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643
Opinion Mining and Sentiment Analysis
, 2008
"... An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, active ..."
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Cited by 749 (3 self)
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An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include materialon summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.
The Proposition Bank: An Annotated Corpus of Semantic Roles
- Computational Linguistics
, 2005
"... The Proposition Bank project takes a practical approach to semantic representation, adding a layer of predicate-argument information, or semantic role labels, to the syntactic structures of the Penn Treebank. The resulting resource can be thought of as shallow, in that it does not represent corefere ..."
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Cited by 556 (22 self)
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The Proposition Bank project takes a practical approach to semantic representation, adding a layer of predicate-argument information, or semantic role labels, to the syntactic structures of the Penn Treebank. The resulting resource can be thought of as shallow, in that it does not represent coreference, quantification, and many other higher-order phenomena, but also broad, in that it covers every instance of every verb in the corpus and allows representative statistics to be calculated. We discuss the criteria used to define the sets of semantic roles used in the annotation process and to analyze the frequency of syntactic/semantic alternations in the corpus. We describe an automatic system for semantic role tagging trained on the corpus and discuss the effect on its performance of various types of information, including a comparison of full syntactic parsing with a flat representation and the contribution of the empty ‘‘trace’ ’ categories of the treebank.
ConceptNet: A Practical Commonsense Reasoning Toolkit
- BT TECHNOLOGY JOURNAL
, 2004
"... ConceptNet is a freely available commonsense knowledgebase and natural-language-processing toolkit which supports many practical textual-reasoning tasks over real-world documents including topic-jisting (e.g. a news article containing the concepts, "gun," "convenience store," &qu ..."
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Cited by 343 (7 self)
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ConceptNet is a freely available commonsense knowledgebase and natural-language-processing toolkit which supports many practical textual-reasoning tasks over real-world documents including topic-jisting (e.g. a news article containing the concepts, "gun," "convenience store," "demand money" and "make getaway" might suggest the topics "robbery" and "crime"), affect-sensing (e.g. this email is sad and angry), analogy-making (e.g. "scissors," "razor," "nail clipper," and "sword" are perhaps like a "knife" because they are all "sharp," and can be used to "cut something"), and other contextoriented inferences. The knowledgebase is a semantic network presently consisting of over 1.6 million assertions of commonsense knowledge encompassing the spatial, physical, social, temporal, and psychological aspects of everyday life. Whereas similar large-scale semantic knowledgebases like Cyc and WordNet are carefully handcrafted, ConceptNet is generated automatically from the 700,000 sentences of the Open Mind Common Sense Project -- a World Wide Web based collaboration with over 14,000 authors.
Learning extraction patterns for subjective expressions
- Conference on Empirical Methods in Natural Language Processing (EMNLP-03
, 2003
"... To address this issue, we have been exploring the use of bootstrapping methods to allow subjectivity classifiers to learn from a collection of unannotated texts. Our research uses high-precision subjectivity classifiers to automatically identify subjective and objective sentences in unannotated text ..."
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Cited by 297 (21 self)
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To address this issue, we have been exploring the use of bootstrapping methods to allow subjectivity classifiers to learn from a collection of unannotated texts. Our research uses high-precision subjectivity classifiers to automatically identify subjective and objective sentences in unannotated texts. This process allows us to generate a large set of labeled sentences automatically. The second emphasis of our research is using extraction patterns to represent subjective expressions. These patterns are linguistically richer and more flexible than single words or N-grams. Using the (automatically) labeled sentences as training data, we apply an extraction pattern learning algorithm to automatically generate patterns representing subjective expressions. The learned patterns can be used to automatically identify more subjective sentences, which grows the training set, and the entire process can then be bootstrapped. Our experimental results show that this bootstrapping process increases the recall of the high-
From treebank to propbank
- In Language Resources and Evaluation
, 2002
"... This paper describes our approach to the development of a Proposition Bank, which involves the addition of semantic information to the Penn English Treebank. Our primary goal is the labeling of syntactic nodes with specific argument labels that preserve the similarity of roles such as the window in ..."
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Cited by 265 (14 self)
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This paper describes our approach to the development of a Proposition Bank, which involves the addition of semantic information to the Penn English Treebank. Our primary goal is the labeling of syntactic nodes with specific argument labels that preserve the similarity of roles such as the window in John broke the window and the window broke. After motivating the need for explicit predicate argument structure labels, we briefly discuss the theoretical considerations of predicate argument structure and the need to maintain consistency across syntactic alternations. The issues of consistency of argument structure across both polysemous and synonymous verbs are also discussed and we present our actual guidelines for these types of phenomena, along with numerous examples of tagged sentences and verb frames. Metaframes are introduced as a technique for handling similar frames among near− synonymous verbs. We conclude with a summary of the current status of annotation process. 1.
Creating Subjective and Objective Sentence Classifiers from Unannotated Texts
- INTELLIGENT TEXT PROCESSING (CICLING-05)
, 2005
"... This paper presents the results of developing subjectivity classifiers using only unannotated texts for training. The performance rivals that of previous supervised learning approaches. In addition, we advance the state of the art in objective sentence classification by learning extraction patterns ..."
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Cited by 170 (11 self)
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This paper presents the results of developing subjectivity classifiers using only unannotated texts for training. The performance rivals that of previous supervised learning approaches. In addition, we advance the state of the art in objective sentence classification by learning extraction patterns associated with objectivity and creating objective classifiers that achieve substantially higher recall than previous work with comparable precision.
Learning Subjective Nouns Using Extraction Pattern Bootstrapping
, 2003
"... We explore the idea of creating a subjectivity classifier that uses lists of subjective nouns learned by bootstrapping algorithms. The goal of our research is to develop a system that can distinguish subjective sentences from objective sentences. First, we use two bootstrapping algorithms that ..."
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Cited by 169 (11 self)
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We explore the idea of creating a subjectivity classifier that uses lists of subjective nouns learned by bootstrapping algorithms. The goal of our research is to develop a system that can distinguish subjective sentences from objective sentences. First, we use two bootstrapping algorithms that exploit extraction patterns to learn sets of subjective nouns. Then we train a Naive Bayes classifier using the subjective nouns, discourse features, and subjectivity clues identified in prior research. The bootstrapping algorithms learned over 1000 subjective nouns, and the subjectivity classifier performed well, achieving 77% recall with 81% precision.
Using Predicate-Argument Structures for Information Extraction
- IN PROCEEDINGS OF ACL 2003
, 2003
"... In this paper we present a novel, customizable IE paradigm that takes advantage of predicate-argument structures. We also introduce a new way of automatically identifying predicate argument structures, which is central to our IE paradigm. It is based on: (1) an extended set of features; and ( ..."
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Cited by 158 (4 self)
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In this paper we present a novel, customizable IE paradigm that takes advantage of predicate-argument structures. We also introduce a new way of automatically identifying predicate argument structures, which is central to our IE paradigm. It is based on: (1) an extended set of features; and (2) inductive decision tree learning.
Identifying relations for open information extraction. In:
- Proc. Conference on Empirical Methods in Natural Language Processing (EMNLP),
, 2011
"... Abstract Open Information Extraction (IE) is the task of extracting assertions from massive corpora without requiring a pre-specified vocabulary. This paper shows that the output of state-ofthe-art Open IE systems is rife with uninformative and incoherent extractions. To overcome these problems, we ..."
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Cited by 140 (4 self)
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Abstract Open Information Extraction (IE) is the task of extracting assertions from massive corpora without requiring a pre-specified vocabulary. This paper shows that the output of state-ofthe-art Open IE systems is rife with uninformative and incoherent extractions. To overcome these problems, we introduce two simple syntactic and lexical constraints on binary relations expressed by verbs. We implemented the constraints in the REVERB Open IE system, which more than doubles the area under the precision-recall curve relative to previous extractors such as TEXTRUNNER and WOE pos . More than 30% of REVERB's extractions are at precision 0.8 or highercompared to virtually none for earlier systems. The paper concludes with a detailed analysis of REVERB's errors, suggesting directions for future work.
Just how mad are you? Finding strong and weak opinion clauses
- In Proceedings of AAAI
, 2004
"... identification and extraction of opinions and emotions in text. ..."
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Cited by 131 (2 self)
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identification and extraction of opinions and emotions in text.
