Articles

Animacy detection is a problem whose solu- tion has been shown to be beneficial for a number of syntactic and semantic tasks. We present a state-of-the-art system for this task which uses a number of simple classifiers with heterogeneous data sources in a voting scheme. We show how this framework …...

We present a unified unsupervised statistical model for text normalization. The relation- ship between standard and non-standard to- kens is characterized by a log-linear model, permitting arbitrary features. The weights of these features are trained in a maximum- likelihood framework, employing a novel se- quential Monte Carlo training algorithm to …...

We present a cognitive model of early lexi- cal acquisition which jointly performs word segmentation and learns an explicit model of phonetic variation. We define the model as a Bayesian noisy channel; we sample segmen- tations and word forms simultaneously from the posterior, using beam sampling to control the size …...

Children learn various levels of linguistic structure concurrently, yet most existing mod- els of language acquisition deal with only a single level of structure, implicitly assum- ing a sequential learning process. Developing models that learn multiple levels simultane- ously can provide important insights into how these levels might interact synergistically …...

Distant supervision is a scheme to generate noisy training data for relation extraction by aligning entities of a knowledge base with text. In this work we combine the output of a discriminative at-least-one learner with that of a generative hierarchical topic model to re- duce the noise in distant supervision …...

Since many applications such as timeline sum- maries and temporal IR involving temporal analysis rely on document timestamps, the task of automatic dating of documents has been increasingly important. Instead of using feature-based methods as conventional mod- els, our method attempts to date documents in a year level by exploiting …...

In this paper we classify the temporal relations between pairs of events on an article-wide ba- sis. This is in contrast to much of the exist- ing literature which focuses on just event pairs which are found within the same or adjacent sentences. To achieve this, we leverage on dis- …...

In this position paper, I discuss some linguistic problems that computational work on lexical semantics has attempted to address in the past and the implications for alternative models which in- corporate distributional information. I concentrate in particular on phenomena involving count/mass distinctions, where older approaches attempted to use lexical semantics …...

Using data from Reddy et al. (2011), we present a series of regression models of semantic trans- parency in compound nouns. The results indicate that the frequencies of the compound constituents, the semantic relation between the constituents, and metaphorical shift of a constituent or of the com- pound as a …...

Researchers working on distributional semantics have recently taken up the challenge of going beyond lexical meaning and tackle the issue of compositionality. Several Compositional Distribu- tional Semantics Models (CDSMs) have been developed and promising results have been obtained in evaluations carried out against data sets of small phrases and as …...

This paper gives a detailed description of the ACT (Accuracy of Connective Trans- lation) metric, a reference-based metric that assesses only connective translations. ACT relies on automatic word-level align- ment (using GIZA++) between a source sentence and respectively the reference and candidate translations, along with other heuristics for comparing translations …...

The Discriminative Word Lexicon (DWL) is a maximum-entropy model that pre- dicts the target word probability given the source sentence words. We present two ways to extend a DWL to improve its abil- ity to model the word translation probabil- ity in a phrase-based machine translation (PBMT) system. While DWLs …...

We propose a novel unsupervised word alignment model based on the Hidden Markov Tree (HMT) model. Our model assumes that the alignment variables have a tree structure which is isomorphic to the target dependency tree and models the dis- tortion probability based on the source de- pendency tree, thereby incorporating …...

We apply multi-rate HMMs, a tree struc- tured HMM model, to the word-alignment problem. Multi-rate HMMs allow us to model reordering at both the morpheme level and the word level in a hierarchical fashion. This approach leads to better ma- chine translation results than a morpheme- aware model that does …...

Sentence alignment is an important step in the preparation of parallel data. Most aligners do not perform very well when the input is a noisy, rather than a highly- parallel, document pair. Evaluating align- ers under noisy conditions would seem to require creating an evaluation dataset by manually annotating a …...

We present a method for inference in hi- erarchical phrase-based translation, where both optimisation and sampling are per- formed in a common exact inference framework related to adaptive rejection sampling. We also present a first imple- mentation of that method along with ex- perimental results shedding light on some fundamental …...

We introduce a lexicalized reordering model for hierarchical phrase-based ma- chine translation. The model scores mono- tone, swap, and discontinuous phrase ori- entations in the manner of the one pre- sented by Tillmann (2004). While this type of lexicalized reordering model is a valuable and widely-used component of standard phrase-based …...

This paper presents a dependency- constrained hierarchical machine transla- tion model that uses Moses open-source toolkit for rule extraction and decoding. Experiments are carried out for the German-English language pair in both di- rections for projective and non-projective dependencies. We examine effects on SCFG size and automatic evaluation results when …...

Despite being closely related languages, German and English are characterized by important word order differences. Long- range reordering of verbs, in particular, represents a real challenge for state-of-the- art SMT systems and is one of the main reasons why translation quality is often so poor in this language pair. In …...

In this paper, we propose a novel syntac- tic based MT evaluation metric which only employs the dependency information in the source side. Experimental results show that our method achieves higher correla- tion with human judgments than BLEU, TER, HWCM and METEOR at both sen- tence and system level for …...