Articles

This paper describes our contribution to the CoNLL 2012 Shared Task.1 We present a novel decoding algorithm for coreference res- olution which is combined with a standard pair-wise coreference resolver in a stacking approach. The stacked decoders are evaluated on the three languages of the Shared Task. We obtain an …...

We describe a machine learning system based on large margin structure perceptron for unre- stricted coreference resolution that introduces two key modeling techniques: latent corefer- ence trees and entropy guided feature induc- tion. The proposed latent tree modeling turns the learning problem computationally feasi- ble. Additionally, using an automatic feature …...

The CoNLL-2012 shared task involved pre- dicting coreference in English, Chinese, and Arabic, using the final version, v5.0, of the OntoNotes corpus. It was a follow-on to the English-only task organized in 2011. Un- til the creation of the OntoNotes corpus, re- sources in this sub-field of language process- ing …...

This paper describes the structure of the LTH coreference solver used in the closed track of the CoNLL 2012 shared task (Pradhan et al., 2012). The solver core is a mention classifier that uses Soon et al. (2001)’s algorithm and features extracted from the dependency graphs of the sentences. This …...

Transition-based dependency parsers are often forced to make attachment deci- sions at a point when only partial infor- mation about the relevant graph configu- ration is available. In this paper, we de- scribe a model that takes into account com- plete structures as they become available to rescore the elements …...

In this paper we describe our participation in the contributed task at ACL Special work- shop 2012. We contribute to the goal of en- riching the textual content of ACL Anthology by identifying the citation contexts in a paper and linking them to their corresponding ref- erences in the bibliography …...

We describe how paperXML, a logical docu- ment structure markup for scholarly articles, is generated on the basis of OCR tool out- puts. PaperXML has been initially developed for the ACL Anthology Searchbench. The main purpose was to robustly provide uni- form access to sentences in ACL Anthology papers from …...

Extracting textual content and document structure from PDF presents a surprisingly (depressingly, to some, in fact) difficult chal- lenge, owing to the purely display-oriented de- sign of the PDF document standard. While a variety of lower-level PDF extraction toolk- its exist, none fully support the recovery of original text (in …...

Studies of computational models of language acquisition depend to a large part on the input available for experiments. In this paper, we study the effect that input size has on the per- formance of word segmentation models embodying different kinds of linguistic assumptions. Because currently available corpora for word segmentation …...

Text-to-scene conversion requires knowledge about how actions and locations are expressed in language and realized in the world. To provide this knowlege, we are creating a lexical resource (VigNet) that extends FrameNet by creating a set of intermediate frames (vignettes) that bridge between the high-level semantics of FrameNet frames and …...

We introduce a discriminatively trained, glob- ally normalized, log-linear variant of the lex- ical translation models proposed by Brown et al. (1993). In our model, arbitrary, non- independent features may be freely incorpo- rated, thereby overcoming the inherent limita- tion of generative models, which require that features be sensitive to …...

Grapheme-to-phoneme conversion (G2P) of names is an important and challenging prob- lem. The correct pronunciation of a name is often reflected in its transliterations, which are expressed within a different phonological in- ventory. We investigate the problem of us- ing transliterations to correct errors produced by state-of-the-art G2P systems. We …...

Summarizing and analyzing Twitter content is an important and challenging task. In this pa- per, we propose to extract topical keyphrases as one way to summarize Twitter. We propose a context-sensitive topical PageRank method for keyword ranking and a probabilistic scor- ing function that considers both relevance and interestingness of …...

This paper describes a novel technique for in- corporating syntactic knowledge into phrase- based machine translation through incremen- tal syntactic parsing. Bottom-up and top- down parsers typically require a completed string as input. This requirement makes it dif- ficult to incorporate them into phrase-based translation, which generates partial hypothe- sized …...

Extensive knowledge bases of entailment rules between predicates are crucial for applied se- mantic inference. In this paper we propose an algorithm that utilizes transitivity constraints to learn a globally-optimal set of entailment rules for typed predicates. We model the task as a graph learning problem and suggest meth- ods …...

The automatic extraction of comparative in- formation is an important text mining problem and an area of increasing interest. In this paper, we study how to build a Korean comparison mining system. Our work is composed of two consecutive tasks: 1) classifying comparative sentences into different types and 2) mining …...

Nested event structures are a common occur- rence in both open domain and domain spe- cific extraction tasks, e.g., a “crime” event can cause a “investigation” event, which can lead to an “arrest” event. However, most cur- rent approaches address event extraction with highly local models that extract each event …...

We present a novel method for record extrac- tion from social streams such as Twitter. Un- like typical extraction setups, these environ- ments are characterized by short, one sentence messages with heavily colloquial speech. To further complicate matters, individual mes- sages may not express the full relation to be uncovered, …...

The role of search queries, as available within query sessions or in isolation from one an- other, in examined in the context of ranking the class labels (e.g., brazilian cities, busi- ness centers, hilly sites) extracted from Web documents for various instances (e.g., rio de janeiro). The co-occurrence of a …...

Text mining and data harvesting algorithms have become popular in the computational lin- guistics community. They employ patterns that specify the kind of information to be har- vested, and usually bootstrap either the pat- tern learning or the term harvesting process (or both) in a recursive cycle, using data learned …...