Articles

Active learning is a promising way for sentiment classification to reduce the annotation cost. In this paper, we focus on the imbalanced class distribution scenario for sentiment classification, wherein the number of positive samples is quite different from that of negative samples. This scenario posits new challenges to active learning. …...

Generic rule-based systems for Information Extraction (IE) have been shown to work reasonably well out-of-the-box, and achieve state-of-the-art accuracy with further domain customization. However, it is generally rec- ognized that manually building and customiz- ing rules is a complex and labor intensive pro- cess. In this paper, we discuss an …...

We propose the weakly supervised Multi- Experts Model (MEM) for analyzing the se- mantic orientation of opinions expressed in natural language reviews. In contrast to most prior work, MEM predicts both opinion po- larity and opinion strength at the level of in- dividual sentences; such fine-grained analysis helps to understand …...

Entity Linking (EL) has received considerable attention in recent years. Given many name mentions in a document, the goal of EL is to predict their referent entities in a knowledge base. Traditionally, there have been two distinct directions of EL research: one focusing on the effects of mention’s context compatibility, …...

Existing techniques for disambiguating named entities in text mostly focus on Wikipedia as a target catalog of entities. Yet for many types of entities, such as restaurants and cult movies, relational databases exist that contain far more extensive information than Wikipedia. This paper introduces a new task, called Open-Database Named-Entity …...

Recent work has explored the use of hidden Markov models for unsupervised discourse and conversation modeling, where each seg- ment or block of text such as a message in a conversation is associated with a hidden state in a sequence. We extend this approach to al- low each block of …...

Incremental processing allows system design- ers to address several discourse phenomena that have previously been somewhat neglected in interactive systems, such as backchannels or barge-ins, but that can enhance the re- sponsiveness and naturalness of systems. Un- fortunately, prior work has focused largely on deterministic incremental decision mak- ing, rendering …...

User simulation is frequently used to train statistical dialog managers for task-oriented domains. At present, goal-driven simula- tors (those that have a persistent notion of what they wish to achieve in the dialog) re- quire some task-specific engineering, making them impossible to evaluate intrinsically. In- stead, they have been evaluated …...

Inferring attributes of discourse participants has been treated as a batch-processing task: data such as all tweets from a given author are gathered in bulk, processed, analyzed for a particular feature, then reported as a result of academic interest. Given the sources and scale of material used in these efforts, …...

We present a system for automatic identification of schizophrenic patients and healthy controls based on narratives the subjects recounted about emotional experiences in their own life. The focus of the study is to identify the lexical features that distinguish the two populations. We report the results of feature selection experiments …...

In this paper, we address the problem of build- ing a multilingual transliteration system using an interlingual representation. Our approach uses international phonetic alphabet (IPA) to learn the interlingual representation and thus allows us to use any word and its IPA repre- sentation as a training example. Thus, our ap- …...

This paper proposes a novel method for lex- icon extraction that extracts translation pairs from comparable corpora by using graph- based label propagation. In previous work, it was established that performance drasti- cally decreases when the coverage of a seed lexicon is small. We resolve this problem by utilizing indirect …...

We consider the problem of using a bilingual dictionary to transfer lexico-syntactic infor- mation from a resource-rich source language to a resource-poor target language. In con- trast to past work that used bitexts to trans- fer analyses of specific sentences at the token level, we instead use features to transfer …...

A mixture of positive (friendly) and nega- tive (antagonistic) relations exist among users in most social media applications. However, many such applications do not allow users to explicitly express the polarity of their interac- tions. As a result most research has either ig- nored negative links or was limited to …...

Named Entity Recognition (NER) is a task which helps in finding out Persons name, Location names, Brand names, Abbreviations, Date, Time etc and classifies them into predefined different categories. NER plays a major role in various Natural Language Processing (NLP) fields like Information Extraction, Machine Translations and Question Answering. This …...

We describe an approach to coreference resolution that relies on the intuition that easy decisions should be made early, while harder decisions should be left for later when more information is available. We are inspired by the recent success of the rule-based system of Raghunathan et al. (2010), which relies …...

This paper reports the highest results (95% in MUC and 92% in CoNLL metric) in the litera- ture for Turkish named entity recognition; more specifically for the task of detecting person, location and organization entities in general news texts. We give an in depth analysis of the previous reported results …...

In this paper, we propose a differential evolution (DE) based two-stage evolutionary approach for named entity recognition (NER). The first stage concerns with the problem of relevant feature selection for NER within the frameworks of two popular machine learning algorithms, namely Conditional Random Field (CRF) and Support Vector Machine (SVM). …...

In recent years, statistical parsers have reached high performance levels on well-edited texts. Domain adaptation techniques have improved parsing results on text genres differing from the journalistic data most parsers are trained on. However, such corpora usually comply with standard linguistic, spelling and typographic conventions. In the meantime, the emergence …...

This paper introduces a method to improve supervised word sense disambiguation perfor- mance by including a new class of features which leverage contextual information from large unannotated corpora. This new feature class, selectors, contains words that appear in other corpora with the same local context as a given lexical instance. …...