Articles

Inspired by previous preprocessing ap- proaches to SMT, this paper proposes a novel, probabilistic approach to reordering which combines the merits of syntax and phrase-based SMT. Given a source sentence and its parse tree, our method generates, by tree operations, an n-best list of re- ordered inputs, which are then …...

This paper presents a Function Word cen- tered, Syntax-based (FWS) solution to ad- dress phrase ordering in the context of statistical machine translation (SMT). Mo- tivated by the observation that function words often encode grammatical relation- ship among phrases within a sentence, we propose a probabilistic synchronous gram- mar to …...

In this paper, we propose forest-to-string rules to enhance the expressive power of tree-to-string translation models. A forest- to-string rule is capable of capturing non- syntactic phrase pairs by describing the cor- respondence between multiple parse trees and one string. To integrate these rules into tree-to-string translation models, auxil- iary …...

Even in a massive corpus such as the Web, a substantial fraction of extractions appear in- frequently. This paper shows how to assess the correctness of sparse extractions by uti- lizing unsupervised language models. The REALM system, which combines HMM- based and n-gram-based language models, ranks candidate extractions by the …...

This paper addresses the issue of text nor- malization, an important yet often over- looked problem in natural language proc- essing. By text normalization, we mean converting ‘informally inputted’ text into the canonical form, by eliminating ‘noises’ in the text and detecting paragraph and sen- tence boundaries in the text. …...

In this paper we present methods for im- proving the disambiguation of noun phrase (NP) coordination within the framework of a lexicalised history-based parsing model. As well as reducing noise in the data, we look at modelling two main sources of information for disambiguation: symmetry in conjunct structure, and the …...

Language model (LM) adaptation is im- portant for both speech and language processing. It is often achieved by com- bining a generic LM with a topic-specific model that is more relevant to the target document. Unlike previous work on un- supervised LM adaptation, this paper in- vestigates how effectively using …...

Current research in text mining favours the quantity of texts over their quality. But for bilingual terminology mining, and for many language pairs, large comparable corpora are not available. More importantly, as terms are defined vis-à-vis a specific domain with a restricted register, it is expected that the quality rather …...

A character-based measure of similarity is an important component of many natu- ral language processing systems, including approaches to transliteration, coreference, word alignment, spelling correction, and the identification of cognates in related vocabu- laries. We propose an alignment-based dis- criminative framework for string similarity. We gather features from substring pairs …...

We propose a novel algorithm for English to Persian transliteration. Previous meth- ods proposed for this language pair apply a word alignment tool for training. By contrast, we introduce an alignment algo- rithm particularly designed for translitera- tion. Our new model improves the English to Persian transliteration accuracy by 14% …...

Most current machine transliteration sys- tems employ a corpus of known source- target word pairs to train their system, and typically evaluate their systems on a similar corpus. In this paper we explore the perfor- mance of transliteration systems on corpora that are varied in a controlled way. In partic- …...

We introduce a framework for syntactic parsing with latent variables based on a form of dynamic Sigmoid Belief Networks called Incremental Sigmoid Belief Networks. We demonstrate that a previous feed-forward neural network parsing model can be viewed as a coarse approximation to inference with this class of graphical model. By …...

We present a novel framework that com- bines strengths from surface syntactic pars- ing and deep syntactic parsing to increase deep parsing accuracy, specifically by com- bining dependency and HPSG parsing. We show that by using surface dependencies to constrain the application of wide-coverage HPSG rules, we can benefit from …...

Dependency analysis of natural language has gained importance for its applicability to NLP tasks. Non-projective structures are common in dependency analysis, there- fore we need fine-grained means of describ- ing them, especially for the purposes of machine-learning oriented approaches like parsing. We present an evaluation on twelve languages which explores …...

Many errors produced by unsupervised and semi-supervised relation extraction (RE) systems occur because of wrong recogni- tion of entities that participate in the rela- tions. This is especially true for systems that do not use separate named-entity rec- ognition components, instead relying on general-purpose shallow parsing. Such sys- tems have …...

Extraction of relations between entities is an important part of Information Extraction on free text. Previous methods are mostly based on statistical correlation and depend- ency relations between entities. This paper re-examines the problem at the multi- resolution layers of phrase, clause and sen- tence using dependency and discourse rela- …...

A minimally supervised machine learning framework is described for extracting rela- tions of various complexity. Bootstrapping starts from a small set of n-ary relation in- stances as “seeds”, in order to automati- cally learn pattern rules from parsed data, which then can extract new instances of the relation and its …...

Creating large amounts of annotated data to train statistical PCFG parsers is expensive, and the performance of such parsers declines when training and test data are taken from different domains. In this paper we use self- training in order to improve the quality of a parser and to adapt it …...

We present a new approach to relation ex- traction that requires only a handful of train- ing examples. Given a few pairs of named entities known to exhibit or not exhibit a particular relation, bags of sentences con- taining the pairs are extracted from the web. We extend an existing …...

This paper addresses the automatic classifi- cation of semantic relations in noun phrases based on cross-linguistic evidence from a set of five Romance languages. A set of novel semantic and contextual English– Romance NP features is derived based on empirical observations on the distribution of the syntax and meaning of …...