Articles

fast align is a simple, fast, and effi- cient approach for word alignment based on the IBM model 2. fast align per- forms well for language pairs with rel- atively similar word orders; however, it does not perform well for language pairs with drastically different word orders. We propose a …...

We introduce a new approach to argumen- tation mining that we applied to a parallel German/English corpus of short texts an- notated with argumentation structure. We focus on structure prediction, which we break into a number of subtasks: relation identification, central claim identification, role classification, and function classifica- tion. Our …...

toIn this paper we present the first ever, the best of our knowledge, dis- course parser for multi-party chat dia- logues. Discourse in multi-party dia- logues dramatically differs from mono- logues since threaded conversations are commonplace rendering prediction of the discourse structure compelling. Moreover, the fact that our data come …...

Neural networks have been shown to improve performance across a range of natural-language tasks. However, design- ing and training them can be complicated. Frequently, researchers resort to repeated experimentation to pick optimal settings. In this paper, we address the issue of choosing the correct number of units in hidden layers. …...

Comparison is one of the most impor- tant phenomena in language for express- ing objective and subjective facts about various entities. Systems that can under- stand and reason over comparative struc- ture can play a major role in the applica- tions which require deeper understanding of language. In this paper …...

We ask how much information a human translator adds to an original text, and we provide a bound. We address this ques- tion in the context of bilingual text com- pression: given a source text, how many bits of additional information are required to specify the target text produced by …...

Extracting named entities in text and link- ing extracted names to a given knowledge base are fundamental tasks in applications for text understanding. Existing systems typically run a named entity recognition (NER) model to extract entity names first, then run an entity linking model to link ex- tracted names to …...

We investigate dual decomposition for joint MAP inference of many strings. Given an arbitrary graphical model, we de- compose it into small acyclic sub-models, whose MAP configurations can be found by finite-state composition and dynamic programming. We force the solutions of these subproblems to agree on overlap- ping variables, by …...

We propose FINET, a system for detect- ing the types of named entities in short inputs—such as sentences or tweets—with respect to WordNet’s super fine-grained type system. FINET generates candidate types using a sequence of multiple extrac- tors, ranging from explicitly mentioned types to implicit types, and subsequently selects the …...

This paper proposes a novel hierarchical recurrent neural network language model (HRNNLM) for document modeling. Af- ter establishing a RNN to capture the co- herence between sentences in a documen- t, HRNNLM integrates it as the sentence history information into the word level RNN to predict the word sequence with …...

We present a novel model for the task of joint mention extraction and classifi- cation. Unlike existing approaches, our model is able to effectively capture over- lapping mentions with unbounded lengths. The model is highly scalable, with a time complexity that is linear in the number of words in the …...

Cross-document co-reference resolution (CCR) computes equivalence classes over textual mentions denoting the same entity in a document corpus. Named-entity link- ing (NEL) disambiguates mentions onto entities present in a knowledge base (KB) or maps them to null if not present in the KB. Traditionally, CCR and NEL have been addressed …...

We present a general framework for com- paring multiple groups of documents. A bipartite graph model is proposed where document groups are represented as one node set and the comparison criteria are represented as the other node set. Using this model, we present basic algorithms to extract insights into similarities …...

Multi-label text categorization is a type of text categorization, where each document is assigned to one or more categories. Re- cently, a series of methods have been de- veloped, which train a classifier for each label, organize the classifiers in a partially ordered structure and take predictions pro- duced by …...

Distributed word representations are very useful for capturing semantic information and have been successfully applied in a variety of NLP tasks, especially on En- glish. In this work, we innovatively de- velop two component-enhanced Chinese character embedding models and their bi- gram extensions. Distinguished from En- glish word embeddings, our …...

We present an unsupervised method to find lexical variations in Roman Urdu informal text. Our method includes a phonetic algorithm UrduPhone, a feature- based similarity function, and a clustering algorithm Lex-C. UrduPhone encodes ro- man Urdu strings to their phonetic equiv- alent representations. This produces an initial grouping of different …...

This paper presents a new algorithm to automatically solve algebra word prob- lems. Our algorithm solves a word prob- lem via analyzing a hypothesis space con- taining all possible equation systems gen- erated by assigning the numbers in the word problem into a set of equation sys- tem templates extracted …...

Wikipedia is the largest collection of ency- clopedic data ever written in the history of humanity. Thanks to its coverage and its availability in machine-readable format, it has become a primary resource for large- scale research in historical and cultural studies. In this work, we focus on the subset of …...

This paper addresses text categorization problem that training data may derive from a different time period from the test data. We present a learning framework which extends a boosting technique to learn accurate model for timeline adapta- tion. The results showed that the method was comparable to the current state-of-the- …...

Recently, neural network based sentence modeling methods have achieved great progress. Among these methods, the re- cursive neural networks (RecNNs) can ef- fectively model the combination of the words in sentence. However, RecNNs need a given external topological struc- ture, like syntactic tree. In this paper, we propose a gated …...