Articles

 A semantic class is a collection of items (words or phrases) which have semantically peer or sibling relationship. This paper studies the employment of topic models to automati- cally construct semantic classes, taking as the source data a collection of raw semantic classes (RASCs), which were extracted by ap- …...

This paper describes the extraction from Wikipedia of lexical reference rules, iden- tifying references to term meanings trig- gered by other terms. We present extrac- tion methods geared to cover the broad range of the lexical reference relation and analyze them extensively. Most extrac- tion methods yield high precision levels, …...

We describe a novel method for the task of unsupervised POS tagging with a dic- tionary, one that uses integer programming to explicitly search for the smallest model that explains the data, and then uses EM to set parameter values. We evaluate our method on a standard test corpus using …...

An important and well-studied problem is the production of semantic lexicons from a large corpus. In this paper, we present a system named ASIA (Automatic Set In- stance Acquirer), which takes in the name of a semantic class as input (e.g., “car makers”) and automatically outputs its in- stances (e.g., …...

Stochastic gradient descent (SGD) uses approximate gradients estimated from subsets of the training data and updates the parameters in an online fashion. This learning framework is attractive because it often requires much less training time in practice than batch training algorithms. However, L1-regularization, which is be- coming popular in natural …...

In this paper, we present a discriminative word-character hybrid model for joint Chi- nese word segmentation and POS tagging. Our word-character hybrid model offers high performance since it can handle both known and unknown words. We describe our strategies that yield good balance for learning the characteristics of known and …...

Cross-lingual tasks are especially difficult due to the compounding effect of errors in language processing and errors in machine translation (MT). In this paper, we present an error analysis of a new cross-lingual task: the 5W task, a sentence-level understanding task which seeks to return the English 5W's (Who, What, …...

inCoreferencing entities across documents a large corpus enables advanced document understanding tasks such as aquestion answering. This paper presents novel cross document coreference approach that leverages the profiles of entities which are constructed by using information extraction tools and reconciled by using a within-document coreference module. We propose to match …...

Recent work on temporal relation iden- tification has focused on three types of relations between events: temporal rela- tions between an event and a time expres- sion, between a pair of events and between an event and the document creation time. These types of relations have mostly been identified in …...

Iterative bootstrapping algorithms are typ- ically compared using a single set of hand- picked seeds. However, we demonstrate that performance varies greatly depend- ing on these seeds, and favourable seeds for one algorithm can perform very poorly with others, making comparisons unreli- able. We exploit this wide variation with bagging, …...

In this paper, we propose a novel system for translating organization names from Chinese to English with the assistance of web resources. Firstly, we adopt a chunking- based segmentation method to improve the segmentation of Chinese organization names which is plagued by the OOV problem. Then a heuristic query construction …...

Pure statistical parsing systems achieves high in-domain accuracy but performs poorly out-domain. In this paper, we propose two different approaches to pro- duce syntactic dependency structures us- ing a large-scale hand-crafted HPSG gram- mar. The dependency backbone of an HPSG analysis is used to provide general linguistic insights which, when …...

In this paper, we propose a novel method for semi-supervised learning of non- projective log-linear dependency parsers using directly expressed linguistic prior knowledge (e.g. a noun’s parent is often a verb). Model parameters are estimated us- ing a generalized expectation (GE) objec- tive function that penalizes the mismatch between model …...

Broad-coverage annotated treebanks nec- essary to train parsers do not exist for many resource-poor languages. The wide availability of parallel text and accurate parsers in English has opened up the pos- sibility of grammar induction through par- tial transfer across bitext. We consider generative and discriminative models for dependency grammar …...

We present a novel transition system for dependency parsing, which constructs arcs only between adjacent words but can parse arbitrary non-projective trees by swapping the order of words in the input. Adding the swapping operation changes the time complexity for deterministic parsing from linear to quadratic in the worst case, …...

We formulate the problem of non- projective dependency parsing as a polynomial-sized integer linear pro- gram. Our formulation is able to handle non-local output features in an efficient manner; not only is it compatible with prior knowledge encoded as hard con- straints, it can also learn soft constraints from data. …...

An efficient decoding algorithm is a cru- cial element of any statistical machine translation system. Some researchers have noted certain similarities between SMT decoding and the famous Traveling Sales- man Problem; in particular (Knight, 1999) has shown that any TSP instance can be mapped to a sub-case of a word-based …...

Hierarchical phrase-based models are at- tractive because they provide a consis- tent framework within which to character- ize both local and long-distance reorder- ings, but they also make it dif�cult to distinguish many implausible reorderings from those that are linguistically plausi- ble. Rather than appealing to annotation- driven syntactic modeling, …...

Existing evaluation metrics for machine translation lack crucial robustness: their correlations with hu- man quality judgments vary considerably across lan- guages and genres. We believe that the main reason is their inability to properly capture meaning: A good translation candidate means the same thing as the reference translation, regardless of …...

A number of approaches to Automatic MT Evaluation based on deep linguistic knowledge have been suggested. How- ever, n-gram based metrics are still to- day the dominant approach. The main reason is that the advantages of employ- ing deeper linguistic information have not been clarified yet. In this work, we …...