Solving headswitching translation cases in LFG-DOT

It has been shown that LFG-MT (Kaplan et al., 1989) has difficulties with Headswitching data (Sadler et al., 1989, 1990; Sadler & Thompson, 1991). We revisit these arguments in this paper. Despite attempts at solving these problematic constructions using approaches based on linear logic (Van Genabith et al., 1998) and restriction (Kaplan & Wedekind, 1993), we point out further problems which are introduced. We then show how LFG-DOP (Bod & Kaplan, 1998) can be extended to serve as a novel hybrid model for MT, LFG-DOT (Way, 1999, 2001), which promises to improve upon the DOT model of translation (Poutsma 1998, 2000) as well as LFG-MT. LFG-DOT improves the robustness of LFG-MT through the use of the LFG-DOP Discard operator, which produces generalized fragments by discarding certain f-structure features. LFG-DOT can, therefore, deal with ill-formed or previously unseen input where LFG-MT cannot. Finally, we demonstrate that LFG-DOT can cope with such translational phenomena which prove problematic for other LFG-based models of translation

Seeing the wood for the trees: data-oriented translation

Data-Oriented Translation (DOT), which is based on Data-Oriented Parsing (DOP), comprises an experience-based approach to translation, where new translations are derived with reference to grammatical analyses of previous translations. Previous DOT experiments [Poutsma, 1998, Poutsma, 2000a, Poutsma, 2000b] were small in scale because important advances in DOP technology were not incorporated into the translation model. Despite this, related work [Way, 1999, Way, 2003a, Way, 2003b] reports that DOT models are viable in that solutions to ‘hard’ translation cases are readily available. However, it has not been shown to date that DOT models scale to larger datasets. In this work, we describe a novel DOT system, inspired by recent advances in DOP parsing technology. We test our system on larger, more complex corpora than have been used heretofore, and present both automatic and human evaluations which show that high quality translations can be achieved at reasonable speeds

Structured parameter estimation for LFG-DOP using Backoff

Despite its state-of-the-art performance, the Data Oriented Parsing (DOP) model has been shown to suffer from biased parameter estimation, and the good performance seems more the result of ad hoc adjustments than correct probabilistic generalization over the data. In recent work, we developed a new estimation procedure, called Backoff Estimation, for DOP models that are based on Phrase-Structure annotations (so called Tree-DOP models). Backoff Estimation deviates from earlier methods in that it treats the model parameters as a highly structured space of correlated events (backoffs), rather than a set of disjoint events. In this paper we show that the problem of biased estimates also holds for DOP models that are based on Lexical-Functional Grammar annotations (i.e. LFG-DOP), and that the LFG-DOP parameters also constitute a hierarchically structured space. Subsequently, we adapt the Backoff Estimation algorithm from Tree-DOP to LFG-DOP models. Backoff Estimation turns out to be a natural solution to some of the specific problems of robust parsing under LFGDOP

Disambiguation strategies for data-oriented translation

The Data-Oriented Translation (DOT) model { originally proposed in (Poutsma, 1998, 2003) and based on Data-Oriented Parsing (DOP) (e.g. (Bod, Scha, & Sima'an, 2003)) { is best described as a hybrid model of translation as it combines examples, linguistic information and a statistical translation model. Although theoretically interesting, it inherits the computational complexity associated with DOP. In this paper, we focus on one computational challenge for this model: efficiently selecting the `best' translation to output. We present four different disambiguation strategies in terms of how they are implemented in our DOT system, along with experiments which investigate how they compare in terms of accuracy and efficiency

Linguistic Constraints in LFG-DOP

LFG-DOP (Bod and Kaplan, 1998, 2003) provides an appealing answer to the question of how probabilistic methods can be incorporated into linguistic theory. However, despite its attractions, the standard model of LFG-DOP suffers from serious problems of overgeneration, because (a) it is unable to define fragments of the right level of generality, and (b) it has no way of capturing the effect of anything except simple positive constraints. We show how the model can be extended to overcome these problems. The question of how probabilistic methods should be incorporated into linguistic theory is important from both a practical, grammar engineering, perspective, and from the perspective of ‘pure ’ linguistic theory. From a practical point of view such techniques are essential if a system is to achieve a useful breadth of coverag

GF-DOP: grammatical feature data-oriented parsing

This paper proposes an extension of Tree-DOP which approximates the LFG-DOP model. GF-DOP combines the robustness of the DOP model with some of the linguistic competence of LFG. LFG c-structure trees are augmented with LFG functional information, with the aim of (i) generating more informative parses than Tree-DOP; (ii) improving overall parse ranking by modelling grammatical features; and (iii) avoiding the inconsistent probability models of LFG-DOP. In a number of experiments on the HomeCentre corpus, we report on which (groups of) features most heavily influence parse quality, both positively and negatively

Data-oriented models of parsing and translation

The merits of combining the positive elements of the rule-based and data-driven approaches to MT are clear: a combined model has the potential to be highly accurate, robust, cost-effective to build and adaptable. While the merits are clear, however, how best to combine these techniques into a model which retains the positive characteristics of each approach, while inheriting as few of the disadvantages as possible, remains an unsolved problem. One possible solution to this challenge is the Data-Oriented Translation (DOT) model originally proposed by Poutsma (1998, 2000, 2003), which is based on Data-Oriented Parsing (DOP) (e.g. (Bod, 1992; Bod et al., 2003)) and combines examples, linguistic information and a statistical translation model. In this thesis, we seek to establish how the DOT model of translation relates to the other main MT methodologies currently in use. We find that this model differs from other hybrid models of MT in that it inextricably interweaves the philosophies of the rule-based, example-based and statistical approaches in an integrated framework. Although DOT embodies many positive characteristics on a theoretical level, it also inherits the computational complexity associated with DOP. Previous experiments assessing the performance of the DOT model of translation were small in scale and the training data used was not ideally suited to the task (Poutsma, 2000, 2003). However, the algorithmic limitations of the DOT implementation used to perform these experiments prevented a more informative assessment from being carried out. In this thesis, we look to the innovative solutions developed to meet the challenges of implementing the DOP model, and investigate their application to DOT. This investigation culminates in the development of a DOT system; this system allows us to perform translation experiments which are on a larger scale and incorporate greater translational complexity than heretofore. Our evaluation indicates that the positive characteristics of the model identified on a theoretical level are also in evidence when it is subjected to empirical assessment. For example, in terms of exact match accuracy, the DOT model outperforms an SMT model trained and tested on the same data by up to 89.73%. The DOP and DOT models for which we provide empirical evaluations assume contextfree phrase-structure tree representations. However, such models can also be developed for more sophisticated linguistic formalisms. In this thesis, we also focus on the efforts which have been made to integrate the representations of Lexical-Functional Grammar (LFG) with DOP and DOT. We investigate the usefulness of the algorithms developed for DOP (and adapted here to Tree-DOT) when implementing the (more complex) LFG-DOP and LFG-DOT models. We examine how constraints are employed in these models for more accurate disambiguation and seek an alternative methodology for improved constraint specification. We also hypothesise as to how the constraints used to predict both good parses and good translations might be pruned in a motivated fashion. Finally, we explore the relationship between translational equivalence and limited generalisation reusability for both the tree-based and LFG-based DOT models, focussing on how this relationship differs depending on which formalism is assumed

Information Extraction using Context-free Grammatical Inference from Positive Examples

Information extraction from textual data has various applications, such as semantic search. Learning from positive example have theoretical limitations, for many useful applications (including natural languages), substantial part of practical structure (CFG) can be captured by framework introduced in this paper. Our approach to automate identification of structural information is based on grammatical inference. This paper mainly introduces the Context-free Grammar learning from positive examples. We aim to extract Information from unstructured and semi-structured document using Grammatical Inference. DOI: 10.17762/ijritcc2321-8169.15064

D4.1. Technologies and tools for corpus creation, normalization and annotation

The objectives of the Corpus Acquisition and Annotation (CAA) subsystem are the acquisition and processing of monolingual and bilingual language resources (LRs) required in the PANACEA context. Therefore, the CAA subsystem includes: i) a Corpus Acquisition Component (CAC) for extracting monolingual and bilingual data from the web, ii) a component for cleanup and normalization (CNC) of these data and iii) a text processing component (TPC) which consists of NLP tools including modules for sentence splitting, POS tagging, lemmatization, parsing and named entity recognition