Articles

We study the issue of porting a known NLP method to a language with little existing NLP resources, specifically Hebrew SVM-based chunking. We introduce two SVM-based methods – Model Tampering and Anchored Learning. These allow fine grained analysis of the learned SVM models, which provides guidance to identify errors in …...

We propose a new, simple model for the auto- matic induction of selectional preferences, using corpus-based semantic similarity metrics. Fo- cusing on the task of semantic role labeling, we compute selectional preferences for seman- tic roles. In evaluations the similarity-based model shows lower error rates than both Resnik’s WordNet-based model …...

We examine the problem of choosing word order for a set of dependency trees so as to minimize total dependency length. We present an algorithm for computing the op- timal layout of a single tree as well as a numerical method for optimizing a gram- mar of orderings over a …...

Convolution tree kernel has shown promis- ing results in semantic role classification. However, it only carries out hard matching, which may lead to over-fitting and less ac- curate similarity measure. To remove the constraint, this paper proposes a grammar- driven convolution tree kernel for semantic role classification by introducing more …...

This paper shows how to use the Unfold- Fold transformation to transform Projective Bilexical Dependency Grammars (PBDGs) into ambiguity-preserving weakly equiva- lent Context-Free Grammars (CFGs). These CFGs can be parsed in O(n3) time using a CKY algorithm with appropriate indexing, rather than the O(n5) time required by a naive encoding. …...

Dependency-based representations of natu- ral language syntax require a fine balance between structural flexibility and computa- tional complexity. In previous work, several constraints have been proposed to identify classes of dependency structures that are well- balanced in this sense; the best-known but also most restrictive of these is projectivity. Most …...

We show that the problems of parsing and sur- face realization for grammar formalisms with “context-free” derivations, coupled with Mon- tague semantics (under a certain restriction) can be reduced in a uniform way to Datalog query evaluation. As well as giving a polynomial- time algorithm for computing all derivation trees …...

Machine translation of a source language sentence involves selecting appropriate tar- get language words and ordering the se- lected words to form a well-formed tar- get language sentence. Most of the pre- vious work on statistical machine transla- tion relies on (local) associations of target words/phrases with source words/phrases for …...

This paper presents a novel application of Alternating Structure Optimization (ASO) to the task of Semantic Role Labeling (SRL) of noun predicates in NomBank. ASO is a recently proposed linear multi-task learn- ing algorithm, which extracts the common structures of multiple tasks to improve accu- racy, via the use of …...

We present a novel method for predicting in- flected word forms for generating morpho- logically rich languages in machine trans- lation. We utilize a rich set of syntactic and morphological knowledge sources from both source and target sentences in a prob- abilistic model, and evaluate their contribu- tion in generating …...

Efficient decoding has been a fundamental problem in machine translation, especially with an integrated language model which is essential for achieving good translation quality. We develop faster approaches for this problem based on k-best parsing algo- rithms and demonstrate their effectiveness on both phrase-based and syntax-based MT systems. In both …...

We present the design and evaluation of a translator’s amenuensis that uses compa- rable corpora to propose and rank non- literal solutions to the translation of expres- sions from the general lexicon. Using dis- tributional similarity and bilingual diction- aries, the method outperforms established techniques for extracting translation equivalents from …...

In this paper, we put forward an information theoretic definition of the redundancy that is observed across the sound inventories of the world’s languages. Through rigorous statis- tical analysis, we find that this redundancy is an invariant property of the consonant in- ventories. The statistical analysis further un- folds that …...

In this paper we investigate named entity transliteration based on a phonetic scoring method. The phonetic method is computed using phonetic features and carefully designed pseudo features. The proposed method is tested with four languages – Arabic, Chinese, Hindi and Korean – and one source language – English, using comparable …...

We present a general framework for automatically extracting social networks and biographical facts from conversational speech. Our approach relies on fusing the output produced by multiple informa- tion extraction modules, including entity recognition and detection, relation detec- tion, and event detection modules. We describe the specific features and algo- rithmic …...

Grapheme-to-phoneme conversion (g2p) is a core component of any text-to-speech sys- tem. We show that adding simple syllab- ification and stress assignment constraints, namely ‘one nucleus per syllable’ and ‘one main stress per word’, to a joint n-gram model for g2p conversion leads to a dramatic improvement in conversion accuracy. …...

Psychiatric document retrieval attempts to help people to efficiently and effectively locate the consultation documents relevant to their depressive problems. Individuals can understand how to alleviate their symp- toms according to recommendations in the relevant documents. This work proposes the use of high-level topic information ex- tracted from consultation documents …...

Automatic segmentation is important for making multimedia archives comprehensi- ble, and for developing downstream infor- mation retrieval and extraction modules. In this study, we explore approaches that can segment multiparty conversational speech by integrating various knowledge sources (e.g., words, audio and video recordings, speaker intention and context). In particu- lar, …...

We present a novel approach to automati- cally annotate images using associated text. We detect and classify all entities (persons and objects) in the text after which we de- termine the salience (the importance of an entity in a text) and visualness (the extent to which an entity can be …...

We present a user requirements study for Question Answering on meeting records that assesses the difficulty of users ques- tions in terms of what type of knowledge is required in order to provide the correct an- swer. We grounded our work on the em- pirical analysis of elicited user queries. …...