Posts Tagged ‘natural language’
10
Jul
Posted by cognitivecomputing in Agents, Health & Wellness, Language, Web / Web 2.0. Tagged: case-based reasoning, healthcare, information retrieval, natural language, semantic memory, text cbr. Leave a comment
We introduce a Conversational Interaction framework as an innovative and natural approach to facilitate easier information access by combining web search and recommendations. This framework includes an intelligent information agent (Cobot) in the conversation to provide contextually relevant social and web search recommendations. Cobot supports the information discovery process by integrating web information retrieval along with proactive connections to relevant users who can participate in real-time conversations. We describe the conversational framework and report on some preliminary experiments in the system.
Read the paper:
Conversational Framework for Web Search and Recommendations
by Saurav Sahay, Ashwin Ram
ICCBR-10 Workshop on Reasoning from Experiences on the Web (WebCBR-10), Alessandria, Italy, 2010.
www.cc.gatech.edu/faculty/ashwin/papers/er-10-01.pdf
21
Jul
Posted by cognitivecomputing in Agents, Health & Wellness, Language, Web / Web 2.0. Tagged: healthcare, information retrieval, natural language, semantic memory. Leave a comment
Knowledge and user-generated content is proliferating on the web in scientific publications, information portals and online social media. This knowledge explosion has continued to outpace technological innovation in efficient information access technologies. In this paper, we describe methods and technologies for “Conversational Search” as an innovative solution to facilitate easier information access and reduce the information overload for users.
Conversational Search is an interactive and collaborative information finding interaction. The participants in this interaction engage in social conversations aided with an intelligent information agent (Cobot) that provides contextually relevant search recommendations. The collaborative and conversational search activity helps users make faster and more informed search and discovery. It also helps the agent learn about conversations with interactions and social feedback to make better recommendations. Conversational search leverages the social discovery process by integrating web information retrieval along with the social interactions.
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Collaborative Information Access: A Conversational Search Approach
by Saurav Sahay, Anu Venkatesh, Ashwin Ram
ICCBR-09 Workshop on Reasoning from Experiences on the Web (WebCBR-09), Seattle, July 2009
www.cc.gatech.edu/faculty/ashwin/papers/er-09-05.pdf
10
Jun
Posted by cognitivecomputing in Language. Tagged: information retrieval, natural language. Leave a comment
We present an application of a clustering technique to a large original dataset of SCI publications which is capable at disentangling the different research lines followed by a scientist, their duration over time and the intensity of effort devoted to each of them. Information is obtained by means of software-assisted content analysis, based on the co-occurrence of words in the full abstract and title of a set of SCI publications authored by 650 American star-physicists across 17 years. We estimated that scientists in our dataset over the time span contributed on average to 16 different research lines lasting on average 3.5 years and published nearly 5 publications in each single line of research. The technique is potentially useful for scholars studying science and the research community, as well as for research agencies, to evaluate if the scientist is new to the topic and for librarians, to collect timely biographic information.
Read the paper:
Using Content Analysis to Investigate The Research Paths Chosen by Scientists over Time
by Chiara Franzoni, Chris Simpkins, Baoli Li, Ashwin Ram
Scientometrics journal 83(1):321-335, April 2010. (Earlier version in 1th International Conference on Scientometrics and Infometrics (ISSI-07), Madrid, Spain, June 2007.)
www.springerlink.com/content/5462n515405715u2/?p=8344e997766b4ecdabee78f5e27a9faa&pi=18
www.cc.gatech.edu/faculty/ashwin/papers/er-07-06.pdf
29
Jan
Posted by cognitivecomputing in Language, Talks, Web / Web 2.0. Tagged: natural language. Leave a comment
As consumers become producers and, now, participants in online social communities, there are new opportunities and challenges in the increasing amounts of textual information and interactions on the web, within enterprises, in government, and in new types of social media and virtual worlds.
Natural Language Processing (NLP) researchers have traditionally regarded language as the object of study. In this talk, I argue that NLP is as much a study of people as of language per se. Doing NLP well requires us to model and reason about Content (domain knowledge), Context (goals and tasks), and Community (social context). I discuss why modeling the three C’s is difficult, and illustrate some approaches to these problems using examples from my recent academic and commercial projects.
Invited talk at PARC (Palo Alto Research Labs), Palo Alto, CA, January 2009
4
Sep
Posted by cognitivecomputing in Health & Wellness, Language, Web / Web 2.0. Tagged: healthcare, information retrieval, natural language, semantic memory, text cbr. Leave a comment
Effective encoding of information is one of the keys to qualitative problem solving. Our aim is to explore Knowledge Representation techniques that capture meaningful word associations occurring in documents. We have developed iReMedI, a TCBR-based problem solving system as a prototype to demonstrate our idea. For representation we have used a combination of NLP and graph based techniques which we call as Shallow Syntactic Triples, Dependency Parses and Semantic Word Chains. To test their effectiveness we have developed retrieval techniques based on PageRank, Shortest Distance and Spreading Activation methods. The various algorithms discussed in the paper and the comparative analysis of their results provides us with useful insight for creating an effective problem solving and reasoning system.
Read the paper:
iReMedI – Intelligent Retrieval from Medical Information
by Saurav Sahay, Bharat Ravisekar, Anu Venkatesh, Sundaresan Venkatasubramanian, Priyanka Prabhu, Ashwin Ram
9th European Conference on Case-Based Reasoning (ECCBR-08), Trier, Germany
www.cc.gatech.edu/faculty/ashwin/papers/er-08-05.pdf
1
Jul
Posted by cognitivecomputing in Language. Tagged: information retrieval, natural language. Leave a comment
In this paper we investigate how to automatically determine the subjectivity orientation of questions posted by real users in community question answering (CQA) portals. Subjective questions seek answers containing private states, such as personal opinion and experience. In contrast, objective questions request objective, verifiable information, often with support from reliable sources. Knowing the question orientation would be helpful not only for evaluating answers provided by users, but also for guiding the CQA engine to process questions more intelligently. Our experiments on Yahoo! Answers data show that our method exhibits promising performance.
Read the paper:
Subjectivity Analysis for Questions in QA Communities
by Baoli Li, Yandong Liu, Ashwin Ram, Ernie Garcia, Eugene Agichtein
31st Annual International ACM SIGIR Conference (ACM-SIGIR-08), Singapore, July 2008
www.cc.gatech.edu/faculty/ashwin/papers/er-08-02.pdf
1
Mar
Posted by cognitivecomputing in Health & Wellness, Language, Web / Web 2.0. Tagged: healthcare, information retrieval, natural language, semantic memory. Leave a comment
To realize the vision of a Semantic Web for Life Sciences, discovering relations between resources is essential. It is very difficult to automatically extract relations from Web pages expressed in natural language formats. On the other hand, because of the explosive growth of information, it is difficult to manually extract the relations. In this paper we present techniques to automatically discover relations between biomedical resources from the Web. For this purpose we retrieve relevant information from Web Search engines and Pubmed database using various lexico-syntactic patterns as queries over SOAP web services. The patterns are initially handcrafted but can be progressively learnt. The extracted relations can be used to construct and augment ontologies and knowledge bases. Experiments are presented for general biomedical relation discovery and domain specific search to show the usefulness of our technique.
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Discovering Semantic Biomedical Relations utilizing the Web
by Saurav Sahay, Sougata Mukherjea, Eugene Agichtein, Ernie Garcia, Sham Navathe, Ashwin Ram
ACM Transactions on Knowledge Discovery from Data, 2(1):3, 2008
www.cc.gatech.edu/faculty/ashwin/papers/er-08-01.pdf
10
Oct
Posted by cognitivecomputing in Health & Wellness, Language, Learning. Tagged: healthcare, natural language, semantic memory. Leave a comment
We describe our vision for a new generation medical knowledge annotation and acquisition system called SENTIENT-MD (Semantic Annotation and Inference for Medical Knowledge Discovery). Key aspects of our vision include deep Natural Language Processing techniques to abstract the text into a more semantically meaningful representation guided by domain ontology. In particular, we introduce a notion of semantic fitness to model an optimal level of abstract representation for a text fragment given a domain ontology. We apply this notion to appropriately condense and merge nodes in semantically annotated syntactic parse trees. These transformed semantically annotated trees are more amenable to analysis and inference for abstract knowledge discovery, such as for automatically inferring general medical rules for enhancing an expert system for nuclear cardiology. This work is a part of a long term research effort on continuously mining medical literature for automatic clinical decision support.
Read the paper:
Semantic Annotation and Inference for Medical Knowledge Discovery
by Saurav Sahay, Eugene Agichtein, Baoli Li, Ernie Garcia, Ashwin Ram
NSF Symposium on Next Generation of Data Mining (NGDM-07), Baltimore, MD, October 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-16.pdf
31
Aug
Posted by cognitivecomputing in Language. Tagged: information retrieval, natural language. Leave a comment
Associative classification, which originates from numerical data mining, has been applied to deal with text data recently. Text data is firstly digitalized to database of transactions, and then training and prediction is actually conducted on the derived numerical dataset. This intuitive strategy has demonstrated quite good performance. However, it doesn’t take into consideration the inherent characteristics of text data as much as possible, although it has to deal with some specific problems of text data such as lemmatizing and stemming during digitalization. In this paper, we propose a bottom-up strategy to adapt associative classification to text categorization, in which we take into account structure information of text. Experiments on Reuters-21578 dataset show that the proposed strategy can make use of text structure information and achieve better performance.
Read the paper:
Adapting Associative Classification to Text Categorization
by Baoli Li, Neha Sugandh, Ernie Garcia, Ashwin Ram
ACM Conference on Document Engineering (ACM-DocEng-07), Winnipeg, Canada, August 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-13.pdf
29
Jun
Posted by cognitivecomputing in Language. Tagged: natural language. Leave a comment
Textual Entailment Recognition is a semantic inference task that is required in many natural language processing (NLP) applications. In this paper, we present our system for the third PASCAL recognizing textual entailment (RTE-3) challenge. The system is built on a machine learning framework with the following features derived by state-of-the-art NLP techniques: lexical semantic similarity (LSS), named entities (NE), dependent content word pairs (DEP), average distance (DIST), negation (NG), task (TK), and text length (LEN).
On the RTE-3 test dataset, our system achieves the accuracy of 0.64 and 0.6488 for the two official submissions, respectively. Experimental results show that LSS and NE are the most effective features. Further analyses indicate that a baseline dummy system can achieve accuracy 0.545 on the RTE-3 test dataset, which makes RTE-3 relatively easier than RTE-2 and RTE-1. In addition, we demonstrate with examples that the current Average Precision measure and its evaluation process need to be changed.
Read the paper:
Machine Learning Based Semantic Inference: Experiments and Observations at RTE-3
by Baoli Li, Joseph Irwin, Ernie Garcia, Ashwin Ram
Association for Computational Linguistics (ACL) Challenge Workshop on Textual Entailment and Paraphrase (WTEP-07), Prague, Czech Republic, June 2007
www.cc.gatech.edu/faculty/ashwin/papers/er-07-12.pdf
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