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Extracting phrases describing problems with products and services from twitter messages
Narendra Gupta
Conference on Intelligent Text Processing and Computational Linguistics CICling 2013,
2013.
[PDF]
[BIB]
{Social media contains many types of information which is useful to businesses. In this paper we discuss automatic extraction from twitter data the descriptions of problems consumer experience with products and services. We first identify the problem tweets i.e. the tweets containing descriptions of problems. We the extract the phrases that describe the problem. In our approach such descriptions are extracted as a combination of trigger and target phrases. Triggers are mostly verb phrases and are identified by using hand crafted lexical and syntactic patterns. Targets on the other hand are noun phrases related to the triggers. We frame the problem of finding target phrases corresponding to a trigger phrase as a ranking problem and show the results of our experiments with maximum entropy classifier and voted perceptron. Both approaches outperform the rule based approach reported before. We also show that because of inherent limitations of voted perceptron, maximum entropy based ranking are more suitable for our problem.}
EMOTION DETECTION IN EMAIL CUSTOMER CARE
Narendra Gupta, Mazin Gilbert, Giuseppe Di
Computational Intelligence, An international Journal,
2012.
[PDF]
[BIB]
Wiley-Blackwell Copyright
"The definitive version is available at onlinelibrary.wiley.com." , 2012-10-04, http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8640.2012.00454.x/full
{Prompt and knowledgeable responses to customers’ emails are critical in maximizing customer satisfaction.
Such emails, often contain complaints about unfair treatment due to negligence, incompetence, rigid protocols,
unfriendly systems, and unresponsive personnel. In this paper, we refer to these emails as emotional emails. They
provide valuable feedback to improve contact center processes and customer care, as well as, to enhance customer
retention. This paper describes a method for extracting salient features and identifying emotional emails in customer
care. Salient features reflect customer frustration, dissatisfaction with the business, and threats to either leave,
take legal action and/or report to authorities. Compared to a baseline system using word unigrams, our proposed
approach with salient features resulted in absolute F-measure improvement of greater then 20%.}
Extracting descriptions of problems with product and service from twitter data
Narendra Gupta
Proceedings of the 3rd Workshop on Social Web Search and Mining (SWSM2011) in conjunction with SIGIR 2011,
The 3rd Workshop on Social Web Search and Mining (SWSM2011) in conjunction with SIGIR 2011, July 24&,
2011.
[PDF]
[BIB]
There is enough evidence that social media contains timely information that businesses could use to their benefits.
In this paper we discuss automatic extraction of descriptions of problems from twitter data. More specifically we present a system that filters tweets related to an enterprise and extracts descriptions of problems with their product/service. First step of this extraction process is to identify the tweets containing such descriptions. We view this as text classification problem. We propose that sentences describing problems can be characterized by their lexical and syntactic structure. Our experiments show that use of such structural features in classification models, results in the F-measure of 0.742. It is a significant improvement over a baseline F-measure of 0.66, obtained by using only word ngram features. Since twitter data is dynamic, classification models have to be adopted to changing nature of problems and language distributions. We describe a simple adaptation scheme, and experimentally demonstrate its effectiveness. Finally we discuss our method to pinpoint the phrases describing the problems in the identified tweets. We show that by using simple syntactic pattern an extraction F-measure of 0.434 is achieved. Considering the noise in the tweeter data this level of performance is quite encouraging.
System And Method For Training A Critical E-mail Classifier Using A Plurality Of Base Classifiers And N-Grams,
Tue Jun 05 16:10:37 EDT 2012
Disclosed is a method and system for identifying critical emails. To identify critical emails, a critical email classifier is trained from training data comprising labeled emails. The classifier extracts N-grams from the training data and identifies N-gram features from the extracted N-grams. The classifier also extracts salient features from the training data. The classifier is trained based on the identified N-gram features and the salient features so that the classifier can classify unlabeled emails as critical emails or non-critical emails.
System And Method Of Spoken Language Understanding In Human Computer Dialogs,
Tue May 29 16:10:31 EDT 2012
A system and method are disclosed that improve automatic speech recognition in a spoken dialog system. The method comprises partitioning speech recognizer output into self-contained clauses, identifying a dialog act in each of the self-contained clauses, qualifying dialog acts by identifying a current domain object and/or a current domain action, and determining whether further qualification is possible for the current domain object and/or current domain action. If further qualification is possible, then the method comprises identifying another domain action and/or another domain object associated with the current domain object and/or current domain action, reassigning the another domain action and/or another domain object as the current domain action and/or current domain object and then recursively qualifying the new current domain action and/or current object. This process continues until nothing is left to qualify.
System And Method Of Generating Responses To Text-Based Messages,
Tue Dec 20 16:06:48 EST 2011
In accordance with one aspect of the present invention, an automated method of and system for generating a response to a text-based natural language message is disclosed. The method includes identifying a sentence in the text-based natural language message. Also, identifying an input clause in the sentence. Further, comparing the input clause to a previously received clause, where the previously received clause is correlated with a previously generated response message. Additionally, generating an output response message based on the previously generated response message. The system includes means for performing the method steps.
Systems, Methods And Programs For Evaluating Audio Messages,
Tue Nov 01 16:06:25 EDT 2011
Systems, methods, and programs, for evaluating audio messages store a model that may include language patterns, audio patterns, and/or metafeatures that indicate a likelihood that the audio message is a spam message or a non-spam message and compare the content of the input audio message with the model. Based on the comparison, the systems, methods, and programs identify the input audio message as a spam message or a non-spam message.
Automated Call Router For Business Directory Using The World Wide Web,
Tue Jul 26 16:05:47 EDT 2011
The embodiments include a system, a computer readable medium, and a method for establishing a communication connection after searching the World Wide Web for relevant phone information. The system can include a first communication device for forming at least one communication connection between the first communication device and a second communication device, search means adapted to accept a query, access means adapted to (i) search and identify relevant phone number information using the query (ii) create at least one icon to link the first communication device to a relevant phone number included in the relevant phone number information identified by the query, and (iii) reformulate the query if no relevant phone numbers are identified during the search. The system also includes click-to-dial means adapted to establish at least one communication connection from the first communication device to the second communication device.
Automatic Learning For Mapping Spoken/Text Descriptions Of Products Onto Available Products,
Tue May 03 16:05:03 EDT 2011
A method, processing device, and machine-readable medium are provided. Costs of states of a state space are calculated. Each state represent one or more available product attributes having zero or more decided attribute values. The calculating is based, at least in part, on training data associated with previously requested and offered products. Determining a next state such that one or more products are available and a sum of values, including a cost of a next state and a cost of a perturbation of one of the one or more requested product attribute values to reach the next state is a minimum value. A value for a product attribute is mapped according to the minimum sum of values and product attribute values of available products.
Voice-Enabled Dialog System,
Tue Jan 11 16:04:22 EST 2011
A voice-enabled help desk service is disclosed. The service comprises an automatic speech recognition module for recognizing speech from a user, a spoken language understanding module for understanding the output from the automatic speech recognition module, a dialog management module for generating a response to speech from the user, a natural voices text-to-speech synthesis module for synthesizing speech to generate the response to the user, and a frequently asked questions module. The frequently asked questions module handles frequently asked questions from the user by changing voices and providing predetermined prompts to answer the frequently asked question.
System And Method Of Exploiting Human-Human Data For Spoken Language Understanding Systems,
Tue Dec 14 15:05:22 EST 2010
A method is disclosed for generating labeled utterances from human-human utterances for use in training a semantic classification model for a spoken dialog system. The method comprises augmenting received human-human utterances with data that relates to call-type gaps in the human-human utterances, augmenting the received human-human utterances by placing at least one word in the human-human utterances that improves the training ability of the utterances according to the conversation patterns of the spoken dialog system, clausifying the human-human utterances, labeling the clausified and augmented human-human utterances and building the semantic classification model for the spoken dialog system using the labeled utterances.
Providing Called Number Characteristics To Click To Dial Customers,
Tue Jun 29 15:04:10 EDT 2010
A system and method to provide content and call attributes for a destination phone number using a click-to-dial connection includes accepting a query, retrieving links to a document, and searching through cached data using the query to identify relevant or destination number information. If no match is found, the method continues with accessing the document identified by the link for identifying relevant number information and creating a click-to-dial icon to link to the relevant numbers included in the relevant phone number information identified by the query. Next, a popup box for content relevant to the click-to-dial icon and a click feature on the popup box are created to retrieve call destination attributes for viewing by a user. Further, computer instructions create at least one communication connection between two communication devices after viewing call destination attributes.
Method Of Generation A Labeling Guide For Spoken Dialog Services,
Tue Jun 01 15:03:58 EDT 2010
A method is disclosed for designing a labeling guide for use by a labeler in labeling data used for training a spoken language understanding (SLU) module for an application. The method comprises a labeling guide designer selecting domain-independent actions applicable to an application, selecting domain-dependent objects according to characteristics of the application, and generating a labeling guide using the selected domain-independent actions and selected domain-dependent objects. An advantage of the labeling guide generated in this manner is that the labeling guide designer can easily port the labeling guide to a new application by selecting a set of domain-independent action and then selecting the domain-dependent objects related to the new application.
System and method of spoken language understanding in a spoken dialog service,
Tue Nov 11 18:13:12 EST 2008
A voice-enabled help desk service is disclosed. The service comprises an automatic speech recognition module for recognizing speech from a user, a spoken language understanding module for understanding the output from the automatic speech recognition module, a dialog management module for generating a response to speech from the user, a natural voices text-to-speech synthesis module for synthesizing speech to generate the response to the user, and a frequently asked questions module. The frequently asked questions module handles frequently asked questions from the user by changing voices and providing predetermined prompts to answer the frequently asked question.
Method of generating a labeling guide for spoken dialog services,
Tue Apr 29 18:12:46 EDT 2008
A method is disclosed for designing a labeling guide for use by a labeler in labeling data used for training a spoken language understanding (SLU) module for an application. The method comprises a labeling guide designer selecting domain-independent actions applicable to an application, selecting domain-dependent objects according to characteristics of the application, and generating a labeling guide using the selected domain-independent actions and selected domain-dependent objects. An advantage of the labeling guide generated in this manner is that the labeling guide designer can easily port the labeling guide to a new application by selecting a set of domain-independent action and then selecting the domain-dependent objects related to the new application.
Spoken language understanding that incorporates prior knowledge into boosting,
Tue Feb 05 17:08:37 EST 2008
A system for understanding entries, such as speech, develops a classifier by employing prior knowledge with which a given corpus of training entries is enlarged threefold. A rule is created for each of the labels employed in the classifier, and the created rules are applied to the given corpus to create a corpus of attachments by appending a weight of .eta.p(x), or 1-.eta.p(x), to labels of entries that meet, or fail to meet, respectively, conditions of the labels' rules, and to also create a corpus of non-attachments by appending a weight of 1-.eta.p(x), or .eta.p(x), to labels of entries that meet, or fail to meet conditions of the labels' rules.
Method for building a natural language understanding model for a spoken dialog system,
Tue Nov 13 18:12:25 EST 2007
A method of generating a natural language model for use in a spoken dialog system is disclosed. The method comprises using sample utterances and creating a number of hand crafted rules for each call-type defined in a labeling guide. A first NLU model is generated and tested using the hand crafted rules and sample utterances. A second NLU model is built using the sample utterances as new training data and using the hand crafted rules. The second NLU model is tested for performance using a first batch of labeled data. A series of NLU models are built by adding a previous batch of labeled data to training data and using a new batch of labeling data as test data to generate the series of NLU models with training data that increases constantly. If not all the labeling data is received, the method comprises repeating the step of building a series of NLU models until all labeling data is received. After all the training data is received, at least once, the method comprises building a third NLU model using all the labeling data, wherein the third NLU model is used in generating the spoken dialog service.
System for handling frequently asked questions in a natural language dialog service,
Tue Mar 27 18:11:58 EDT 2007
A voice-enabled help desk service is disclosed. The service comprises an automatic speech recognition module for recognizing speech from a user, a spoken language understanding module for understanding the output from the automatic speech recognition module, a dialog management module for generating a response to speech from the user, a natural voices text-to-speech synthesis module for synthesizing speech to generate the response to the user, and a frequently asked questions module. The frequently asked questions module handles frequently asked questions from the user by changing voices and providing predetermined prompts to answer the frequently asked question.
Spoken language understanding that incorporates prior knowledge into boosting,
Tue Dec 19 17:08:34 EST 2006
A system for understanding entries, such as speech, develops a classifier by employing prior knowledge with which a given corpus of training entries is enlarged threefold. A rule is created for each of the labels employed in the classifyier, and the created rules are applied to the given corpus to create a corpus of attachments by appending a weight of .eta.p(x), or 1-.eta.p(x), to labels of entries that meet, or fail to meet, respectively, conditions of the labels' rules, and to also create a corpus of non-attachments by appending a weight of 1-.eta.p(x), or .eta.p(x), to labels of entries that meet, or fail to meet conditions of the labels' rules.
Voice-operated interface for DTMF-controlled systems,
Tue Mar 15 18:10:19 EST 2005
An arrangement for allowing hands-free access to DTMF-controlled systems, such as one's voice mail messaging systems, utilizes a speech-to-DTMF tone application that monitors the communication between the user and the DTMF-controlled system. A speech recognition unit is utilized to retrieve certain voice commands (e.g., next, skip, repeat, forward, etc.) when uttered by the user. The application then translates the received commands into the proper DTMF tone sequence used by the DTMF-controlled system and transmits the DTMF tones to the system. The application is particularly useful in the cell phone environment and avoids the necessity of the user to constantly switch between using the keypad and listening to messages/commands from the system.
