Articles

Dirichlet process mixture model (DPM- M) has great potential for detecting the underlying structure of data. Extensive studies have applied it for text cluster- ing in terms of topics. However, due to the unsupervised nature, the topic cluster- s are always less satisfactory. Considering that people often have some prior …...

Cooking recipes exist in abundance; but due to their unstructured text format, they are hard to study quantitatively beyond treating them as simple bags of words. In this paper, we propose an ingredient- instruction dependency tree data structure to represent recipes. The proposed rep- resentation allows for more refined com- …...

Characters are fundamental to literary analysis. Current approaches are heav- ily reliant on NER to identify characters, causing many to be overlooked. We pro- pose a novel technique for character detec- tion, achieving significant improvements over state of the art on multiple datasets. 1...

A key challenge introduced by the re- cent SpaceEval shared task on spatial re- lation extraction is the identification of MOVELINKs, a type of spatial relation in which up to eight spatial elements can participate. To handle the complexity of extracting MOVELINKs, we combine two ideas that have been successfully …...

Improving the search and browsing ex- perience in PubMed® is a key compo- nent in helping users detect information of interest. In particular, when explor- ing a novel field, it is important to pro- vide a comprehensive view for a specific subject. One solution for providing this panoramic picture is …...

Distantly supervised approaches have be- come popular in recent years as they allow training relation extractors without text- bound annotation, using instead known relations from a knowledge base and a large textual corpus from an appropri- ate domain. While state of the art dis- tant supervision approaches use off-the- shelf …...

Out-of-vocabulary name errors in speech recognition create significant problems for downstream language processing, but the fact that they are rare poses challenges for automatic detection, particularly in an open-domain scenario. To address this problem, a multi-task recurrent neural net- work language model for sentence-level name detection is proposed for use …...

We present KB-UNIFY, a novel approach for integrating the output of different Open Information Extraction systems into a single unified and fully disambiguated knowledge repository. KB-UNIFY con- sists of three main steps: (1) disambigua- tion of relation argument pairs via a sense- based vector representation and a large unified sense …...

This paper addresses the problem of corpus-level entity typing, i.e., inferring from a large corpus that an entity is a member of a class such as “food” or “artist”. The application of entity typ- ing we are interested in is knowledge base completion, specifically, to learn which classes an entity …...

This paper introduces a convolutional sen- tence kernel based on word embeddings. Our kernel overcomes the sparsity issue that arises when classifying short docu- ments or in case of little training data. Ex- periments on six sentence datasets showed statistically significant higher accuracy over the standard linear kernel with n- …...

Representation learning of knowledge bases aims to embed both entities and relations into a low-dimensional space. Most existing methods only consider direct relations in representation learning. We argue that multiple-step relation paths also contain rich inference patterns be- tween entities, and propose a path-based representation learning model. This model considers …...

Elementary-level science exams pose sig- nificant knowledge acquisition and rea- soning challenges for automatic question answering. We develop a system that rea- sons with knowledge derived from text- books, represented in a subset of first- order logic. Automatic extraction, while scalable, often results in knowledge that is incomplete and noisy, …...

ListNet is a well-known listwise learning to rank model and has gained much atten- tion in recent years. A particular problem of ListNet, however, is the high computa- tion complexity in model training, main- ly due to the large number of object per- mutations involved in computing the gra- dients. …...

Knowledge Base Population (KBP) tasks, such as slot filling, show the particular importance of entity-oriented automatic relevant document acquisition. Rich, diverse and reliable relevant documents satisfy the fundamental require- ment that a KBP system explores the nature of an entity. Towards the bottleneck problem be- tween comprehensiveness and definiteness of …...

Target entity disambiguation (TED), the task of identifying target entities of the same domain, has been recognized as a critical step in various important applica- tions. In this paper, we propose a graph- based model called TremenRank to collec- tively identify target entities in short texts given a name list …...

This paper introduces the task of question- answer driven semantic role labeling (QA-SRL), where question-answer pairs are used to represent predicate-argument structure. For example, the verb “intro- duce” in the previous sentence would be labeled with the questions “What is in- troduced?”, and “What introduces some- thing?”, each paired with …...

Understanding entailment and contradic- tion is fundamental to understanding nat- ural language, and inference about entail- ment and contradiction is a valuable test- ing ground for the development of seman- tic representations. However, machine learning research in this area has been dra- matically limited by the lack of large-scale resources. …...

Linking named mentions detected in a source document to an existing knowl- edge base provides disambiguated entity referents for the mentions. This al- lows better document analysis, knowl- edge extraction and knowledge base pop- ulation. Most of the previous research extensively exploited the linguistic fea- tures of the source documents …...

A fundamental issue in opinion mining is to search a corpus for opinion units, each of which typically comprises the evalua- tion by an author for a target object from an aspect, such as “This hotel is in a good location”. However, few attempts have been made to address cases …...

Web reviews have been intensively studied in argumentation-related tasks such as sen- timent analysis. However, due to their fo- cus on content-based features, many sen- timent analysis approaches are effective only for reviews from those domains they have been specifically modeled for. This paper puts its focus on domain indepen- …...