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This article presents a search-intent-based method to generate pornographic blacklists for collaborative cyberporn filtering. A novel porn-detection framework that can find newly appearing pornographic web pages by mining search query logs is proposed. First, suspected queries are identified along with their clicked URLs by an automatically constructed lexicon. Then, a candidate URL is determined if the number of clicks satisfies majority voting rules. Finally, a candidate whose URL contains at least one categorical keyword will be included in a blacklist. Several experiments are conducted on an MSN search porn dataset to demonstrate the effectiveness of our method. The resulting blacklist generated by our search-intent-based method achieves high precision (0.701) while maintaining a favorably low false-positive rate (0.086). The experiments of a real-life filtering simulation reveal that our proposed method with its accumulative update strategy can achieve 44.15% of a macro-averaging blocking rate, when the update frequency is set to 1 day. In addition, the overblocking rates are less than 9% with time change due to the strong advantages of our search-intent-based method. This user-behavior-oriented method can be easily applied to search engines for incorporating only implicit collective intelligence from query logs without other efforts. In practice, it is complementary to intelligent content analysis for keeping up with the changing trails of objectionable websites from users' perspectives. [ABSTRACT FROM AUTHOR]

Named entities are major constituents of a document but are usually unknown words. This work proposes a systematic way of dealing with formulation, transformation, translation, and transliteration of multilingual-named entities. The rules and similarity matrices for translation and transliteration are learned automatically from parallel-named-entity corpora. The results are applied in cross-language access to collections of images with captions. Experimental results demonstrate that the similarity-based transliteration of named entities is effective, and runs in which transliteration is considered outperform the runs in which it is neglected. [ABSTRACT FROM AUTHOR]

Proposes a part-of-speech-based (POS-based) criterion for aligning bilingual texts. Advantages of POS-based criterion; Performance evaluation method to calculate precision and recall; Degree of matching between real bead sequence and computed bead sequence; Divide-and-conquer technique; Dynamic programming; Simulated annealing; Translation of bilingual texts.

Highlights • We investigate the approaches of learning bilingual word representations with and without word alignment comprehensively. • In the approaches without word alignment, we propose four types of methods to formulate the contexts for Skip-gram modelling. • In the approaches with word alignment, we induce word alignment links by word alignment tools to learn bilingual semantics. • We explore the impact of different word alignment tools and alignment directions in learning bilingual word representations. Abstract Representation of words in different languages is fundamental for various cross-lingual applications. In the past researches, there was an argument in using or not using word alignment in learning bilingual word representations. This paper presents a comprehensive empirical study on the uses of parallel corpus to learn the word representations in the embedding space. Various non-alignment and alignment approaches are explored to formulate the contexts for Skip-gram modeling. In the approaches without word alignment, concatenating A and B, concatenating B and A, interleaving A with B, shuffling A and B, and using A and B separately are considered, where A and B denote parallel sentences in two languages. In the approaches with word alignment, three word alignment tools, including GIZA++, TsinghuaAligner, and fast_align, are employed to align words in sentences A and B. The effects of alignment direction from A to B or from B to A are also discussed. To deal with the unaligned words in the word alignment approach, two alternatives, using the words aligned with their immediate neighbors and using the words in the interleaving approach, are explored. We evaluate the performance of the adopted approaches in four tasks, including bilingual dictionary induction, cross-lingual information retrieval, cross-lingual analogy reasoning, and cross-lingual word semantic relatedness. These tasks cover the issues of translation, reasoning, and information access. Experimental results show the word alignment approach with conditional interleaving achieves the best performance in most of the tasks. [ABSTRACT FROM AUTHOR]

Opinion words are crucial information for sentiment analysis. In some text, however, opinion words are absent or highly ambiguous. The resulting implicit opinions are more difficult to extract and label than explicit ones. In this paper, cutting-edge machine-learning approaches - deep neural network and word-embedding - are adopted for implicit opinion mining at the snippet and clause levels. Hotel reviews written in Chinese are collected and annotated as the experimental data set. Results show the convolutional neural network models not only outperform traditional support vector machine models, but also capture hidden knowledge within the raw text. The strength of word-embedding is also analyzed. [ABSTRACT FROM AUTHOR]

Abstract: This paper considers five features including titles, community chains, terms, temporal expressions, and hostnames for personal name disambiguation. In nine test data sets covering three ambiguous personal names, we address the issues of awareness degree of an entity, the source of materials and web pages in different areas. In a single-clusterer approach, employing all features achieve average F-score 0.635, which is better than employing contextual terms only 0.502. When community chains are expanded by using the web, the average F-score is increased to 0.676. We also propose a multiple-clusterer approach, which cascades five clusterers corresponding to the five features. The average F-score is further improved to 0.684. Expanding community chains also enhances the average F-score of the multiple-clusterer approach to 0.697. In summary, the proposed features are quite useful; the cascaded multiple-clusterer approach is better than the single-clusterer approach; and expanding community chains using the web has positive effects on personal name disambiguation. The experiments show that this approach has significant improvements. [Copyright &y& Elsevier]

This paper investigates sentiments in blog data using machine learning techniques such as support vector machines (SVM) and conditional random fields (CRF). Bloggers collaboratively contribute to the formation of blog corpora, which contain a rich set of icon-tagged text expressions. First, a collocation model on the expressions is used to construct a lexicon which provides keyword features for subsequent classification models. SVM and CRF classifiers use these keywords as features to assign a sentence sentiment bipolarity or arousal-valence space categories. Our methods determine a sentiment category by taking contexts into account. Experiments show that CRF classifiers outperform SVM classifiers and the performance benefits from increasing the training size of blog materials. [ABSTRACT FROM AUTHOR]

In this research, we propose 3 different approaches to measure the semantic relatedness between 2 words: (i) boost the performance of GloVe word embedding model via removing or transforming abnormal dimensions; (ii) linearly combine the information extracted from WordNet and word embeddings; and (iii) utilize word embedding and 12 linguistic information extracted from WordNet as features for Support Vector Regression. We conducted our experiments on 8 benchmark data sets, and computed Spearman correlations between the outputs of our methods and the ground truth. We report our results together with 3 state‐of‐the‐art approaches. The experimental results show that our method can outperform state‐of‐the‐art approaches in all the selected English benchmark data sets. [ABSTRACT FROM AUTHOR]

Integration of in-domain knowledge into an out-of-domain statistical machine translation (SMT) system poses challenges due to the lack of resources. Lack of in-domain bilingual corpora is one such issue. In this paper, we propose a simplification–translation–restoration (STR) framework for domain adaptation in SMT systems. An SMT system to translate medical records from English to Chinese is taken as a case study. We identify the critical segments in a medical sentence and simplify them to alleviate the data sparseness problem in the out-of-domain SMT system. After translating the simplified sentence, the translations of these critical segments are restored to their proper positions. Besides the simplification pre-processing step and the restoration post-processing step, we also enhance the translation and language models in the STR framework by using pseudo bilingual corpora generated by the background MT system. In the experiments, we adapt an SMT system from a government document domain to a medical record domain. The results show the effectiveness of the STR framework. [ABSTRACT FROM AUTHOR]

Most previous studies make psychiatric diagnoses based on diagnostic terms. In this study we sought to augment Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) diagnostic criteria with deep neural network models to make psychiatric diagnoses based on psychiatric notes. We augmented DSM-5 diagnostic criteria with self-attention-based bidirectional long short-term memory (BiLSTM) models to identify schizophrenia, bipolar, and unipolar depressive disorders. Given that the diagnostic criteria for psychiatric diagnosis include a certain symptom profile and functional impairment, we first extracted psychiatric symptoms and functional features with two approaches, including a lexicon-based approach and a dependency parsing approach. Then, we incorporated free-text discharge notes and extracted features for psychiatric diagnoses with the proposed models. The micro-averaged F1 scores of the two automatic annotation approaches were greater than 0.8. BiLSTM models with self-attention outperformed the rule-based models with DSM-5 criteria in the prediction of schizophrenia and bipolar disorder, while the latter outperformed the former in predicting unipolar depressive disorder. Approaches for augmenting DSM-5 criteria with a self-attention-based BiLSTM outperformed both pure rule-based and pure deep neural network models. In terms of classification of psychiatric diagnoses, we observed that the performance for schizophrenia and bipolar disorder was acceptable. This DSM-5-augmented deep neural network models showed good performance in identifying psychiatric diagnoses from psychiatric notes. We conclude that it is possible to establish a model that consults clinical notes to make psychiatric diagnoses comparably to physicians. Further research will be extended to outpatient notes and other psychiatric disorders. • Most previous studies make psychiatric diagnoses based on diagnostic terms. • We developed model to identify diagnosis from history and mental status examination. • DSM-5 augmented BiLSTM models outperformed the rule-based or pure deep network model. [ABSTRACT FROM AUTHOR]

Statistical language modeling (LM) that intends to quantify the acceptability of a given piece of text has long been an interesting yet challenging research area. In particular, language modeling for information retrieval (IR) has enjoyed remarkable empirical success; one emerging stream of the LM approach for IR is to employ the pseudo-relevance feedback process to enhance the representation of an input query so as to improve retrieval effectiveness. This paper presents a continuation of such a general line of research and the major contributions are three-fold. First, we propose a principled framework which can unify the relationships among several widely-cited query modeling formulations. Second, on top of this successfully developed framework, two extensions have been proposed. On one hand, we present an extended query modeling formulation by incorporating critical query-specific information cues to guide the model estimation. On the other hand, a word-based relevance modeling has also been leveraged to overcome the obstacle of time-consuming model estimation when the framework is being utilized for practical applications. In addition, we further adopt and formalize such a framework to the speech recognition and summarization tasks. A series of experiments reveal the empirical potential of such an LM framework and the performance merits of the deduced models on these two tasks. [ABSTRACT FROM AUTHOR]

User queries to the Web tend to have more than one interpretation due to their ambiguity and other characteristics. How to diversify the ranking results to meet users' various potential information needs has attracted considerable attention recently. This paper is aimed at mining the subtopics of a query either indirectly from the returned results of retrieval systems or directly from the query itself to diversify the search results. For the indirect subtopic mining approach, clustering the retrieval results and summarizing the content of clusters is investigated. In addition, labeling topic categories and concept tags on each returned document is explored. For the direct subtopic mining approach, several external resources, such as Wikipedia, Open Directory Project, search query logs, and the related search services of search engines, are consulted. Furthermore, we propose a diversified retrieval model to rank documents with respect to the mined subtopics for balancing relevance and diversity. Experiments are conducted on the ClueWeb09 dataset with the topics of the TREC09 and TREC10 Web Track diversity tasks. Experimental results show that the proposed subtopic-based diversification algorithm significantly outperforms the state-of-the-art models in the TREC09 and TREC10 Web Track diversity tasks. The best performance our proposed algorithm achieves is α-nDCG@5 0.307, IA-P@5 0.121, and α#-nDCG@5 0.214 on the TREC09, as well as α-nDCG@10 0.421, IA-P@10 0.201, and α#-nDCG@10 0.311 on the TREC10. The results conclude that the subtopic mining technique with the up-to-date users' search query logs is the most effective way to generate the subtopics of a query, and the proposed subtopic-based diversification algorithm can select the documents covering various subtopics. [ABSTRACT FROM AUTHOR]