Ontology learning is a critical task in industry, dealing with identifying and extracting concepts captured in text data such that these concepts can be used in different tasks, e.g. information retrieval. Ontology learning is non-trivial due to several reasons with limited amount of prior research work that automatically learns a domain specific ontology from data. In our work, we propose a two-stage classification system to automatically learn an ontology from unstructured text data. We first collect candidate concepts, which are classified into concepts and irrelevant collocates by our first classifier. The concepts from the first classifier are further classified by the second classifier into different concept types. The proposed system is deployed as a prototype at a company and its performance is validated by using complaint and repair verbatim data collected in automotive industry from different data sources.
In our work, we also propose a new approach to disambiguate abbreviations. There are several related works. @cite_2 extracted features such as concept unique identifiers and then built a classification model. @cite_9 identified context based features for classification, but they assumed an ambiguous phrase only has one correct expansion in the same article. @cite_3 proposed a word embedding based approach to select the expansion from all possible expansions with largest embedding similarity. There are two major differences between our approach and these works. First, we propose a new model which combines the statistical approach (TF-IDF) and machine learning approach (Naive Bayes model) together, i.e. we measure the importance of each collocate by TF-IDF and estimate the posterior probability of each possible expansion, while in their work they either only applied machine learning classification models or simply calculated statistical similarity between abbreviation and possible expansions. Second, in these works strong assumptions were made, such as each phrase only has one expansion in the same article and features are conditionally independent, while we do not have any assumptions for our model and therefore it is more robust.
@cite_9 @cite_3 @cite_2
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An unsupervised iterative approach for extracting a new lexicon (or unknown words) from a Chinese text corpus is proposed in this paper. Instead of using a non-iterative segmentation-merging-filtering-and-disambiguation approach, the proposed method iteratively integrates the contextual constraints (among word candidates) and a joint character association metric to progressively improve the segmentation results of the input corpus (and thus the new word list.) An augmented dictionary, which includes potential unknown words (in addition to known words), is used to segment the input corpus, unlike traditional approaches which use only known words for segmentation. In the segmentation process, the augmented dictionary is used to impose contextual constraints over known words and potential unknown words within input sentences; an unsupervised Viterbi Training process is then applied to ensure that the selected potential unknown words (and known words) maximize the likelihood of the input corpus. On the other hand, the joint character association metric (which reflects the global character association characteristics across the corpus) is derived by integrating several commonly used word association metrics, such as mutual information and entropy, with a joint Gaussian mixture density function; such integration allows the filter to use multiple features simultaneously to evaluate character association, unlike traditional filters which apply multiple features independently. The proposed method then allows the contextual constraints and the joint character association metric to enhance each other; this is achieved by iteratively applying the joint association metric to truncate unlikely unknown words in the augmented dictionary and using the segmentation result to improve the estimation of the joint association metric. The refined augmented dictionary and improved estimation are then used in the next iteration to acquire better segmentation and carry out more reliable filtering. Experiments show that both the precision and recall rates are improved almost monotonically, in contrast to non-iterative segmentation-merging-filtering-and-disambiguation approaches, which often sacrifice precision for recall or vice versa. With a corpus of 311,591 sentences, the performance is 76 (bigram), 54 (trigram), and 70 (quadragram) in F-measure, which is significantly better than using the non-iterative approach with F-measures of 74 (bigram), 46 (trigram), and 58 (quadragram). When recommending news items, most of the traditional algorithms are based on TF-IDF, i.e., a term-based weighting method which is mostly used in information retrieval and text mining. However, many new technologies have been made available since the introduction of TF-IDF. This paper proposes a new method for recommending news items based on TF-IDF and a domain ontology. It is demonstrated that adapting TF-IDF with the semantics of a domain ontology, resulting in Concept Frequency - Inverse Document Frequency (CF-IDF), yields better results than using the original TF-IDF method. CF-IDF is built and tested in Athena, a recommender extension to the Hermes news personalization framework. Athena employs a user profile to store concepts or terms found in news items browsed by the user. The framework recommends new articles to the user using a traditional TF-IDF recommender and the CF-IDF recommender. A statistical evaluation of both methods shows that the use of an ontology significantly improves the performance of a traditional recommender. It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domain knowledge in the form of affinities between input features and classes. For example, in a baseball vs. hockey text classification problem, even without any labeled data, we know that the presence of the word puck is a strong indicator of hockey. We refer to this type of domain knowledge as a labeled feature. In this paper, we propose a method for training discriminative probabilistic models with labeled features and unlabeled instances. Unlike previous approaches that use labeled features to create labeled pseudo-instances, we use labeled features directly to constrain the model's predictions on unlabeled instances. We express these soft constraints using generalized expectation (GE) criteria --- terms in a parameter estimation objective function that express preferences on values of a model expectation. In this paper we train multinomial logistic regression models using GE criteria, but the method we develop is applicable to other discriminative probabilistic models. The complete objective function also includes a Gaussian prior on parameters, which encourages generalization by spreading parameter weight to unlabeled features. Experimental results on text classification data sets show that this method outperforms heuristic approaches to training classifiers with labeled features. Experiments with human annotators show that it is more beneficial to spend limited annotation time labeling features rather than labeling instances. For example, after only one minute of labeling features, we can achieve 80 accuracy on the ibm vs. mac text classification problem using GE-FL, whereas ten minutes labeling documents results in an accuracy of only 77 Recent work in text classification has used two different first-order probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multi-variate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey and Croft 1996; Koller and Sahami 1997). Others use a multinomial model, that is, a uni-gram language model with integer word counts (e.g. Lewis and Gale 1994; Mitchell 1997). This paper aims to clarify the confusion by describing the differences and details of these two models, and by empirically comparing their classification performance on five text corpora. We find that the multi-variate Bernoulli performs well with small vocabulary sizes, but that the multinomial performs usually performs even better at larger vocabulary sizes--providing on average a 27 reduction in error over the multi-variate Bernoulli model at any vocabulary size.