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

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Does Gaussian Elimination Teach About Uninterpretable Feature Elimination in CHL?

Are uninterpretable features (uF) similar to vanishing unknowns, such as or in linear algebra? Does Gaussian elimination (GE) teach us anything about uF elimination (uFE) in the computational procedures of human natural language (CHL)? It may teach us nothing. It may be pointless to compare GE to uFE. To answer the question, we compare GE with uFE and raise questions that have not been posed seriously and perform a toy experiment of linear algebraic uFE with and weighted by coefficients and constants, which express the ratio (symmetry degree) of to in the probe (P) and goal (G). The three types of solvability in GE parallel those in uFE. Namely, ① a unique solution (successful GE), ② no solution (failed GE), and ③ infinitely many solutions (failed GE) respectively correspond to ① complete AGREE, ② incomplete AGREE, and ③ internal merge. ③ is recycled in uFE, but not in GE. The experiment answers (or deepens) many uFE puzzles. We also investigate whether graph theory teaches us anything about P, G, and sentence structures.departmental bulletin pape

Evolution, Perfection, and Theories of Language

In this article it is argued that evolutionary plausibility must be made an important constraining factor when building theories of language. Recent suggestions that presume that language is necessarily a perfect or optimal system are at odds with this position, evolutionary theory showing us that evolution is a meliorizing agent often producing imperfect solutions. Perfection of the linguistic system is something that must be demonstrated, rather than presumed. Empirically, examples of imperfection are found not only in nature and in human cognition, but also in language — in the form of ambiguity, redundancy, irregularity, movement, locality conditions, and extra-grammatical idioms. Here it is argued that language is neither perfect nor optimal, and shown how theories of language which place these proper-ties at their core run into both conceptual and empirical problems

On past participle agreement in transitive clauses in French

This paper provides a Minimalist analysis of past participle agreement in French in transitive clauses. Our account posits that the head v of vP in such structures carries an (accusativeassigning) structural case feature which may apply (with or without concomitant agreement) to case-mark a clause-mate object, the subject of a defective complement clause, or an intermediate copy of a preposed subject in spec-CP. In structures where a goal is extracted from vP (e.g. via wh-movement) v also carries an edge feature, and may also carry a specificity feature and a set of (number and gender) agreement features. We show how these assumptions account for agreement of a participle with a preposed specific clausemate object or defective-clause subject, and for the absence of agreement with an embedded object, with the complement of an impersonal verb, and with the subject of an embedded (finite or nonfinite) CP complement. We also argue that the absence of agreement marking (in expected contexts) on the participles faitmade and laissélet in infinitive structures is essentially viral in nature. Finally, we claim that obligatory participle agreement with reflexive and reciprocal objects arises because the derivation of reflexives involves A-movement and concomitant agreement