Articles

State of the art set expansion algorithms pro- duce varying quality expansions for different entity types. Even for the highest quality ex- pansions, errors still occur and manual re- finements are necessary for most practical uses. In this paper, we propose algorithms to aide this refinement process, greatly reducing the …...

This paper introduces a novel unsupervised constraint-driven learning algorithm for iden- tifying named-entity (NE) transliterations in bilingual corpora. The proposed method does not require any annotated data or aligned cor- pora. Instead, it is bootstrapped using a simple resource – a romanization table. We show that this resource, when used …...

Domain adaptation is an important problem in named entity recognition (NER). NER classi- fiers usually lose accuracy in the domain trans- fer due to the different data distribution be- tween the source and the target domains. The major reason for performance degrading is that each entity type often has lots …...

We address a text regression problem: given a piece of text, predict a real-world continuous quantity associated with the text’s meaning. In this work, the text is an SEC-mandated finan- cial report published annually by a publicly- traded company, and the quantity to be pre- dicted is volatility of stock …...

A wish is “a desire or hope for something to happen.” In December 2007, people from around the world offered up their wishes to be printed on confetti and dropped from the sky during the famous New Year’s Eve “ball drop” in New York City’s Times Square. We present an …...

We introduce a novel precedence reordering approach based on a dependency parser to sta- tistical machine translation systems. Similar to other preprocessing reordering approaches, our method can efficiently incorporate linguis- tic knowledge into SMT systems without in- creasing the complexity of decoding. For a set of five subject-object-verb (SOV) order …...

We propose a novel probabilistic syn- choronous context-free grammar formalism for statistical machine translation, in which syntactic nonterminal labels are represented as “soft” preferences rather than as “hard” matching constraints. This formalism allows us to efficiently score unlabeled synchronous derivations without forgoing traditional syntactic constraints. Using this score as a …...

We use the Margin Infused Relaxed Algo- rithm of Crammer et al. to add a large num- ber of new features to two machine transla- tion systems: the Hiero hierarchical phrase- based translation system and our syntax-based translation system. On a large-scale Chinese- English translation task, we obtain statistically significant …...

Morphological segmentation breaks words into morphemes (the basic semantic units). It is a key component for natural language pro- cessing systems. Unsupervised morphologi- cal segmentation is attractive, because in ev- ery language there are virtually unlimited sup- plies of text, but very few labeled resources. However, most existing model-based systems …...

Computational processing of text exchanged in interactive venues in which participants en- gage in simultaneous conversations can bene- fit from techniques for automatically grouping overlapping sequences of messages into sepa- rate conversations, a problem known as “dis- entanglement.” While previous methods ex- ploit both lexical and non-lexical information that exists …...

We propose a unified approach to web search query alterations in Japanese that is not lim- ited to particular character types or ortho- graphic similarity between a query and its al- teration candidate. Our model is based on pre- vious work on English query correction, but makes some crucial improvements: …...

In this paper, we propose a method for learn- ing reordering model for BTG-based statisti- cal machine translation (SMT). The model focuses on linguistic features from bilingual phrases. Our method involves extracting reor- dering examples as well as features such as part-of-speech and word class from aligned parallel sentences. The …...

This paper introduces a new approach to rank- ing speech utterances by a system’s confi- dence that they contain a spoken word. Multi- ple alternate pronunciations, or degradations, of a query word’s phoneme sequence are hy- pothesized and incorporated into the ranking function. We consider two methods for hy- pothesizing …...

We propose a principled probabilisitc frame- work which uses trees over the vocabulary to capture similarities among terms in an infor- mation retrieval setting. This allows the re- trieval of documents based not just on occur- rences of specific query terms, but also on sim- ilarities between terms (an effect …...

The tree-transducer grammars that arise in current syntactic machine translation systems are large, flat, and highly lexicalized. We ad- dress the problem of parsing efficiently with such grammars in three ways. First, we present a pair of grammar transformations that admit an efficient cubic-time CKY-style parsing algorithm despite leaving most …...

In this paper we present a Markov Logic Net- work for Semantic Role Labelling that jointly performs predicate identification, frame dis- ambiguation, argument identification and ar- gument classification for all predicates in a sentence. Empirically we find that our ap- proach is competitive: our best model would appear on par …...

An important part of textual inference is mak- ing deductions involving monotonicity, that is, determining whether a given assertion en- tails restrictions or relaxations of that asser- tion. For instance, the statement ‘We know the epidemic spread quickly’ does not entail ‘We know the epidemic spread quickly via fleas’, but …...

In current phrase-based SMT systems, more training data is generally better than less. However, a larger data set eventually intro- duces a larger model that enlarges the search space for the translation problem, and con- sequently requires more time and more re- sources to translate. We argue redundant in- formation …...

We describe a generative model for clustering named entities which also models named en- tity internal structure, clustering related words by role. The model is entirely unsupervised; it uses features from the named entity itself and its syntactic context, and coreference in- formation from an unsupervised pronoun re- solver. The …...

Current phrase-based statistical machine translation systems process each test sentence in isolation and do not enforce global consis- tency constraints, even though the test data is often internally consistent with respect to topic or style. We propose a new consistency model for machine translation in the form of a graph-based …...