So you report with reasonable accuracies what the sentiment about a particular brand or product is. After publishing this report, your client comes back to you and says "Hey this is good. Now can you tell me ways in which I can convert the negative sentiments into positive sentiments?" – Sentiment Analysis stops there and we enter the realms of Opinion Mining. Opinion Mining is about having a deeper understanding of the review that was written. Typically, a detailed review will not just have a sentiment attached to it.
There's a lot of buzz around the term "Sentiment Analysis" and the various ways of doing it. So you report with reasonable accuracies what the sentiment about a particular brand or product is. After publishing this report, your client comes back to you and says "Hey this is good. Now can you tell me ways in which I can convert the negative sentiments into positive sentiments?" – Sentiment Analysis stops there and we enter the realms of Opinion Mining. Opinion Mining is about having a deeper understanding of the review that was written.
We present the mixture-of-parents maximum entropy Markov model (MoP-MEMM), a class of directed graphical models extending MEMMs. The MoP-MEMM allows tractable incorporation of long-range dependencies between nodes by restricting the conditional distribution of each node to be a mixture of distributions given the parents. We show how to efficiently compute the exact marginal posterior node distributions, regardless of the range of the dependencies. This enables us to model non-sequential correlations present within text documents, as well as between interconnected documents, such as hyperlinked web pages. We apply the MoP-MEMM to a named entity recognition task and a web page classification task. In each, our model shows significant improvement over the basic MEMM, and is competitive with other long-range sequence models that use approximate inference.
Just Research 4616 Henry Street Pittsburgh, PA 15213 Abstract Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling time series data, and have been applied with success to many language-related tasks such as part of speech tagging, speech recognition, text segmentation and topic detection. This paper describes the application of HMMs to another language related task--information extraction--the problem of locating textual sub-segments that answer a particular information need. In our work, the HMM state transition probabilities and word emission probabilities are learned from labeled training data. As in many machine learning problems, however, the lack of sufficient labeled training data hinders the reliability of the model. The key contribution of this paper is the use of a statistical technique called "shrinkage" that significantly improves parameter estimation of the HMM emission probabilities in the face of sparse training data. In experiments on seminar announcements and Reuters acquisitions articles, shrinkage is shown to reduce error by up to 40%, and the resulting HMM outperforms a state-of-the-art rule-learning system. Introduction The Internet makes available a tremendous amount of text that has been generated for human consumption; unfortunately, this information is not easily manipulated or analyzed by computers. Information extraction is the process of filling fields in a database by automatically extracting subsequences of human-readable text.
Recently,a number of authorshave proposedtreating dialogue systems as Markov decision processes(MDPs). However,the practicalapplicationofMDP algorithms to dialogue systems faces a numberof severe technicalchallenges.We have built a general software tool (RLDS, for ReinforcementLearning for Dialogue Systems) on the MDP framework, and have applied it to dialogue corpora gatheredbased from two dialoguesystemsbuilt at AT&T Labs. Our experimentsdemonstratethat RLDS holds promise as a tool for "browsing" and understandingcorrelationsin complex, temporallydependentdialogue corpora.