Public relations will continue to transform, and the changes you see
are monumental. For better or for worse, a career in PR means handling
communications in the public spotlight because of the increasing use of
social media. In the wake of democratized content and businesses satisfying
the needs of the digitally connected consumer, PR had to evolve with
a new approach. This approach required a shift in thinking, from strategy
and planning all the way through to implementation and measurement.
We present a novel system that helps nonexperts ﬁnd sets of similar words. The user begins by specifying one or more seed words. The system then iteratively suggests a series of candidate words, which the user can either accept or reject. Current techniques for this task typically bootstrap a classiﬁer based on a ﬁxed seed set. In contrast, our system involves the user throughout the labeling process, using active learning to intelligently explore the space of similar words.
We present a novel model to represent and assess the discourse coherence of text. Our model assumes that coherent text implicitly favors certain types of discourse relation transitions. We implement this model and apply it towards the text ordering ranking task, which aims to discern an original text from a permuted ordering of its sentences.
Dialogue act classification is a central challenge for dialogue systems. Although the importance of emotion in human dialogue is widely recognized, most dialogue act classification models make limited or no use of affective channels in dialogue act classification. This paper presents a novel affect-enriched dialogue act classifier for task-oriented dialogue that models facial expressions of users, in particular, facial expressions related to confusion.
Named entity disambiguation is the task of linking an entity mention in a text to the correct real-world referent predeﬁned in a knowledge base, and is a crucial subtask in many areas like information retrieval or topic detection and tracking. Named entity disambiguation is challenging because entity mentions can be ambiguous and an entity can be referenced by different surface forms.
The Chinese language is characterized by the lack of formal devices such as morphological tense and number that often provide important clues for syntactic processing tasks. While state-of-theart tagging systems have achieved accuracies above 97% on English, Chinese POS tagging has proven to be more challenging and obtained accuracies about 93-94% (Tseng et al., 2005b; Huang et al., 2007, 2009; Li et al., 2011).
Machine learning approaches have been developed to address relation extraction, which is the task of extracting semantic relations between entities expressed in text. Supervised approaches are limited in scalability because labeled data is expensive to produce. A particularly attractive approach, called distant supervision (DS), creates labeled data by heuristically aligning entities in text with those in a knowledge base, such as Freebase (Mintz et al., 2009).
Coreferencing entities across documents in a large corpus enables advanced document understanding tasks such as question answering. This paper presents a novel cross document coreference approach that leverages the proﬁles of entities which are constructed by using information extraction tools and reconciled by using a within-document coreference module. We propose to match the proﬁles by using a learned ensemble distance function comprised of a suite of similarity specialists.
Modern models of relation extraction for tasks like ACE are based on supervised learning of relations from small hand-labeled corpora. We investigate an alternative paradigm that does not require labeled corpora, avoiding the domain dependence of ACEstyle algorithms, and allowing the use of corpora of any size. Our experiments use Freebase, a large semantic database of several thousand relations, to provide distant supervision.
Creating labeled training data for relation extraction is expensive. In this paper, we study relation extraction in a special weakly-supervised setting when we have only a few seed instances of the target relation type we want to extract but we also have a large amount of labeled instances of other relation types. Observing that different relation types can share certain common structures, we propose to use a multi-task learning method coupled with human guidance to address this weakly-supervised relation extraction problem. ...
This paper describes an empirical study of the “Information Synthesis” task, deﬁned as the process of (given a complex information need) extracting, organizing and inter-relating the pieces of information contained in a set of relevant documents, in order to obtain a comprehensive, non redundant report that satisﬁes the information need.
Kernel based methods dominate the current trend for various relation extraction tasks including protein-protein interaction (PPI) extraction. PPI information is critical in understanding biological processes. Despite considerable efforts, previously reported PPI extraction results show that none of the approaches already known in the literature is consistently better than other approaches when evaluated on different benchmark PPI corpora.
This paper studies textual inference by investigating comma structures, which are highly frequent elements whose major role in the extraction of semantic relations has not been hitherto recognized. We introduce the problem of comma resolution, deﬁned as understanding the role of commas and extracting the relations they imply.
Relation extraction is the task of finding semantic relations between two entities from text. In this paper, we propose a novel feature-based Chinese relation extraction approach that explicitly defines and explores nine positional structures between two entities. We also suggest some correction and inference mechanisms based on relation hierarchy and co-reference information etc. The approach is effective when evaluated on the ACE 2005 Chinese data set.
Searching for a person name in a Web Search Engine usually leads to a number of web pages that refer to several people sharing the same name. In this paper we study whether it is reasonable to assume that pages about the desired person can be ﬁltered by the user by adding query terms. Our results indicate that, although in most occasions there is a query reﬁnement that gives all and only those pages related to an individual, it is unlikely that the user is able to ﬁnd this expression a priori. ...
Many algorithms have been developed to harvest lexical semantic resources, however few have linked the mined knowledge into formal knowledge repositories. In this paper, we propose two algorithms for automatically ontologizing (attaching) semantic relations into WordNet. We present an empirical evaluation on the task of attaching partof and causation relations, showing an improvement on F-score over a baseline model. iati
Several NLP tasks are characterized by asymmetric data where one class label NONE, signifying the absence of any structure (named entity, coreference, relation, etc.) dominates all other classes. Classiﬁers built on such data typically have a higher precision and a lower recall and tend to overproduce the NONE class. We present a novel scheme for voting among a committee of classiﬁers that can signiﬁcantly boost the recall in such situations.
Many methods are available for computing semantic similarity between individual words, but certain NLP tasks require the comparison of word pairs. This paper presents a kernel-based framework for application to relational reasoning tasks of this kind. The model presented here combines information about two distinct types of word pair similarity: lexical similarity and relational similarity.
The core of the problem is finding a way of describing the intended referent that distinguishes it from other potential referents with which it might be confused. We refer to this problem as the c o n t e n t d e t e r m i n a t i o n task. In this paper, we point out some limitations in an earlier solution proposed in Dale [1988, 1989], and discuss the possibilites of extending this solution by incorporating a use of constraints motivated by the work of Haddock [1987, 1988].
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