proach allows us to achieve three goals with a single unified model: (1) reconstruction of both ancient and modern word forms, (2) discovery of …...

We present a maximally streamlined approach to learning HMM-based acoustic models for automatic speech recognition. In our approach, an initial mono- phone HMM is iteratively refined using a split-merge EM procedure which makes no assumptions about subphone structure or context-dependent structure, and which uses only a single Gaussian per HMM …...

In this paper, we describe a two-stage multi- lingual dependency parser used for the mul- tilingual track of the CoNLL 2007 shared task. The system consists of two compo- nents: an unlabeled dependency parser us- ing Gibbs sampling which can incorporate sentence-level (global) features as well as token-level (local) features, …...

Deterministic dependency parsers use pars- ing actions to construct dependencies. These parsers do not compute the probabil- ity of the whole dependency tree. They only determine parsing actions stepwisely by a trained classifier. To globally model parsing actions of all steps that are taken on the input sentence, we propose …...

We present an extension of phrase-based statistical machine translation models that enables the straight-forward integration of additional annotation at the word-level — may it be linguistic markup or automati- cally generated word classes. In a num- ber of experiments we show that factored translation models lead to better transla- tion …...

Unknown words are a well-known hindrance to natural language applications. In particu- lar, they drastically impact machine transla- tion quality. An easy way out commercial translation systems usually offer their users is the possibility to add unknown words and their translations into a dedicated lex- icon. Recently, Stroppa and Yvon …...

This paper reports on the benefits of large- scale statistical language modeling in ma- chine translation. A distributed infrastruc- ture is proposed which we use to train on up to 2 trillion tokens, resulting in language models having up to 300 billion n-grams. It is capable of providing smoothed probabil- …...

In scientific literature, sentences that cite re- lated work can be a valuable resource for applications such as summarization, syn- onym identification, and entity extraction. In order to determine which equivalent en- tities are discussed in the various citation sentences, we propose aligning the words within these sentences according to …...

In this paper, we address the problem of extracting data records and their attributes from unstructured biomedical full text. There has been little effort reported on this in the research community. We argue that semantics is important for record extraction or finer-grained language processing tasks. We derive a data record …...

Query segmentation is the process of tak- ing a user’s search-engine query and di- viding the tokens into individual phrases or semantic units. Identification of these query segments can potentially improve both document-retrieval precision, by first returning pages which contain the exact query segments, and document-retrieval re- call, by allowing …...

Speech recognition transcripts are far from perfect; they are not of sufficient quality to be useful on their own for spoken document retrieval. This is especially the case for con- versational speech. Recent efforts have tried to overcome this issue by using statistics from speech lattices instead of only the …...

This paper proposes a framework for semi-supervised structured output learning (SOL), specifically for sequence labeling, based on a hybrid generative and discrim- inative approach. We define the objective function of our hybrid model, which is writ- ten in log-linear form, by discriminatively combining discriminative structured predic- tor(s) with generative model(s) …...

In text categorization, term selection is an important step for the sake of both cate- gorization accuracy and computational ef- ficiency. Different dimensionalities are ex- pected under different practical resource re- strictions of time or space. Traditionally in text categorization, the same scoring or ranking criterion is adopted for all …...

We achieved a state of the art performance in statistical machine translation by using a large number of features with an online large-margin training algorithm. The mil- lions of parameters were tuned only on a small development set consisting of less than 1K sentences. Experiments on Arabic-to- English translation indicated …...

We compare and contrast the strengths and weaknesses of a syntax-based machine translation model with a phrase-based ma- chine translation model on several levels. We briefly describe each model, highlight- ing points where they differ. We include a quantitative comparison of the phrase pairs that each model has to work …...