LingPipe is a software library that provides a suite of natural language processing (NLP) tools for tasks such as tokenization, part-of-speech tagging, and named entity recognition. LingPipe is written in Java, and it is designed to be easy to use and flexible, allowing developers to build a wide range of NLP applications.
One common use case for LingPipe is in the development of text analysis or information extraction systems, where it can be used to extract structured information from unstructured text. For example, a system built with LingPipe might be used to extract names and contact information from a set of business cards, or to extract dates and locations from a set of news articles.
LingPipe also provides tools for machine learning, allowing developers to train and evaluate machine learning models for NLP tasks such as classification and clustering. This makes it possible to build more advanced NLP systems that can adapt and improve over time as they are exposed to more data.
LingPipe can be used to build various components of a dialog system, such as natural language understanding (NLU) modules that can parse and interpret user input. NLU is a critical component of a dialog system, as it is responsible for extracting the meaning and intent behind the user’s words.
One common way to use LingPipe for NLU is to train a machine learning model to classify user input into a set of predefined intents. For example, a dialog system for booking a hotel room might have intents such as “book_room,” “check_availability,” and “cancel_reservation.” LingPipe could be used to train a model that takes a user’s input and assigns it to one of these intents, which the dialog system can then use to determine the appropriate response.
LingPipe can also be used to extract structured information from user input, such as dates, locations, and other entities that may be relevant to the dialog. For example, a user might say “I want to book a room for two nights starting on June 1st,” and LingPipe could be used to extract the dates “June 1st” and “two nights” and pass them to the dialog system.
Designing an interactive open-domain question answering system S Quarteroni, S Manandhar – Natural Language Engineering, 2009 – Cambridge Univ Press … or location (LOC), which corresponds to the types of Named Entities (NEs) recognized by the NE recognizer (Lingpipe in our … Hence, the design of task-oriented dialogue systems cannot happen without an accurate analysis of the conversational phenomena observed in human … Cited by 57 Related articles All 12 versions
Towards the rapid development of a natural language understanding module C Moreira, AC Mendes, L Coheur, B Martins – Intelligent Virtual Agents, 2011 – Springer … Here, we follow the approach described in , although their focus is on frame-based dialogue systems. … This process uses the LingPipe3 implementation of the Aho-Corasick algorithm , that searches for matches against a dictionary in linear time in terms of the length of the … Cited by 7 Related articles All 13 versions
The University of Washington’s UWclmaQA System. D Jinguji, WD Lewis, EN Efthimiadis, J Minor… – TREC, 2006 – researchgate.net … The system architecture is shown in Figure 1. The system was built using primarily open source tools, such as Lucene and LingPipe, which were unified into one system design using customized enhancements. … This type of “dialog-system” would be supported by our architecture … Cited by 4 Related articles All 5 versions
[BOOK] Natural Language Processing with Java and LingPipe Cookbook B Baldwin, K Dayanidhi – 2014 – books.google.com … It was his idea to make LingPipe open source, which opened many doors and led to this book. … Since first being introducedto the fieldin 2006,hehas worked ondiverse problems suchas spoken dialog systems, machine translation, text normalization, coreference resolution, and … All 2 versions
Harnessing context incongruity for sarcasm detection A Joshi, V Sharma… – Proceedings of the 53rd …, 2015 – anthology.aclweb.org … Page 790. References Alias-i. 2008. Lingpipe natural language toolkit. Francesco Barbieri, Horacio Saggion, and Francesco Ronzano. 2014. … 2006. yeah right: sarcasm recognition for spoken dialogue systems. In INTERSPEECH. Oren Tsur, Dmitry Davidov, and Ari Rappoport. … Cited by 2
Texterra: A framework for text analysis DY Turdakov, NA Astrakhantsev, YR Nedumov… – … and Computer Software, 2014 – Springer … 2 http://opennlp.apache.org 3 http://alias i.com/lingpipe … recommend items; • analysis of text messages in social networks and forums to determine hidden demographic attributes ; • design of question answering systems, automatic summarization systems, dialog systems, etc. … Cited by 6 Related articles All 4 versions
Crowdflow: Integrating machine learning with mechanical turk for speed-cost-quality flexibility AJ Quinn, BB Bederson, T Yeh, J Lin – Better performance over iterations, 2010 – wp.aq.gs … The classification code is provided with the LingPipe natural language processing toolkit for Java (http://alias i.com/lingpipe/). …  Horvitz, E., Paek, T.. Complementary computing: policies for transferring callers from dialog systems to human receptionists, User Modeling and User … Cited by 25 Related articles All 35 versions
Hierarchical Discourse Parsing Based on Similarity Metrics. R Vadlapudi, P Malepati, S Yelati – RANLP, 2009 – aclweb.org … Discourse parsing finds its applications in a va- riety of fields some of which include summarization (focused summaries), dialog systems (language gener- ation … 2.1 First Phase In the first phase, we use the output of the co-reference module (LingPipe) to create a collection of … Related articles All 7 versions
Geographical information resolution and its application to the question answering systems DF Domenech, HR Hontoria – 2007 – Citeseer … simple rules that detected important words in the person’s input. GUS was a dialog system for airline reservation. … Human-Machine interaction is done by dialog systems that allow to do ques- tions in the context of previous interactions. … Cited by 2 Related articles All 2 versions
An introduction to question answering over linked data C Unger, A Freitas, P Cimiano – Reasoning Web. Reasoning on the Web …, 2014 – Springer Page 1. An Introduction to Question Answering over Linked Data Christina Unger 1 , André Freitas 2 , and Philipp Cimiano 1 1 CITEC, Bielefeld University, Inspiration 1, 33615 Bielefeld 2 Insight, National University of Galway (NUIG), Galway Abstract. … Cited by 5 Related articles All 4 versions
SHARED tasks and comparative evaluation in natural language generation R DALE, M WHITE – 2012-02-15)[2012-08-01]. http://www. ling. ohio- … – ling.ohio-state.edu Page 1. SHARED TASKS AND COMPARATIVE EVALUATION IN NATURAL LANGUAGE GENERATION Workshop Report Edited by ROBERT DALE Macquarie University MICHAEL WHITE The Ohio State University Page 2. Page 3. Contents Preface iii Acknowledgements v … Cited by 2 Related articles All 8 versions
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NaLIX: A generic natural language search environment for XML data Y Li, H Yang, HV Jagadish – ACM Transactions on Database Systems ( …, 2007 – dl.acm.org … Management]: Languages—Query lan- guages; H.4.0 [Information Systems Applications]: General General Terms: System, Design, Algorithms, Experimentation Additional Key Words and Phrases: Natural language interface, iterative search, XQuery, XML, dialog system … Cited by 26 Related articles All 10 versions
Generating Paraphrases with Greater Variation Using Syntactic Phrases RD Madsen – 2006 – scholarsarchive.byu.edu … recognition output and correct the errors. This technique also helps when coupled with spoken dialogue systems to improve understanding and robustness of the dialogue system. Optical character recognition (Kolak et al., 2003) can also use a noisy channel … Related articles All 3 versions
Linguistic Approach to Information Extraction and Sentiment Analysis on Twitter S Nepal – 2012 – rave.ohiolink.edu Page 1. Page 2. Linguistic Approach to Information Extraction and Sentiment Analysis on Twitter by Srijan Nepal A thesis submitted in partial satisfaction of the requirements for the degree of Master of Science in Computer Science and Engineering in the … Related articles All 3 versions
Advanced Techniques for Personalized, Interactive Question Answering S Quarteroni – 2007 – Citeseer … Page 18. 17 Question Answering in the late 1970s and until the end of the 1980s was tightly linked to human-computer dialogue systems, such as expert systems drawing information from structured knowledge bases. Indeed … Cited by 7 Related articles All 6 versions
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Sentence-based sentiment analysis for expressive text-to-speech A Trilla, F Alias – Audio, Speech, and Language Processing, …, 2013 – ieeexplore.ieee.org … ARN-R is implemented following . • LSA uses the SVD implementation provided by Ling- Pipe3 to construct a latent semantic space . • MLR uses the Stochastic Gradient Descent optimisation procedure provided by LingPipe . … 3http://alias-i.com/lingpipe/ … Cited by 16 Related articles All 5 versions
Answering open-domain temporally restricted questions in a multilingual context R Basten – Master’s thesis, University of Twente and LT- …, 2005 – fotoalbumgeerte.berteun.nl … object’. Defini- tion questions are for example: ‘Who is Heinrich Böll?’ or ‘What is a computer?’ dialogue system System that interacts with its users in order to give them information or accomplish some task for them. Instead of … Cited by 2 Related articles All 6 versions
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Novel Methods for Text Preprocessing and Classification T Gasanova – 2015 – vts.uni-ulm.de … 87 2.17 Co-Operation of Biology Related Algorithms (COBRA) . . . . 89 3.1 Overview of Spoken Dialogue Systems . . . . . 105 4.1 Common diagramm of text preprocessing and text classification . . . . . …