Search (82 results, page 2 of 5)

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Source

Knowledge organization. 43(2016) no.6, S.440-461

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Source

Knowledge organization. 46(2019) no.3, S.171-186

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Source

Grundlagen der praktischen Information und Dokumentation. Handbuch zur Einführung in die Informationswissenschaft und -praxis. 6., völlig neu gefaßte Ausgabe. Hrsg. von R. Kuhlen, W. Semar u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried

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Abstract

Term proximity is effective for many information retrieval (IR) research fields yet remains unexplored in blogosphere IR. The blogosphere is characterized by large amounts of noise, including incohesive, off-topic content and spam. Consequently, the classical bag-of-words unigram IR models are not reliable enough to provide robust and effective retrieval performance. In this article, we propose to boost the blog postretrieval performance by employing term proximity information. We investigate a variety of popular and state-of-the-art proximity-based statistical IR models, including a proximity-based counting model, the Markov random field (MRF) model, and the divergence from randomness (DFR) multinomial model. Extensive experimentation on the standard TREC Blog06 test dataset demonstrates that the introduction of term proximity information is indeed beneficial to retrieval from the blogosphere. Results also indicate the superiority of the unordered bi-gram model with the sequential-dependence phrases over other variants of the proximity-based models. Finally, inspired by the effectiveness of proximity models, we extend our study by exploring the proximity evidence between query terms and opinionated terms. The consequent opinionated proximity model shows promising performance in the experiments.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.11, S.2278-2298

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Series

Information Systems and Applications, incl. Internet/Web, and HCI; Bd. 9088

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Abstract

Spam and wildly varying documents make searching in Twitter challenging. Most Twitter search systems generally treat a Tweet as a plain text when modeling relevance. However, a series of conventions allows users to Tweet in structural ways using a combination of different blocks of texts. These blocks include plain texts, hashtags, links, mentions, etc. Each block encodes a variety of communicative intent and the sequence of these blocks captures changing discourse. Previous work shows that exploiting the structural information can improve the structured documents (e.g., web pages) retrieval. In this study we utilize the structure of Tweets, induced by these blocks, for Twitter retrieval and Twitter opinion retrieval. For Twitter retrieval, a set of features, derived from the blocks of text and their combinations, is used into a learning-to-rank scenario. We show that structuring Tweets can achieve state-of-the-art performance. Our approach does not rely on social media features, but when we do add this additional information, performance improves significantly. For Twitter opinion retrieval, we explore the question of whether structural information derived from the body of Tweets and opinionatedness ratings of Tweets can improve performance. Experimental results show that retrieval using a novel unsupervised opinionatedness feature based on structuring Tweets achieves comparable performance with a supervised method using manually tagged Tweets. Topic-related specific structured Tweet sets are shown to help with query-dependent opinion retrieval.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2522-2539

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Abstract

Traditionally, many information retrieval models assume that terms occur in documents independently. Although these models have already shown good performance, the word independency assumption seems to be unrealistic from a natural language point of view, which considers that terms are related to each other. Therefore, such an assumption leads to two well-known problems in information retrieval (IR), namely, polysemy, or term mismatch, and synonymy. In language models, these issues have been addressed by considering dependencies such as bigrams, phrasal-concepts, or word relationships, but such models are estimated using simple n-grams or concept counting. In this paper, we address polysemy and synonymy mismatch with a concept-based language modeling approach that combines ontological concepts from external resources with frequently found collocations from the document collection. In addition, the concept-based model is enriched with subconcepts and semantic relationships through a semantic smoothing technique so as to perform semantic matching. Experiments carried out on TREC collections show that our model achieves significant improvements over a single word-based model and the Markov Random Field model (using a Markov classifier).

Source

Journal of the Association for Information Science and Technology. 67(2016) no.12, S.2909-2927

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Abstract

This paper is concerned with paraphrase detection, i.e., identifying sentences that are semantically identical. The ability to detect similar sentences written in natural language is crucial for several applications, such as text mining, text summarization, plagiarism detection, authorship authentication and question answering. Recognizing this importance, we study in particular how to address the challenges with detecting paraphrases in user generated short texts, such as Twitter, which often contain language irregularity and noise, and do not necessarily contain as much semantic information as longer clean texts. We propose a novel deep neural network-based approach that relies on coarse-grained sentence modelling using a convolutional neural network (CNN) and a recurrent neural network (RNN) model, combined with a specific fine-grained word-level similarity matching model. More specifically, we develop a new architecture, called DeepParaphrase, which enables to create an informative semantic representation of each sentence by (1) using CNN to extract the local region information in form of important n-grams from the sentence, and (2) applying RNN to capture the long-term dependency information. In addition, we perform a comparative study on state-of-the-art approaches within paraphrase detection. An important insight from this study is that existing paraphrase approaches perform well when applied on clean texts, but they do not necessarily deliver good performance against noisy texts, and vice versa. In contrast, our evaluation has shown that the proposed DeepParaphrase-based approach achieves good results in both types of texts, thus making it more robust and generic than the existing approaches.

Source

Information processing and management. 54(2018) no.6, S.922-937

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Abstract

We show that generating English Wikipedia articles can be approached as a multi-document summarization of source documents. We use extractive summarization to coarsely identify salient information and a neural abstractive model to generate the article. For the abstractive model, we introduce a decoder-only architecture that can scalably attend to very long sequences, much longer than typical encoder- decoder architectures used in sequence transduction. We show that this model can generate fluent, coherent multi-sentence paragraphs and even whole Wikipedia articles. When given reference documents, we show it can extract relevant factual information as reflected in perplexity, ROUGE scores and human evaluations.

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Abstract

Research into unsupervised ways of stemming has resulted, in the past few years, in the development of methods that are reliable and perform well. Our approach further shifts the boundaries of the state of the art by providing more accurate stemming results. The idea of the approach consists in building a stemmer in two stages. In the first stage, a stemming algorithm based upon clustering, which exploits the lexical and semantic information of words, is used to prepare large-scale training data for the second-stage algorithm. The second-stage algorithm uses a maximum entropy classifier. The stemming-specific features help the classifier decide when and how to stem a particular word. In our research, we have pursued the goal of creating a multi-purpose stemming tool. Its design opens up possibilities of solving non-traditional tasks such as approximating lemmas or improving language modeling. However, we still aim at very good results in the traditional task of information retrieval. The conducted tests reveal exceptional performance in all the above mentioned tasks. Our stemming method is compared with three state-of-the-art statistical algorithms and one rule-based algorithm. We used corpora in the Czech, Slovak, Polish, Hungarian, Spanish and English languages. In the tests, our algorithm excels in stemming previously unseen words (the words that are not present in the training set). Moreover, it was discovered that our approach demands very little text data for training when compared with competing unsupervised algorithms.

Source

Information processing and management. 51(2015) no.1, S.68-91

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Abstract

Today's conventional search engines hardly do provide the essential content relevant to the user's search query. This is because the context and semantics of the request made by the user is not analyzed to the full extent. So here the need for a semantic web search arises. SWS is upcoming in the area of web search which combines Natural Language Processing and Artificial Intelligence. The objective of the work done here is to design, develop and implement a semantic search engine- SIEU(Semantic Information Extraction in University Domain) confined to the university domain. SIEU uses ontology as a knowledge base for the information retrieval process. It is not just a mere keyword search. It is one layer above what Google or any other search engines retrieve by analyzing just the keywords. Here the query is analyzed both syntactically and semantically. The developed system retrieves the web results more relevant to the user query through keyword expansion. The results obtained here will be accurate enough to satisfy the request made by the user. The level of accuracy will be enhanced since the query is analyzed semantically. The system will be of great use to the developers and researchers who work on web. The Google results are re-ranked and optimized for providing the relevant links. For ranking an algorithm has been applied which fetches more apt results for the user query.

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Abstract

This article describes and evaluates various information retrieval models used to search document collections written in English through submitting queries written in various other languages, either members of the Indo-European family (English, French, German, and Spanish) or radically different language groups such as Chinese. This evaluation method involves searching a rather large number of topics (around 300) and using two commercial machine translation systems to translate across the language barriers. In this study, mean average precision is used to measure variances in retrieval effectiveness when a query language differs from the document language. Although performance differences are rather large for certain languages pairs, this does not mean that bilingual search methods are not commercially viable. Causes of the difficulties incurred when searching or during translation are analyzed and the results of concrete examples are explained.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.11, S.2266-2273

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Series

Advances in information science

Source

Journal of the American Society for Information Science and Technology. 63(2012) no.2, S.218-224

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Abstract

Specialized medical ontologies and terminologies, such as SNOMED CT and the Unified Medical Language System (UMLS), have been successfully leveraged in medical information systems to provide a standard web-accessible medium for interoperability, access, and reuse. However, these clinically oriented terminologies and ontologies cannot provide sufficient support when integrated into consumer-oriented applications, because these applications must "understand" both technical and lay vocabulary. The latter is not part of these specialized terminologies and ontologies. In this article, we propose a two-step approach for building consumer health terminologies from text: 1) automatic extraction of definitions from consumer-oriented articles and web documents, which reflects language in use, rather than relying solely on dictionaries, and 2) learning to map definitions expressed in natural language to terminological knowledge by inducing a syntactic-semantic grammar rather than using hand-written patterns or grammars. We present quantitative and qualitative evaluations of our two-step approach, which show that our framework could be used to induce consumer health terminologies from text.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.4, S.727-744

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Abstract

Subjective language contains information about private states. The goal of subjective language identification is to determine that a private state is expressed, without considering its polarity or specific emotion. A component of word meaning, "Personal Sense," has clear potential in the field of subjective language identification, as it reflects a meaning of words in terms of unique personal experience and carries personal characteristics. In this paper we investigate how Personal Sense can be harnessed for the purpose of identifying subjectivity in news titles. In the process, we develop a new Personal Sense annotation framework for annotating and classifying subjectivity, polarity, and emotion. The Personal Sense framework yields high performance in a fine-grained subsentence subjectivity classification. Our experiments demonstrate lexico-syntactic features to be useful for the identification of subjectivity indicators and the targets that receive the subjective Personal Sense.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1411-1422

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Abstract

This research creates an architecture for investigating the existence of probable lexical divergences between articles, categorized as Institute for Scientific Information (ISI) and non-ISI, and consequently, if such a difference is discovered, to propose the best available classification method. Based on a collection of ISI- and non-ISI-indexed articles in the areas of business and computer science, three classification models are trained. A sensitivity analysis is applied to demonstrate the impact of words in different syntactical forms on the classification decision. The results demonstrate that the lexical domains of ISI and non-ISI articles are distinguishable by machine learning techniques. Our findings indicate that the support vector machine identifies ISI-indexed articles in both disciplines with higher precision than do the Naïve Bayesian and K-Nearest Neighbors techniques.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.3, S.501-511

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Abstract

Rule-based and corpus-based machine translation (MT) have coexisted for more than 20 years. Recently, boundaries between the two paradigms have narrowed and hybrid approaches are gaining interest from both academia and businesses. However, since hybrid approaches involve the multidisciplinary interaction of linguists, computer scientists, engineers, and information specialists, understandably a number of issues exist. While statistical methods currently dominate research work in MT, most commercial MT systems are technically hybrid systems. The research community should investigate the benefits and questions surrounding the hybridization of MT systems more actively. This paper discusses various issues related to hybrid MT including its origins, architectures, achievements, and frustrations experienced in the community. It can be said that both rule-based and corpus- based MT systems have benefited from hybridization when effectively integrated. In fact, many of the current rule/corpus-based MT approaches are already hybridized since they do include statistics/rules at some point.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.10, S.2160-2165

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Abstract

In this paper we propose an effective sentence retrieval method that consists of incorporating query-independent features into standard sentence retrieval models. To meet this aim, we apply a formal methodology and consider different query-independent features. In particular, we show that opinion-based features are promising. Opinion mining is an increasingly important research topic but little is known about how to improve retrieval algorithms with opinion-based components. In this respect, we consider here different kinds of opinion-based features to act as query-independent evidence and study whether this incorporation improves retrieval performance. On the other hand, information needs are usually related to people, locations or organizations. We hypothesize here that using these named entities as query-independent features may also improve the sentence relevance estimation. Finally, the length of the retrieval unit has been shown to be an important component in different retrieval scenarios. We therefore include length-based features in our study. Our evaluation demonstrates that, either in isolation or in combination, these query-independent features help to improve substantially the performance of state-of-the-art sentence retrieval methods.

Source

Information processing and management. 48(2012) no.6, S.1203-1229

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Abstract

This paper presents an overview of automatic methods for building domain knowledge structures (domain models) from text collections. Applications of domain models have a long history within knowledge engineering and artificial intelligence. In the last couple of decades they have surfaced noticeably as a useful tool within natural language processing, information retrieval and semantic web technology. Inspired by the ubiquitous propagation of domain model structures that are emerging in several research disciplines, we give an overview of the current research landscape and some techniques and approaches. We will also discuss trade-offs between different approaches and point to some recent trends.

Source

Information processing and management. 48(2012) no.3, S.552-568

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Source

Information - Wissenschaft und Praxis. 61(2010) H.6/7, S.361-366