Literatur zur Informationserschließung

Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.

© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 18. September 2018)

Suchergebnisse

• 1
  Reyes Ayala, B. ; Knudson, R. ; Chen, J. ; Cao, G. ; Wang, X.: Metadata records machine translation combining multi-engine outputs with limited parallel data.
  In: Journal of the Association for Information Science and Technology. 69(2018) no.1, S.47-59.
  
  Abstract: One way to facilitate Multilingual Information Access (MLIA) for digital libraries is to generate multilingual metadata records by applying Machine Translation (MT) techniques. Current online MT services are available and affordable, but are not always effective for creating multilingual metadata records. In this study, we implemented 3 different MT strategies and evaluated their performance when translating English metadata records to Chinese and Spanish. These strategies included combining MT results from 3 online MT systems (Google, Bing, and Yahoo!) with and without additional linguistic resources, such as manually-generated parallel corpora, and metadata records in the two target languages obtained from international partners. The open-source statistical MT platform Moses was applied to design and implement the three translation strategies. Human evaluation of the MT results using adequacy and fluency demonstrated that two of the strategies produced higher quality translations than individual online MT systems for both languages. Especially, adding small, manually-generated parallel corpora of metadata records significantly improved translation performance. Our study suggested an effective and efficient MT approach for providing multilingual services for digital collections.
  
  Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23925/full.
  
  Themenfeld: Computerlinguistik
  
  
• 2
  Ghazzawi, N. ; Robichaud, B. ; Drouin, P. ; Sadat, F.: Automatic extraction of specialized verbal units.
  In: Terminology. 23(2018) no.2, S.207-237.
  
  Abstract: This paper presents a methodology for the automatic extraction of specialized Arabic, English and French verbs of the field of computing. Since nominal terms are predominant in terminology, our interest is to explore to what extent verbs can also be part of a terminological analysis. Hence, our objective is to verify how an existing extraction tool will perform when it comes to specialized verbs in a given specialized domain. Furthermore, we want to investigate any particularities that a language can represent regarding verbal terms from the automatic extraction perspective. Our choice to operate on three different languages reflects our desire to see whether the chosen tool can perform better on one language compared to the others. Moreover, given that Arabic is a morphologically rich and complex language, we consider investigating the results yielded by the extraction tool. The extractor used for our experiment is TermoStat (Drouin 2003). So far, our results show that the extraction of verbs of computing represents certain differences in terms of quality and particularities of these units in this specialized domain between the languages under question.
  
  Anmerkung: Vgl.: doi:10.1075/term.00002.gha.
  
  Themenfeld: Computerlinguistik
  
  
• 3
  Rötzer, F.: KI-Programm besser als Menschen im Verständnis natürlicher Sprache.[16. Januar 2018].
  In: https://www.heise.de/tp/features/KI-Programm-besser-als-Menschen-im-Verstaendnis-natuerlicher-Sprache-3941786.html?view=print.
  (Telepolis)
  
  Abstract: Jetzt scheint es allmählich ans Eingemachte zu gehen. Ein von der chinesischen Alibaba-Gruppe entwickelte KI-Programm konnte erstmals Menschen in der Beantwortung von Fragen und dem Verständnis von Text schlagen. Die chinesische Regierung will das Land führend in der Entwicklung von Künstlicher Intelligenz machen und hat dafür eine nationale Strategie aufgestellt. Dazu ernannte das Ministerium für Wissenschaft und Technik die Internetkonzerne Baidu, Alibaba und Tencent sowie iFlyTek zum ersten nationalen Team für die Entwicklung der KI-Technik der nächsten Generation. Baidu ist zuständig für die Entwicklung autonomer Fahrzeuge, Alibaba für die Entwicklung von Clouds für "city brains" (Smart Cities sollen sich an ihre Einwohner und ihre Umgebung anpassen), Tencent für die Enwicklung von Computervision für medizinische Anwendungen und iFlyTec für "Stimmenintelligenz". Die vier Konzerne sollen offene Plattformen herstellen, die auch andere Firmen und Start-ups verwenden können. Überdies wird bei Peking für eine Milliarde US-Dollar ein Technologiepark für die Entwicklung von KI gebaut. Dabei geht es selbstverständlich nicht nur um zivile Anwendungen, sondern auch militärische. Noch gibt es in den USA mehr KI-Firmen, aber China liegt bereits an zweiter Stelle. Das Pentagon ist beunruhigt. Offenbar kommt China rasch vorwärts. Ende 2017 stellte die KI-Firma iFlyTek, die zunächst auf Stimmerkennung und digitale Assistenten spezialisiert war, einen Roboter vor, der den schriftlichen Test der nationalen Medizinprüfung erfolgreich bestanden hatte. Der Roboter war nicht nur mit immensem Wissen aus 53 medizinischen Lehrbüchern, 2 Millionen medizinischen Aufzeichnungen und 400.000 medizinischen Texten und Berichten gefüttert worden, er soll von Medizinexperten klinische Erfahrungen und Falldiagnosen übernommen haben. Eingesetzt werden soll er, in China herrscht vor allem auf dem Land, Ärztemangel, als Helfer, der mit der automatischen Auswertung von Patientendaten eine erste Diagnose erstellt und ansonsten Ärzten mit Vorschlägen zur Seite stehen.
  
  Inhalt: Vgl.: http://www.heise.de/-3941786.
  
  Themenfeld: Computerlinguistik
  Wissenschaftsfach: Kognitionswissenschaft ; Informatik
  
  
• 4
  Çelebi, A. ; Özgür, A.: Segmenting hashtags and analyzing their grammatical structure.
  In: Journal of the Association for Information Science and Technology. 69(2018) no.5, S.675-686.
  
  Abstract: Originated as a label to mark specific tweets, hashtags are increasingly used to convey messages that people like to see in the trending hashtags list. Complex noun phrases and even sentences can be turned into a hashtag. Breaking hashtags into their words is a challenging task due to the irregular and compact nature of the language used in Twitter. In this study, we investigate feature-based machine learning and language model (LM)-based approaches for hashtag segmentation. Our results show that LM alone is not successful at segmenting nontrivial hashtags. However, when the N-best LM-based segmentations are incorporated as features into the feature-based approach, along with context-based features proposed in this study, state-of-the-art results in hashtag segmentation are achieved. In addition, we provide an analysis of over two million distinct hashtags, autosegmented by using our best configuration. The analysis reveals that half of all 60 million hashtag occurrences contain multiple words and 80% of sentiment is trapped inside multiword hashtags, justifying the need for hashtag segmentation. Furthermore, we analyze the grammatical structure of hashtags by parsing them and observe that 77% of the hashtags are noun-based, whereas 11.9% are verb-based.
  
  Inhalt: Vgl.: https://onlinelibrary.wiley.com/doi/abs/10.1002/asi.23989.
  
  Themenfeld: Metadaten ; Computerlinguistik
  Objekt: Hashtag
  
  
• 5
  Korman, D.Z. ; Mack, E. ; Jett, J. ; Renear, A.H.: Defining textual entailment.
  In: Journal of the Association for Information Science and Technology. 69(2018) no.6, S.763-772.
  
  Abstract: Textual entailment is a relationship that obtains between fragments of text when one fragment in some sense implies the other fragment. The automation of textual entailment recognition supports a wide variety of text-based tasks, including information retrieval, information extraction, question answering, text summarization, and machine translation. Much ingenuity has been devoted to developing algorithms for identifying textual entailments, but relatively little to saying what textual entailment actually is. This article is a review of the logical and philosophical issues involved in providing an adequate definition of textual entailment. We show that many natural definitions of textual entailment are refuted by counterexamples, including the most widely cited definition of Dagan et al. We then articulate and defend the following revised definition: T textually entails H?=?df typically, a human reading T would be justified in inferring the proposition expressed by H from the proposition expressed by T. We also show that textual entailment is context-sensitive, nontransitive, and nonmonotonic.
  
  Inhalt: Vgl.: https://onlinelibrary.wiley.com/doi/abs/10.1002/asi.24007.
  
  Themenfeld: Computerlinguistik
  
  
• 6
  Li, N. ; Sun, J.: Improving Chinese term association from the linguistic perspective.
  In: Knowledge organization. 44(2017) no.1, S.13-23.
  
  Abstract: The study aims to solve how to construct the semantic relations of specific domain terms by applying linguistic rules. The semantic structure analysis at the morpheme level was used for semantic measure, and a morpheme-based term association model was proposed by improving and combining the literal-based similarity algorithm and co-occurrence relatedness methods. This study provides a novel insight into the method of semantic analysis and calculation by morpheme parsing, and the proposed solution is feasible for the automatic association of compound terms. The results show that this approach could be used to construct appropriate term association and form a reasonable structural knowledge graph. However, due to linguistic differences, the viability and effectiveness of the use of our method in non-Chinese linguistic environments should be verified.
  
  Themenfeld: Semantisches Umfeld in Indexierung u. Retrieval ; Computerlinguistik
  
  
• 7
  AL-Smadi, M. ; Jaradat, Z. ; AL-Ayyoub, M. ; Jararweh, Y.: Paraphrase identification and semantic text similarity analysis in Arabic news tweets using lexical, syntactic, and semantic features.
  In: Information processing and management. 53(2017) no.3, S.640-652.
  
  Abstract: The rapid growth in digital information has raised considerable challenges in particular when it comes to automated content analysis. Social media such as twitter share a lot of its users' information about their events, opinions, personalities, etc. Paraphrase Identification (PI) is concerned with recognizing whether two texts have the same/similar meaning, whereas the Semantic Text Similarity (STS) is concerned with the degree of that similarity. This research proposes a state-of-the-art approach for paraphrase identification and semantic text similarity analysis in Arabic news tweets. The approach adopts several phases of text processing, features extraction and text classification. Lexical, syntactic, and semantic features are extracted to overcome the weakness and limitations of the current technologies in solving these tasks for the Arabic language. Maximum Entropy (MaxEnt) and Support Vector Regression (SVR) classifiers are trained using these features and are evaluated using a dataset prepared for this research. The experimentation results show that the approach achieves good results in comparison to the baseline results.
  
  Inhalt: Vgl.: http://www.sciencedirect.com/science/article/pii/S0306457316302382.
  
  Themenfeld: Computerlinguistik
  
  
• 8
  Lu, K. ; Cai, X. ; Ajiferuke, I. ; Wolfram, D.: Vocabulary size and its effect on topic representation.
  In: Information processing and management. 53(2017) no.3, S.653-665.
  
  Abstract: This study investigates how computational overhead for topic model training may be reduced by selectively removing terms from the vocabulary of text corpora being modeled. We compare the impact of removing singly occurring terms, the top 0.5%, 1% and 5% most frequently occurring terms and both top 0.5% most frequent and singly occurring terms, along with changes in the number of topics modeled (10, 20, 30, 40, 50, 100) using three datasets. Four outcome measures are compared. The removal of singly occurring terms has little impact on outcomes for all of the measures tested. Document discriminative capacity, as measured by the document space density, is reduced by the removal of frequently occurring terms, but increases with higher numbers of topics. Vocabulary size does not greatly influence entropy, but entropy is affected by the number of topics. Finally, topic similarity, as measured by pairwise topic similarity and Jensen-Shannon divergence, decreases with the removal of frequent terms. The findings have implications for information science research in information retrieval and informetrics that makes use of topic modeling.
  
  Inhalt: Vgl.: http://www.sciencedirect.com/science/article/pii/S0306457317300298.
  
  Themenfeld: Computerlinguistik
  
  
• 9
  Engerer, V.: Exploring interdisciplinary relationships between linguistics and information retrieval from the 1960s to today.
  In: Journal of the Association for Information Science and Technology. 68(2017) no.3, S.660-680.
  
  Abstract: This article explores how linguistics has influenced information retrieval (IR) and attempts to explain the impact of linguistics through an analysis of internal developments in information science generally, and IR in particular. It notes that information science/IR has been evolving from a case science into a fully fledged, "disciplined"/disciplinary science. The article establishes correspondences between linguistics and information science/IR using the three established IR paradigms-physical, cognitive, and computational-as a frame of reference. The current relationship between information science/IR and linguistics is elucidated through discussion of some recent information science publications dealing with linguistic topics and a novel technique, "keyword collocation analysis," is introduced. Insights from interdisciplinarity research and case theory are also discussed. It is demonstrated that the three stages of interdisciplinarity, namely multidisciplinarity, interdisciplinarity (in the narrow sense), and transdisciplinarity, can be linked to different phases of the information science/IR-linguistics relationship and connected to different ways of using linguistic theory in information science and IR.
  
  Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23684/full [DOI: 10.1002/asi.23684].
  
  Themenfeld: Computerlinguistik ; Geschichte der Sacherschließung
  
  
• 10
  Rozinajová, V. ; Macko, P.: Using natural language to search linked data.
  In: Semantic keyword-based search on structured data sources: COST Action IC1302. Second International KEYSTONE Conference, IKC 2016, Cluj-Napoca, Romania, September 8-9, 2016, Revised Selected Papers. Eds.: A. Calì, A. et al. Springer International Publishing, 2017. S.179-189.
  (Information Systems and Applications, incl. Internet/Web, and HCI; 10151)
  
  Abstract: There are many endeavors aiming to offer users more effective ways of getting relevant information from web. One of them is represented by a concept of Linked Data, which provides interconnected data sources. But querying these types of data is difficult not only for the conventional web users but also for ex-perts in this field. Therefore, a more comfortable way of user query would be of great value. One direction could be to allow the user to use a natural language. To make this task easier we have proposed a method for translating natural language query to SPARQL query. It is based on a sentence structure - utilizing dependen-cies between the words in user queries. Dependencies are used to map the query to the semantic web structure, which is in the next step translated to SPARQL query. According to our first experiments we are able to answer a significant group of user queries.
  
  Inhalt: Vgl. auch: http://www.keystone-cost.eu/ikc2016/program.php.
  
  Themenfeld: Computerlinguistik
  
  
• 11
  Babik, W.: Keywords as linguistic tools in information and knowledge organization.
  In: Theorie, Semantik und Organisation von Wissen: Proceedings der 13. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation (ISKO) und dem 13. Internationalen Symposium der Informationswissenschaft der Higher Education Association for Information Science (HI) Potsdam (19.-20.03.2013): 'Theory, Information and Organization of Knowledge' / Proceedings der 14. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation (ISKO) und Natural Language & Information Systems (NLDB) Passau (16.06.2015): 'Lexical Resources for Knowledge Organization' / Proceedings des Workshops der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation (ISKO) auf der SEMANTICS Leipzig (1.09.2014): 'Knowledge Organization and Semantic Web' / Proceedings des Workshops der Polnischen und Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation (ISKO) Cottbus (29.-30.09.2011): 'Economics of Knowledge Production and Organization'. Hrsg. von W. Babik, H.P. Ohly u. K. Weber. Würzburg : Ergon Verlag, 2017. S.306-313.
  (Fortschritte in der Wissensorganisation; Bd.13)
  
  Themenfeld: Computerlinguistik
  
  
• 12
  Collovini de Abreu, S. ; Vieira, R.: RelP: Portuguese open relation extraction.
  In: Knowledge organization. 44(2017) no.3, S.163-177.
  
  Abstract: Natural language texts are valuable data sources in many human activities. NLP techniques are being widely used in order to help find the right information to specific needs. In this paper, we present one such technique: relation extraction from texts. This task aims at identifying and classifying semantic relations that occur between entities in a text. For example, the sentence "Roberto Marinho is the founder of Rede Globo" expresses a relation occurring between "Roberto Marinho" and "Rede Globo." This work presents a system for Portuguese Open Relation Extraction, named RelP, which extracts any relation descriptor that describes an explicit relation between named entities in the organisation domain by applying the Conditional Random Fields. For implementing RelP, we define the representation scheme, features based on previous work, and a reference corpus. RelP achieved state of the art results for open relation extraction; the F-measure rate was around 60% between the named entities person, organisation and place. For better understanding of the output, we present a way for organizing the output from the mining of the extracted relation descriptors. This organization can be useful to classify relation types, to cluster the entities involved in a common relation and to populate datasets.
  
  Inhalt: Beitrag in einem Special Issue "New Trends for Knowledge Organization, Guest Editor: Renato Rocha Souza".
  
  Themenfeld: Computerlinguistik
  
  
• 13
  Gill, A.J. ; Hinrichs-Krapels, S. ; Blanke, T. ; Grant, J. ; Hedges, M. ; Tanner, S.: Insight workflow : systematically combining human and computational methods to explore textual data.
  In: Journal of the Association for Information Science and Technology. 68(2017) no.7, S.1671-1686.
  
  Abstract: Analyzing large quantities of real-world textual data has the potential to provide new insights for researchers. However, such data present challenges for both human and computational methods, requiring a diverse range of specialist skills, often shared across a number of individuals. In this paper we use the analysis of a real-world data set as our case study, and use this exploration as a demonstration of our "insight workflow," which we present for use and adaptation by other researchers. The data we use are impact case study documents collected as part of the UK Research Excellence Framework (REF), consisting of 6,679 documents and 6.25 million words; the analysis was commissioned by the Higher Education Funding Council for England (published as report HEFCE 2015). In our exploration and analysis we used a variety of techniques, ranging from keyword in context and frequency information to more sophisticated methods (topic modeling), with these automated techniques providing an empirical point of entry for in-depth and intensive human analysis. We present the 60 topics to demonstrate the output of our methods, and illustrate how the variety of analysis techniques can be combined to provide insights. We note potential limitations and propose future work.
  
  Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23767/full.
  
  Themenfeld: Computerlinguistik ; Data Mining
  
  
• 14
  Kauchak, D. ; Leroy, G. ; Hogue, A.: Measuring text difficulty using parse-tree frequency.
  In: Journal of the Association for Information Science and Technology. 68(2017) no.9, S.2088-2100.
  
  Abstract: Text simplification often relies on dated, unproven readability formulas. As an alternative and motivated by the success of term familiarity, we test a complementary measure: grammar familiarity. Grammar familiarity is measured as the frequency of the 3rd level sentence parse tree and is useful for evaluating individual sentences. We created a database of 140K unique 3rd level parse structures by parsing and binning all 5.4M sentences in English Wikipedia. We then calculated the grammar frequencies across the corpus and created 11 frequency bins. We evaluate the measure with a user study and corpus analysis. For the user study, we selected 20 sentences randomly from each bin, controlling for sentence length and term frequency, and recruited 30 readers per sentence (N = 6,600) on Amazon Mechanical Turk. We measured actual difficulty (comprehension) using a Cloze test, perceived difficulty using a 5-point Likert scale, and time taken. Sentences with more frequent grammatical structures, even with very different surface presentations, were easier to understand, perceived as easier, and took less time to read. Outcomes from readability formulas correlated with perceived but not with actual difficulty. Our corpus analysis shows how the metric can be used to understand grammar regularity in a broad range of corpora.
  
  Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23855/full.
  
  Themenfeld: Computerlinguistik
  
  
• 15
  Wolfangel, E.: Ich verstehe.
  In: ¬Die Rheinpfalz am Sonntag. 26.11.2017, S.17.
  (Wissen: Technik, Forschung, Umwelt, Mensch)
  
  Abstract: Computer werden immer besser darin, den Sinn von Sprache zu entschlüsseln. Das ist zwar bequem, wenn man seinem Handy einfach sagen kann, was es tun soll, ohne tippen zu müssen. Aber das bedeutet auch, dass sich E-Mails und andere Botschaften maschinell auswerten lassen. Da wird inzwischen sogar den Forschern mulmig.
  
  Themenfeld: Computerlinguistik
  
  
• 16
  Perovsek, M. ; Kranjca, J. ; Erjaveca, T. ; Cestnika, B. ; Lavraca, N.: TextFlows : a visual programming platform for text mining and natural language processing.
  In: Science of computer programming. In Press, 2016.
  
  Abstract: Text mining and natural language processing are fast growing areas of research, with numerous applications in business, science and creative industries. This paper presents TextFlows, a web-based text mining and natural language processing platform supporting workflow construction, sharing and execution. The platform enables visual construction of text mining workflows through a web browser, and the execution of the constructed workflows on a processing cloud. This makes TextFlows an adaptable infrastructure for the construction and sharing of text processing workflows, which can be reused in various applications. The paper presents the implemented text mining and language processing modules, and describes some precomposed workflows. Their features are demonstrated on three use cases: comparison of document classifiers and of different part-of-speech taggers on a text categorization problem, and outlier detection in document corpora.
  
  Inhalt: Vgl.: http://www.sciencedirect.com/science/article/pii/S0167642316000113. Vgl. auch: http://textflows.org.
  
  Themenfeld: Computerlinguistik
  Objekt: TextFlows
  
  
• 17
  Radev, D.R. ; Joseph, M.T. ; Gibson, B. ; Muthukrishnan, P.: ¬A bibliometric and network analysis of the field of computational linguistics.
  In: Journal of the Association for Information Science and Technology. 67(2016) no.3, S.683-706.
  
  Abstract: The ACL Anthology is a large collection of research papers in computational linguistics. Citation data were obtained using text extraction from a collection of PDF files with significant manual postprocessing performed to clean up the results. Manual annotation of the references was then performed to complete the citation network. We analyzed the networks of paper citations, author citations, and author collaborations in an attempt to identify the most central papers and authors. The analysis includes general network statistics, PageRank, metrics across publication years and venues, the impact factor and h-index, as well as other measures.
  
  Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23394/abstract.
  
  Themenfeld: Informetrie ; Computerlinguistik
  
  
• 18
  Baierer, K. ; Zumstein, P.: Verbesserung der OCR in digitalen Sammlungen von Bibliotheken.
  In: 027.7 Zeitschrift für Bibliothekskultur. 4(2016), H.2.
  
  Abstract: Möglichkeiten zur Verbesserung der automatischen Texterkennung (OCR) in digitalen Sammlungen insbesondere durch computerlinguistische Methoden werden beschrieben und bisherige PostOCR-Verfahren analysiert. Im Gegensatz zu diesen Möglichkeiten aus der Forschung oder aus einzelnen Projekten unterscheidet sich die momentane Anwendung von OCR in der Bibliothekspraxis wesentlich und nutzt das Potential nur teilweise aus.
  
  Inhalt: Beitrag in einem Themenschwerpunkt 'Computerlinguistik und Bibliotheken'. Vgl.: http://0277.ch/ojs/index.php/cdrs_0277/article/view/155/353.
  
  Themenfeld: Computerlinguistik
  Hilfsmittel: OCR
  
  
• 19
  Menge-Sonnentag, R.: Google veröffentlicht einen Parser für natürliche Sprache.
  In: heise Developer (13.05.2016), [http://www.heise.de/developer/meldung/Google-veroeffentlicht-einen-Parser-fuer-natuerliche-Sprache-3207846.html].
  
  Abstract: SyntaxNet zerlegt Sätze in ihre grammatikalischen Bestandteile und bestimmt die syntaktischen Beziehungen der Wörter untereinander. Das Framework ist Open Source und als TensorFlow Model implementiert. Ein Parser für natürliche Sprache ist eine Software, die Sätze in ihre grammatikalischen Bestandteile zerlegt. Diese Zerlegung ist notwendig, damit Computer Befehle verstehen oder Texte übersetzen können. Die digitalen Helfer wie Microsofts Cortana, Apples Siri und Google Now verwenden Parser, um Sätze wie "Stell den Wecker auf 5 Uhr!" richtig umzusetzen. SyntaxNet ist ein solcher Parser, den Google als TensorFlow Model veröffentlicht hat. Entwickler können eigene Modelle erstellen, und SnytaxNet bringt einen vortrainierten Parser für die englische Sprache mit, den seine Macher Parsey McParseface genannt haben.
  
  Inhalt: "Syntaktische Beziehungen Der Parser teilt den Wörtern eine syntaktische Funktion zu und untersucht die syntaktischen Beziehungen zwischen den Einzelteilen. Den englischen Beispielsatz aus dem Blog-Beitrag "Alice saw Bob" analysiert er folgendermaßen: "Alice" und "Bob" sind Substantive, und "saw" ist ein Verb. Letzteres ist gleichzeitig die Wurzel (ROOT), von der die restlichen Beziehungen ausgehen. Alice ist das zugehörige Subjekt (nsubj) und Bob das Objekt (dobj). Längere Sätze werden leicht mehrdeutig. Beispielsweise ist im Satz "Alice sah Bob mit dem Fernglas" nicht erkennbar, wer von den beiden das Fernglas in der Hand hält. Rein syntaktisch ist auch der Satz "Peter schneidet das Brot mit Sonnenblumenkernen" mehrdeutig. Das menschliche Gehirn erkennt die richtige Bedeutung recht zuverlässig, aber für maschinelle Parser stellen sie eine Herausforderung dar. ; SyntaxNet nutzt zur Entscheidung neuronale Netze und versucht die Abhängigkeiten richtig zuzuordnen. Damit "lernt" der Parser, dass es schwierig ist, Sonnenblumenkerne zum Schneiden einzusetzen, und sie somit wohl eher Bestandteil des Brots als ein Werkzeug sind. Die Analyse beschränkt sich jedoch auf den Satz selbst. Semantische Zusammenhänge berücksichtigt das Modell nicht. So lösen sich manche Mehrdeutigkeiten durch den Kontext auf: Wenn Alice im obigen Beispiel das Fernglas beim Verlassen des Hauses eingepackt hat, wird sie es vermutlich benutzen. Trefferquote Mensch vs. Maschine Laut dem Blog-Beitrag kommt Parsey McParseface auf eine Genauigkeit von gut 94 Prozent für Sätze aus dem Penn Treebank Project. Die menschliche Quote soll laut Linguisten bei 96 bis 97 Prozent liegen. Allerdings weist der Beitrag auch darauf hin, dass es sich bei den Testsätzen um wohlgeformte Texte handelt. Im Test mit Googles WebTreebank erreicht der Parser eine Genauigkeit von knapp 90 Prozent."
  
  Anmerkung: Download unter: https://github.com/tensorflow/models/tree/master/syntaxnet. Dort befinden sich auch weitere Information zu dem Modell sowie Vergleichszahlen zur Erkennungsrate.
  
  Themenfeld: Computerlinguistik
  Objekt: SyntaxNet
  
  
• 20
  Savoy, J.: Text representation strategies : an example with the State of the union addresses.
  In: Journal of the Association for Information Science and Technology. 67(2016) no.8, S.1858-1870.
  
  Abstract: Based on State of the Union addresses from 1790 to 2014 (225 speeches delivered by 42 presidents), this paper describes and evaluates different text representation strategies. To determine the most important words of a given text, the term frequencies (tf) or the tf?idf weighting scheme can be applied. Recently, latent Dirichlet allocation (LDA) has been proposed to define the topics included in a corpus. As another strategy, this study proposes to apply a vocabulary specificity measure (Z?score) to determine the most significantly overused word-types or short sequences of them. Our experiments show that the simple term frequency measure is not able to discriminate between specific terms associated with a document or a set of texts. Using the tf idf or LDA approach, the selection requires some arbitrary decisions. Based on the term-specific measure (Z?score), the term selection has a clear theoretical basis. Moreover, the most significant sentences for each presidency can be determined. As another facet, we can visualize the dynamic evolution of usage of some terms associated with their specificity measures. Finally, this technique can be employed to define the most important lexical leaders introducing terms overused by the k following presidencies.
  
  Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23510/abstract.
  
  Themenfeld: Computerlinguistik