Articles

Previous research on domain adaptation (DA) for statistical machine translation (SMT) has mainly focused on the translation model (TM) and the language model (LM). To the best of our knowledge, there is no previous work on reordering model (RM) adaptation for phrase- based SMT. In this paper, we demonstrate that …...

Inferring the information structure of scien- tific documents is useful for many down- stream applications. Existing feature-based machine learning approaches to this task re- quire substantial training data and suffer from limited performance. Our idea is to guide feature-based models with declarative domain knowledge encoded as posterior distribution constraints. We …...

Coreference resolution systems rely heav- ily on string overlap (e.g., Google Inc. and Google), performing badly on mentions with very different words (opaque mentions) like Google and the search giant. Yet prior at- tempts to resolve opaque pairs using ontolo- gies or distributional semantics hurt precision more than improved recall. …...

We present the first work on antecedent se- lection for bridging resolution without restric- tions on anaphor or relation types. Our model integrates global constraints on top of a rich local feature set in the framework of Markov logic networks. The global model improves over the local one and both …...

In a meeting, it is often desirable to extract keywords from each utterance as soon as it is spoken. Thus, this paper proposes a just-in- time keyword extraction from meeting tran- scripts. The proposed method considers two major factors that make it different from key- word extraction from normal texts. …...

In natural language question answering (QA) systems, questions often contain terms and phrases that are critically important for re- trieving or finding answers from documents. We present a learnable system that can ex- tract and rank these terms and phrases (dubbed mandatory matching phrases or MMPs), and demonstrate their utility …...

Our goal is to extract answers from pre- retrieved sentences for Question Answering (QA). We construct a linear-chain Conditional Random Field based on pairs of questions and their possible answer sentences, learning the association between questions and answer types. This casts answer extraction as an an- swer sequence tagging problem …...

Traditional relation extraction seeks to iden- tify pre-specified semantic relations within natural language text, while open Information Extraction (Open IE) takes a more general ap- proach, and looks for a variety of relations without restriction to a fixed relation set. With this generalization comes the question, what is a relation? …...

In this paper we explore the potential of quantum theory as a formal framework for capturing lexical meaning. We present a novel semantic space model that is syntacti- cally aware, takes word order into account, and features key quantum aspects such as superposition and entanglement. We define a dependency-based Hilbert …...

We examine the task of temporal relation clas- sification. Unlike existing approaches to this task, we (1) classify an event-event or event- time pair as one of the 14 temporal relations defined in the TimeBank corpus, rather than as one of the six relations collapsed from the original 14; (2) …...

In natural-language discourse, related events tend to appear near each other to describe a larger scenario. Such structures can be formal- ized by the notion of a frame (a.k.a. template), which comprises a set of related events and prototypical participants and event transitions. Identifying frames is a prerequisite for infor- …...

We consider the problem of translating natu- ral language text queries into regular expres- sions which represent their meaning. The mis- match in the level of abstraction between the natural language representation and the regu- lar expression representation make this a novel and challenging problem. However, a given regular expression …...

This paper presents UoS, a graph-based Word Sense Induction system which attempts to find all applicable senses of a target word given its context, grading each sense accord- ing to its suitability to the context. Senses of a target word are induced through use of a non-parameterised, linear-time clustering al- …...

In this paper, we present our approach to SemEval-2013 Task 9.2. It is a feature rich classification using LIBSVM for Drug-Drug Interactions detection in the BioMedical do- main. The features are extracted considering morphosyntactic, lexical and semantic con- cepts. Tools like openDMAP and TEES are used to extract semantic concepts …...

We describe our system for the DDIExtraction-2013 shared task of classifying Drug-Drug interactions (DDIs) given labeled drug mentions. The challenge called for a five-way classification of all drug pairs in each sentence: a drug pair is either non-interacting, or interacting as one of four types. Our approach begins with the …...

Automatic relation extraction provides great support for scientists and database curators in dealing with the extensive amount of biomed- ical textual data. The DDIExtraction 2013 challenge poses the task of detecting drug- drug interactions and further categorizing them into one of the four relation classes. We present our machine learning …...

For participating in the SemEval 2013 chal- lenge of recognition and classification of drug names, we adapted our chemical en- tity recognition approach consisting in Condi- tional Random Fields for recognizing chemi- cal terms and lexical similarity for entity res- olution to the ChEBI ontology. We obtained promising results, with …...

The DDIExtraction 2013 task in the SemEval conference concerns the detection of drug names and statements of drug-drug interac- tions (DDI) from text. Extraction of DDIs is important for providing up-to-date knowl- edge on adverse interactions between co- administered drugs. We apply the machine learning based Turku Event Extraction Sys- …...

A drug-drug interaction (DDI) occurs when one drug affects the level or activity of another drug. Semeval 2013 DDI Extraction challenge is going to be held with the aim of identify- ing the state of the art relation extraction algo- rithms. In this paper we firstly review some of the …...

This work describes the participation of the WBI-DDI team on the SemEval 2013 – Task 9.2 DDI extraction challenge. The task con- sisted of extracting interactions between pairs of drugs from two collections of documents (DrugBank and MEDLINE) and their clas- sification into four subtypes: advise, effect, mechanism, and int. …...