Articles

We present LEMMING, a modular log- linear model that jointly models lemmati- zation and tagging and supports the inte- gration of arbitrary global features. It is trainable on corpora annotated with gold standard tags and lemmata and does not rely on morphological dictionaries or an- alyzers. LEMMING sets the new …...

Domain adaptation is a challenge for super- vised NLP systems because of expensive and time-consuming manual annotated re- sources. We present a novel method to adapt a supervised coreference resolution system trained on newswire to short narra- tive stories without retraining the system. The idea is to perform inference via …...

Recursive neural models, which use syn- tactic parse trees to recursively generate representations bottom-up, are a popular architecture. However there have not been rigorous evaluations showing for exactly which tasks this syntax-based method is appropriate. In this paper, we benchmark recursive neural models against sequential recurrent neural models, enforcing apples- …...

This paper aims to find errors that lead to dialogue breakdowns in chat-oriented dia- logue systems. We collected chat dialogue data, annotated them with dialogue break- down labels, and collected comments de- scribing the error that led to the break- down. By mining the comments, we first identified error types. …...

If intelligent systems are to interact with humans in a natural manner, the ability to describe daily life activities is impor- tant. To achieve this, sensing human ac- tivities by capturing multimodal informa- tion is necessary. In this study, we con- sider a smart environment for sensing ac- tivities with …...

We present a method of using cohesion to improve discourse element identification for sentences in student essays. New fea- tures for each sentence are derived by con- sidering its relations to global and local cohesion, which are created by means of cohesive resources and subtopic coverage. In our experiments, we …...

This paper presents a study on the role of discourse markers in argumentative dis- course. We annotated a German cor- pus with arguments according to the com- mon claim-premise model of argumen- tation and performed various statistical analyses regarding the discriminative na- ture of discourse markers for claims and premises. …...

Implicit discourse relation recognition re- mains a serious challenge due to the ab- sence of discourse connectives. In this pa- per, we propose a Shallow Convolutional Neural Network (SCNN) for implicit dis- course relation recognition, which con- tains only one hidden layer but is effec- tive in relation recognition. The …...

While most previous work on Wikification has focused on written texts, this paper presents a Wikification approach for spo- ken dialogues. A set of analyzers are pro- posed to learn dialogue-specific properties along with domain knowledge of conver- sations from Wikipedia. Then, the an- alyzed properties are used as constraints …...

Discourse structure is the hidden link be- tween surface features and document-level properties, such as sentiment polarity. We show that the discourse analyses produced by Rhetorical Structure Theory (RST) parsers can improve document-level senti- ment analysis, via composition of local in- formation up the discourse tree. First, we show that …...

Many discourse relations are explicitly marked with discourse connectives, and these examples could potentially serve as a plentiful source of training data for recog- nizing implicit discourse relations. How- ever, there are important linguistic differ- ences between explicit and implicit dis- course relations, which limit the accuracy of such an …...

This paper presents a detailed compar- ative framework for assessing the use- fulness of unsupervised word representa- tions for identifying so-called implicit dis- course relations. Specifically, we compare standard one-hot word pair representations against low-dimensional ones based on Brown clusters and word embeddings. We also consider various word vector combi- …...

For annotation tasks involving independent judgments, probabilistic models have been used to infer ground truth labels from data where a crowd of many annotators labels the same items. Such models have been shown to produce results superior to taking the majority vote, but have not been applied to sequential data. …...

A microblog repost tree provides strong clues on how an event described therein develops. To help social media users capture the main clues of events on mi- croblogging sites, we propose a novel re- post tree summarization framework by ef- fectively differentiating two kinds of mes- sages on repost trees …...

State of the art in opinion mining mainly focuses on positive and negative senti- ment summarisation of online customer reviews. We observe that reviewers tend to provide advice, recommendations and tips to the fellow customers on a variety of points of interest. In this work, we target the automatic detection …...

We present novel experiments in model- ing the rise and fall of story characteristics within narrative, leading up to the Most Reportable Event (MRE), the compelling event that is the nucleus of the story. We construct a corpus of personal narratives from the bulletin board website Reddit, using the organization …...

Twitter is often used in quantitative stud- ies that identify geographically-preferred topics, writing styles, and entities. These studies rely on either GPS coordinates at- tached to individual messages, or on the user-supplied location field in each profile. In this paper, we compare these data ac- quisition techniques and quantify the …...

Analyzing arguments in user-generated Web discourse has recently gained atten- tion in argumentation mining, an evolving field of NLP. Current approaches, which employ fully-supervised machine learn- ing, are usually domain dependent and suffer from the lack of large and diverse annotated corpora. However, annotating arguments in discourse is costly, error- …...

Arabic dialect classification has been an important and challenging problem for Arabic language processing, especially for social media text analysis and machine translation. In this paper we propose an approach to improving Arabic dialect classification with semi-supervised learn- ing: multiple classifiers are trained with weakly supervised, strongly supervised, and unsupervised …...

In this work, we improve the performance of intra-sentential zero anaphora resolu- tion in Japanese using a novel method of recognizing subject sharing relations. In Japanese, a large portion of intra- sentential zero anaphora can be regarded as subject sharing relations between pred- icates, that is, the subject of some …...