73 research outputs found
Latent dirichlet markov allocation for sentiment analysis
In recent years probabilistic topic models have gained tremendous attention in data mining and natural language processing research areas. In the field of information retrieval for text mining, a variety of probabilistic topic models have been used to analyse content of documents. A topic model is a generative model for documents, it specifies a probabilistic procedure by which documents can be generated. All topic models share the idea that documents are mixture of topics, where a topic is a probability distribution over words. In this paper we describe Latent Dirichlet Markov Allocation Model (LDMA), a new generative probabilistic topic model, based on Latent Dirichlet Allocation (LDA) and Hidden Markov Model (HMM), which emphasizes on extracting multi-word topics from text data. LDMA is a four-level hierarchical Bayesian model where topics are associated with documents, words are associated with topics and topics in the model can be presented with single- or multi-word terms. To evaluate performance of LDMA, we report results in the field of aspect detection in sentiment analysis, comparing to the basic LDA model
Language Models
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Context Modeling for Ranking and Tagging Bursty Features in Text Streams
Bursty features in text streams are very useful in many text mining applications. Most existing studies detect bursty features based purely on term frequency changes without taking into account the semantic contexts of terms, and as a result the detected bursty features may not always be interesting or easy to interpret. In this paper we propose to model the contexts of bursty features using a language modeling approach. We then propose a novel topic diversity-based metric using the context models to find newsworthy bursty features. We also propose to use the context models to automatically assign meaningful tags to bursty features. Using a large corpus of a stream of news articles, we quantitatively show that the proposed context language models for bursty features can effectively help rank bursty features based on their newsworthiness and to assign meaningful tags to annotate bursty features. ? 2010 ACM.EI
Modeling Documents as Mixtures of Persons for Expert Finding
In this paper we address the problem of searching for knowledgeable
persons within the enterprise, known as the expert finding (or
expert search) task. We present a probabilistic algorithm using the assumption
that terms in documents are produced by people who are mentioned
in them.We represent documents retrieved to a query as mixtures
of candidate experts language models. Two methods of personal language
models extraction are proposed, as well as the way of combining
them with other evidences of expertise. Experiments conducted with the
TREC Enterprise collection demonstrate the superiority of our approach
in comparison with the best one among existing solutions
Hierarchical Re-estimation of Topic Models for Measuring Topical Diversity
A high degree of topical diversity is often considered to be an important
characteristic of interesting text documents. A recent proposal for measuring
topical diversity identifies three elements for assessing diversity: words,
topics, and documents as collections of words. Topic models play a central role
in this approach. Using standard topic models for measuring diversity of
documents is suboptimal due to generality and impurity. General topics only
include common information from a background corpus and are assigned to most of
the documents in the collection. Impure topics contain words that are not
related to the topic; impurity lowers the interpretability of topic models and
impure topics are likely to get assigned to documents erroneously. We propose a
hierarchical re-estimation approach for topic models to combat generality and
impurity; the proposed approach operates at three levels: words, topics, and
documents. Our re-estimation approach for measuring documents' topical
diversity outperforms the state of the art on PubMed dataset which is commonly
used for diversity experiments.Comment: Proceedings of the 39th European Conference on Information Retrieval
(ECIR2017
Knowledge-based Query Expansion in Real-Time Microblog Search
Since the length of microblog texts, such as tweets, is strictly limited to
140 characters, traditional Information Retrieval techniques suffer from the
vocabulary mismatch problem severely and cannot yield good performance in the
context of microblogosphere. To address this critical challenge, in this paper,
we propose a new language modeling approach for microblog retrieval by
inferring various types of context information. In particular, we expand the
query using knowledge terms derived from Freebase so that the expanded one can
better reflect users' search intent. Besides, in order to further satisfy
users' real-time information need, we incorporate temporal evidences into the
expansion method, which can boost recent tweets in the retrieval results with
respect to a given topic. Experimental results on two official TREC Twitter
corpora demonstrate the significant superiority of our approach over baseline
methods.Comment: 9 pages, 9 figure
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