1. Articles in category: Entailment

    1-24 of 58 1 2 3 »
    1. A Recent Study Says That Natural Language Processing Industry Will Make a Huge Impact in Near Future in Europe Market

      A Recent Study Says That Natural Language Processing Industry Will Make a Huge Impact in Near Future in Europe Market

      RSS A Recent Study Says That Natural Language Processing Industry Will Make a Huge Impact in Near Future in Europe Market Global Natural Language Processing Market Research Report 2017. The report entails the qualitative and quantitative analysis of current and future market estimations. It also divulges details about the mode of research methodology used for the study.

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    2. Homework Assistant (Unreleased) is now in Open Beta in the Play Store

      Homework Assistant (Unreleased) is now in Open Beta in the Play Store

      Download Homework Assistant (Unreleased) apk latest version. - Download Homework Assistant (Unreleased) APK - Early Access 1 Beta Release Expect there to be bugs and quirks to be worked out as any beta release would entail. If you have any feedback please send feedback as I am actively looking into adding new feature and bug fixes. Enjoy the first of its kinda assistant tasked with helping you manage your busy school life.

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    3. Machine Translation (MT) Sales Market Forecast to 2017 Available in New Report

      Machine Translation (MT) Sales Market Forecast to 2017 Available in New Report

      RSS Machine Translation (MT) Sales Market Forecast to 2017 Available in New Report Research Beam has added a report on “Global Machine Translation (MT) Sales Market Research Report 2017”. The report entails the qualitative and quantitative analysis of current and future market estimations. Portland, OR -- ( SBWIRE ) -- 10/30/2017 -- Research Beam has added a report on " Global Machine Translation (MT) Sales Market Research Report 2017".

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    4. N&C Inc. Wins Tech Startup of the Year Award in Baltimore

      N&C Inc. Wins Tech Startup of the Year Award in Baltimore

      N&C Inc. Wins Tech Startup of the Year Award in Baltimore Share Article Broad spectrum of tech community members cast most votes for NLP solutions provider John W. Davis, Ray Bolger, Scott Hughes, and Ralph Bolton of N&C Inc. This recognition of N&C’s accomplishments to date means a lot to our founding team and employees, as we continue pressing forward to achieve major technical and commercial milestones (PRWEB) October 12, 2017 Notice and Comment (N&C) Inc., a provider of Natural Language Processing (NLP) solutions that augment human capacity for solving complex analytical challenges entailing unstructured ...

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      Mentions: Baltimore NLP
    5. Alibaba : to invest over $15bn in global research in 3yrs

      Mubasher:Chinese Alibaba on Wednesday announced plans to invest north of $15 billion in over the next three years under a global research and development programme, which aims to boost collaboration and develop new technologies. The new figure is “slightly more than double” the total amount e-commerce giant invested on research and development between 2014 and the fiscal year that ended 31 March, 2017 , according to CNBC .

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    6. Alibaba to invest over $15bn in global research in 3yrs

      Alibaba to invest over $15bn in global research in 3yrs

      Alibaba to invest over $15bn in global research in 3yrs The DAMO Academy programme will be launched in seven cities 11 October 2017 03:11 PM Mubasher: Chinese Alibaba on Wednesday announced plans to invest north of $15 billion in over the next three years under a global research and development programme, which aims to boost collaboration and develop new technologies.

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    7. Entailment evaluation device, entailment evaluation method, and recording medium

      An entailment evaluation device includes: a generation unit which generates first information indicating at least the order of occurrence of events of first and second simple sentences included in the hypothesis text and generates second information indicating at least the order of occurrence of events of third and fourth simple sentences included in a target text, the third simple sentence being related to the first simple sentence, the fourth simple sentence being related to the second simple sentence; a calculation unit which obtains a calculation result by comparing, based on the first and second information, the order of occurrence of ...

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    8. A Vector Space for Distributional Semantics for Entailment. (arXiv:1607.03780v1 [cs.CL])

      Distributional semantics creates vector-space representations that capture many forms of semantic similarity, but their relation to semantic entailment has been less clear. We propose a vector-space model which provides a formal foundation for a distributional semantics of entailment. Using a mean-field approximation, we develop approximate inference procedures and entailment operators over vectors of probabilities of features being known (versus unknown).

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    9. Addressing Limited Data for Textual Entailment Across Domains. (arXiv:1606.02638v1 [cs.CL])

      We seek to address the lack of labeled data (and high cost of annotation) for textual entailment in some domains. To that end, we first create (for experimental purposes) an entailment dataset for the clinical domain, and a highly competitive supervised entailment system, ENT, that is effective (out of the box) on two domains. We then explore self-training and active learning strategies to address the lack of labeled data.

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    10. Recognizing Entailment and Contradiction by Tree-based Convolution. (arXiv:1512.08422v1 [cs.CL])

      Recognizing entailment and contradiction between two sentences has wide applications in NLP. Traditional methods include feature-rich classifiers or formal reasoning. However, they are usually limited in terms of accuracy and scope. Recently, the renewed prosperity neural networks has made many improvements in a variety of NLP tasks.

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      Mentions: NLP
    11. Enacting textual entailment and ontologies for automated essay grading in chemical domain. (arXiv:1511.02669v1 [cs.AI])

      We propose a system for automated essay grading using ontologies and textual entailment. The process of textual entailment is guided by hypotheses, which are extracted from a domain ontology. Textual entailment checks if the truth of the hypothesis follows from a given text. We enact textual entailment to compare students answer to a model answer obtained from ontology. We validated the solution against various essays written by students in the chemistry domain.

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    12. A large annotated corpus for learning natural language inference. (arXiv:1508.05326v1 [cs.CL])

      Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations. However, machine learning research in this area has been dramatically limited by the lack of large-scale resources.

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    13. Textual entailment method for linking text of an abstract to text in the main body of a document

      A system and method are provided for processing an input document which enable assessment of the coherence of an abstract of the document. The method includes storing the document in memory and, for each sentence of the abstract, comparing the sentence with sentences of a main body of the document using textual entailment techniques to identify whether the sentence of the abstract entails a sentence in the main body of the document. Links can then be generated between the entailing sentences of the abstract and the corresponding entailed sentences of the document. The document and generated links are output. The ...

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    14. Refining the Judgment Threshold to Improve Recognizing Textual Entailment Using Similarity

      In recent years, Recognizing Textual Entailment (RTE) catches strongly the attention of the Natural Language Processing (NLP) community. Using Similarity is an useful method for RTE, in which the Judgment Threshold plays an important role as the learning model. This paper proposes an RTE model based on using similarity. We describe clearly the solutions to determine and to refine the Judgment Threshold for Improvement RTE. The measure of the synonym similarity also is considered. Experiments on a Vietnamese version of the RTE3 corpus are showed. Content Type Book ChapterPages 335-344DOI 10.1007/978-3-642-34707-8_34Authors Quang-Thuy Ha, College of Technology (UET), Vietnam ...
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    15. An Empirical Study of Recognizing Textual Entailment in Japanese Text

      Recognizing Textual Entailment (RTE) is a fundamental task in Natural Language Understanding. The task is to decide whether the meaning of a text can be inferred from the meaning of the other one. In this paper, we conduct an empirical study of the RTE task for Japanese, adopting a machine-learning-based approach. We quantitatively analyze the effects of various entailment features and the impact of RTE resources on the performance of a RTE system. This paper also investigates the use of Machine Translation for the RTE task and determines whether Machine Translation can be used to improve the performance of our ...
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    16. A Statistics-Based Semantic Textual Entailment System

      We present a Textual Entailment (TE) recognition system that uses semantic features based on the Universal Networking Language (UNL). The proposed TE system compares the UNL relations in both the text and the hypothesis to arrive at the two-way entailment decision. The system has been separately trained on each development corpus released as part of the Recognizing Textual Entailment (RTE) competitions RTE-1, RTE-2, RTE-3 and RTE-5 and tested on the respective RTE test sets. Content Type Book ChapterPages 267-276DOI 10.1007/978-3-642-25324-9_23Authors Partha Pakray, Computer Science and Engineering Department, Jadavpur University, Kolkata, IndiaUtsab Barman, Computer Science and Engineering Department, Jadavpur ...
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    17. An Entailment-Based Question Answering System over Semantic Web Data

      This paper reports a novel knowledge-based Question Answering (QA) method with the use of Semantic Web technologies and textual entailment recognition. Different from most of ontology-driven QA methods, this method does not perform deep question analysis to transform a natural language question into an ontology-compliant query for answer retrieval. Instead, it performs textual entailment recognition to discover the question template entailed by a user question from the whole machine-generated set and then takes the associated SPARQL query template to produce the complete query for retrieving the answers from the Semantic Web data that subscribe to the same ontology. An evaluation ...
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    18. A WordNet-based semantic approach to textual entailment and cross-lingual textual entailment

      Abstract  In this paper we explain how to build a recognizing textual entailment (RTE) system which only uses semantic similarity measures based on WordNet. We show how the widely used WordNet-based semantic measures can be generalized to build sentence level semantic metrics in order to be used in both mono-lingual and cross-lingual textual entailment. We experiment with a wide variety of RTE datasets and evaluate the contribution of an algorithm which expands the RTE monolingual corpus. Results achieved with this method yielded significant statistical differences when predicting RTE test sets. We provide an efficiency analysis of these metrics drawing some ...
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    19. Answer Validation through Textual Entailment

      Ongoing research work on an Answer Validation System (AV) based on Textual Entailment and Question Answering has been presented. A number of answer validation modules have been developed based on Textual Entailment, Named Entity Recognition, Question-Answer type analysis, Chunk boundary module and Syntactic similarity module. These answer validation modules have been integrated using a voting technique. We combine the question and the answer into the Hypothesis (H) and the Supporting Text as Text (T) to identify the entailment relation as either “VALIDATED” or “REJECTED”. The important features in the lexical Textual Entailment module are: WordNet based unigram match, bi-gram match ...
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    20. Defining Specialized Entailment Engines Using Natural Logic Relations

      In this paper we propose a framework for the definition and combination of specialized entailment engines, each of which able to deal with a certain aspect of language variability. Such engines are based on transformations, and we define them taking advantage of the conceptual and formal tools available from an extended model of Natural Logic (NL). Given a T,H pair, each engine performs atomic edits to solve the specific linguistic phenomenon it is built to deal with, and assigns an entailment relation as the output of this operation. NL mechanisms of semantic relations composition are then applied to join ...
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      Mentions: Natural Logic
    21. Answer Validation Using Textual Entailment

      We present an Answer Validation System (AV) based on Textual Entailment and Question Answering. The important features used to develop the AV system are Lexical Textual Entailment, Named Entity Recognition, Question-Answer type analysis, chunk boundary module and syntactic similarity module. The proposed AV system is rule based. We first combine the question and the answer into Hypothesis (H) and the Supporting Text as Text (T) to identify the entailment relation as either “VALIDATED” or “REJECTED”. The important features used for the lexical Textual Entailment module in the present system are: WordNet based unigram match, bigram match and skip-gram. In the ...
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    22. Recognizing Textual Entailment with Statistical Methods

      In this paper we propose a new cause-effect non-symmetric measure applied to the task of Recognizing Textual Entailment .First we searched over a big corpus for sentences which contains the discourse marker “because” and collected cause-effect pairs. The entailment recognition is based on measure the cause-effect relation between the text and the hypothesis using the relative frequencies of words from the cause-effect pairs. Our measure outperformed the baseline method, over the three test sets of the PASCAL Recognizing Textual Entailment Challenges (RTE). The measure shows to be good at discriminate over the “true” class. Therefore we develop a meta-classifier using ...
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    1-24 of 58 1 2 3 »
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