Articles

Recognizing semantic relations between sentences, such as entailment and contradiction, is a challenging task that requires detailed analysis of the interaction between diverse linguistic phenomena. In this paper, we propose a latent discriminative model that unifies a statistical framework and a theory of Natural Logic to capture complex interactions between …...

Cross-language information retrieval today is dominated by techniques that rely principally on context-independent token-to-token mappings despite the fact that state-of-the-art statistical machine translation systems now have far richer translation models available in their internal representations. This paper explores combination-of-evidence techniques using three types of statistical translation models: context-independent token translation, …...

We describe a domain-specific method of adapting conditional random fields (CRFs) to mor- phosyntactic tagging of highly-inflectional languages. The solution involves extending CRFs with additional, position-wise restrictions on the output domain, which are used to impose con- sistency between the modeled label sequences and morphosyntactic analysis results both at the …...

The annotations of explicit and implicit discourse connectives in the Penn Discourse Treebank make it possible to investigate on a large scale how different types of discourse relations are expressed. Assuming an account of the Uniform Information Density hypothesis, we expect that discourse relations should be expressed explicitly with a …...

In this paper, we revisit the problem of language identification with the focus on proper discrimination between closely related languages. Strong similarities between certain languages make it very hard to classify them correctly using standard methods that have been proposed in the literature. Dedicated models that focus on specific discrimination …...

The current paper evaluates the performance of the PRESEMT methodology, which facilitates the creation of machine translation (MT) systems for different language pairs. This methodology aims to develop a hybrid MT system that extracts translation information from large, predominantly monolingual corpora, using pattern recognition techniques. PRESEMT has been designed to …...

Reducing the reliance of semantic role labeling (SRL) methods on human-annotated data has become an active area of research. However, the prior work has largely focused on either (1) looking into ways to improve supervised SRL systems by producing surrogate annotated data and reducing sparsity of lexical features or (2) …...

In this paper we present work on the task of Native Language Identification (NLI). We present an alternative corpus to the ICLE which has been used in most work up until now. We believe that our corpus, TOEFL11, is more suitable for the task of NLI and will allow researchers …...

This paper describes our coreference resolu- tion system for the CoNLL-2012 shared task. Our system is based on the Stanford’s dcore- f deterministic system which applies multiple sieves with the order from high precision to low precision to generate coreference chains. We introduce the newly added constraints and sieves and …...

Coreference resolution, which aims at correctly linking meaningful expressions in text, is a much challenging problem in Natural Language Processing community. This paper describes the multilingual coreference modeling system of Web Information Processing Group, Henan University of Technology, China, for the CoNLL-2012 shared task (closed track). The system takes a …...

This paper describes the UniTN/Essex sub- mission to the CoNLL-2012 Shared Task on the Multilingual Coreference Resolution. We have extended our CoNLL-2011 submission, based on BART, to cover two additional lan- guages, Arabic and Chinese. This paper fo- cuses on adapting BART to new languages, discussing the problems we have …...

The CoNLL-2012 shared task is an extension of the last year’s coreference task. We partici- pated in the closed track of the shared tasks in both years. In this paper, we present the im- provements of Illinois-Coref system from last year. We focus on improving mention detec- tion and pronoun …...

This paper presents HITS’ coreference reso- lution system that participated in the CoNLL- 2012 shared task on multilingual unrestricted coreference resolution. Our system employs a simple multigraph representation of the rela- tion between mentions in a document, where the nodes correspond to mentions and the edges correspond to relations between …...

This paper describes a coreference resolution system for CONLL 2012 shared task developed by HLT_HITSZ group, which incorporates rule-based and statistic-based techniques. The system performs coreference resolution through the mention pair classification and linking. For each detected mention pairs in the text, a Decision Tree (DT) based binary classifier is …...

We in this paper present the model for our participation (BCMI) in the CoNLL-2012 Shared Task. This paper describes a pure rule-based method, which assembles dif- ferent filters in a proper order. Different filters handle different situations and the filtering strategies are designed manually. These filters are assigned to different …...

The current work presents the participa- tion of UBIU (Zhekova and K¨ubler, 2010) in the CoNLL-2012 Shared Task: Model- ing Multilingual Unrestricted Coreference in OntoNotes (Pradhan et al., 2012). Our system deals with all three languages: Arabic, Chi- nese and English. The system results show that UBIU works reliably across …...

This paper describes our system participat- ing in the CoNLL-2012 shared task: Mod- eling Multilingual Unrestricted Coreference in Ontonotes. Maximum entropy models are used for our system as classifiers to deter- mine the coreference relationship between ev- ery two mentions (usually noun phrases and pronouns) in each document. We exploit …...

This paper presents a mixed deterministic model for coreference resolution in the CoNLL-2012 shared task. We separate the two main stages of our model, mention detection and coreference resolution, into several sub-tasks which are solved by machine learning method and deterministic rules based on multi-filters, such as lexical, syntactic, semantic, …...

In this paper, we present our system de- scription for the CoNLL-2012 coreference resolution task on English, Chinese and Arabic. We investigate a projection-based model in which we first translate Chinese and Arabic into English, run a publicly available coreference system, and then use a new projection algorithm to map …...

We describe our system for the CoNLL-2012 shared task, which seeks to model corefer- ence in OntoNotes for English, Chinese, and Arabic. We adopt a hybrid approach to coreference resolution, which combines the strengths of rule-based methods and learning- based methods. Our official combined score over all three languages is …...