Articles

The training of most syntactic SMT approaches involves two essential components, word alignment and monolingual parser. In the current state of the art these two components are mutually independent, thus causing problems like lack of rule generalization, and violation of syntactic correspondence in translation rules. In this paper, we propose …...

This paper proposes a method for learning a discriminative parser for machine trans- lation reordering using only aligned par- allel text. This is done by treating the parser’s derivation tree as a latent variable in a model that is trained to maximize re- ordering accuracy. We demonstrate that efficient large-margin …...

In this paper, we investigate different usages of feature representations in the web person name disambiguation task which has been suf- fering from the mismatch of vocabulary and lack of clues in web environments. In litera- ture, the latter receives less attention and re- mains more challenging. We explore the …...

Past work on learning part-of-speech taggers from tag dictionaries and raw data has re- ported good results, but the assumptions made about those dictionaries are often unrealistic: due to historical precedents, they assume ac- cess to information about labels in the raw and test sets. Here, we demonstrate ways to …...

Supervised hierarchical topic modeling and unsupervised hierarchical topic modeling are usually used to obtain hierarchical topics, such as hLLDA and hLDA. Supervised hierarchi- cal topic modeling makes heavy use of the in- formation from observed hierarchical labels, but cannot explore new topics; while unsu- pervised hierarchical topic modeling is able …...

Quote extraction and attribution is the task of automatically extracting quotes from text and attributing each quote to its correct speaker. The present state-of-the-art system uses gold standard information from previous decisions in its features, which, when removed, results in a large drop in performance. We treat the problem as …...

Many NLP tasks make predictions that are in- herently coupled to syntactic relations, but for many languages the resources required to pro- vide such syntactic annotations are unavail- able. For others it is unclear exactly how much of the syntactic annotations can be ef- fectively leveraged with current models, and …...

We examine the task of resolving complex cases of definite pronouns, specifically those for which traditional linguistic constraints on coreference (e.g., Binding Constraints, gender and number agreement) as well as commonly-used resolution heuristics (e.g., string-matching facilities, syntactic salience) are not useful. Being able to solve this task has broader implications …...

This paper proposes cross-lingual language modeling for transcribing source resource- poor languages and translating them into tar- get resource-rich languages if necessary. Our focus is to improve the speech recognition performance of low-resource languages by leveraging the language model statistics from resource-rich languages. The most challeng- ing work of cross-lingual …...

We present a method for training a semantic parser using only a knowledge base and an un- labeled text corpus, without any individually annotated sentences. Our key observation is that multiple forms of weak supervision can be combined to train an accurate semantic parser: semantic supervision from a knowledge base, …...

We propose an adaptive ensemble method to adapt coreference resolution across domains. This method has three features: (1) it can op- timize for any user-specified objective mea- sure; (2) it can make document-specific pre- diction rather than rely on a fixed base model or a fixed set of base models; …...

This paper describes a study on the impact of the original signal (text, speech, visual scene, event) of a text pair on the task of both man- ual and automatic sub-sentential paraphrase acquisition. A corpus of 2,500 annotated sen- tences in English and French is described, and performance on this …...

We propose a novel probabilistic technique for modeling and extracting salient struc- ture from large document collections. As in clustering and topic modeling, our goal is to provide an organizing perspective into otherwise overwhelming amounts of infor- mation. We are particularly interested in revealing and exploiting relationships be- tween documents. …...

The task of inferring the native language of an author based on texts written in a second language has generally been tackled as a clas- sification problem, typically using as features a mix of n-grams over characters and part of speech tags (for small and fixed n) and un- igram …...

We present a new family of models for unsu- pervised parsing, Dependency and Boundary models, that use cues at constituent bound- aries to inform head-outward dependency tree generation. We build on three intuitions that are explicit in phrase-structure grammars but only implicit in standard dependency formu- lations: (i) Distributions of …...

This paper explores log-based query expan- sion (QE) models for Web search. Three lexicon models are proposed to bridge the lexical gap between Web documents and user queries. These models are trained on pairs of user queries and titles of clicked documents. Evaluations on a real world data set show …...

This paper presents a comparative study of graph-based approaches for cross-domain sentiment classification. In particular, the paper analyses two existing methods: an optimisation problem and a ranking algorithm. We compare these graph-based methods with each other and with the other state-of- the-art approaches and conclude that graph domain representations offer …...

Graph-based dependency parsers suffer from the sheer number of higher order edges they need to (a) score and (b) consider during opti- mization. Here we show that when working with LP relaxations, large fractions of these edges can be pruned before they are fully scored—without any loss of optimality guar- …...

We propose a new semantic orientation, Ex- citation, and its automatic acquisition method. Excitation is a semantic property of predicates that classifies them into excitatory, inhibitory and neutral. We show that Excitation is useful for extracting contradiction pairs (e.g., destroy cancer ⊥ develop cancer) and causality pairs (e.g., increase in …...

This paper presents a paraphrase acquisition method that uncovers and exploits generali- ties underlying paraphrases: paraphrase pat- terns are first induced and then used to col- lect novel instances. Unlike existing methods, ours uses both bilingual parallel and monolin- gual corpora. While the former are regarded as a source of …...