Sentence Extraction Module


The sentence extraction module is a component of a dialog system that is responsible for extracting individual sentences or clauses from a larger piece of text. This module typically receives the user’s input in the form of natural language text, along with any additional context or information that the system has about the user or the current conversation. It then uses natural language processing techniques to analyze the input and identify the individual sentences or clauses that are contained within it.

The sentence extraction module typically includes a set of rules or algorithms that define how the system should extract individual sentences or clauses from the input text. For example, the module may use rules to identify the boundaries between sentences, such as punctuation marks or changes in capitalization. It may also use algorithms to identify the structure and meaning of the individual sentences or clauses, such as the presence of subject-verb-object or other grammatical constructions.

Once the sentences or clauses have been extracted, they are passed to other modules in the system, such as the natural language understanding module or the conversation control module, which use them to generate appropriate responses or actions.

See also:

Dialog System Modules | Sentence Boundary Disambiguation & Dialog Systems | Sentence Extractor | Sentence Generation ModuleSentence Grammaticality | Sentence Parsers & Dialog Systems | Sentence Patterns & Dialog SystemsSentence Recognizer | Sentence Splitter 2011

MULTILINGUAL SENTENCE EXTRACTOR M Litvak, M Last, M Friedman – US Patent 20,120,035,912, 2012 – … For example, specific electronic components may be employed in a multilingual sentence extractor module or similar or related circuitry for implementing the functions associated with multilingual sentence extraction in accordance with the present disclosure as described above … Cached

Multi-document summarisation using genetic algorithm-based sentence extraction A Kogilavani, P Balasubramanie – International Journal of Computer …, 2011 – Inderscience … statistical module • GA-based sentence extraction module. Statistical module consists of various steps called preprocessing, linguistic analysis, sentence score calculation using sentence-specific features and sentence similarity matrix construction. … Related articles All 6 versions

[PDF] from Similarity measures and diversity rankings for query-focused sentence extraction P Achananuparp – 2010 – Page 1. Similarity Measures and Diversity Rankings for Query-Focused Sentence Extraction A Thesis Submitted to the Faculty of Drexel University by Palakorn Achananuparp In partial fulfillment of the requirements for the degree of Doctor of Philosophy May 2010 Page 2. … Related articles Library Search All 6 versions

[PDF] from Opinion sentence search engine on open-domain blog O Furuse, N Hiroshima, S Yamada… – Proc. of 20th Int. Joint Conf. …, 2007 – … extraction and query-relevant sentence extraction. The opinion sentence extraction module checks whether each sentence in the crawled blog pages can be considered an opinion. Opinion sentences are extracted and indexed … Cited by 20 Related articles All 16 versions

[PDF] from [PDF] Acquiring Relational Patterns from Wikipedia: A Case Study R Mahendra, L Wanzare, R Bernardi, A Lavelli… – Proceedings of the 5th …, 2011 – … <> <> <> the sentence extractor module would return the following sentence (1), where both the domain and the range of the writer relation are highlighted. … Cited by 1 View as HTML

Document summarisation using combination and reduction of extracted sentences GK Parai, T Tenneti, PK Borah, S Shah – International Journal of …, 2009 – Inderscience … 195 sentences which would be extracted depends on the size of the summary as specified by the user. The sentence extraction module extracts an additional of 10% of the total sentences in the document, more than the size specified by the user. … Related articles All 5 versions

Clustering based optimal summary generation using Genetic Algorithm A Kogilavani, P Balasubramanie – … Intelligence (INCOCCI), 2010 …, 2010 – Page 1. Proceedings of the International Conference on Communication and Computational Intelligence – 2010, Kongu Engineering College, Perundurai, Erode, TN,India.27 – 29 December,2010.pp.324-329. 324 Clustering Based Optimal Summary Generation Using … Related articles

[PDF] from [PDF] Kannada Word Sense Disambiguation for Machine Translation S Parameswarappa… – International Journal of …, 2011 – … In the subsequent sections we will use some of the example sentences not available in Figure 3 to illustrate the concepts. 3.2 Sentence extractor The input for sentence extractor module is Kannada raw corpora. … These sentences are selected by sentence extractor module. … Related articles View as HTML All 4 versions

[PDF] from [PDF] TAC 2008 Update Summarization Task of ICL S Li, W Wan, C Wang – Proceedings of the Text Analysis Conference (TAC), 2008 – … The focus of sentence extraction module is on which feature to extract and how to rank the importance of each sentence with reference to their features. In the post-processing module, sentences with higher scores are extracted to compose of the summary with MMR method. … Cited by 4 Related articles View as HTML All 2 versions

[PDF] from [PDF] Experimenting with clause segmentation for text summarization S Wan, C Paris – Proceedings of the 1st TAC. Gaithersburg, MD, 2008 – … Pyr. SCU’s Ling. Resp. SS 0.13 (52) 1.62 (53) 1.25 (56) 1.45 (52) CS 0.10 (50) 1.29 (51) 1.35 (57) 1.37 (52) Figure 5: Results from the TAC 2008 Evaluation. the other systems. This is not surprising since a very simple sentence extraction module was used to choose sentences. … Cited by 1 Related articles View as HTML All 2 versions

Systems and methods for record linkage and paraphrase generation using surrogate learning S Veeramachaneni – US Patent App. 12/367,371, 2009 – Google Patents … [0047] In addition to processors 501, system 500 includes a memory 502 which stores a sentence corpus or database 510, a source sentence extractor module 520, a target and back- ground sentence extractor module 53 0, a paraphrase classifier module 540, training … Related articles All 3 versions

Multidocument summary generation: Using informative and event words JJ Kuo, HH Chen – ACM Transactions on Asian Language Information …, 2008 – Page 1. 3 Multidocument Summary Generation: Using Informative and Event Words JUNE-JEI KUO and HSIN-HSI CHEN National Taiwan University Summary generation for multiple documents poses a number of issues including … Cited by 12 Related articles All 4 versions

Genetic algorithm based multi-document summarization D Liu, Y He, D Ji, H Yang – PRICAI 2006: Trends in Artificial Intelligence, 2006 – Springer … In sentence extraction module, the best summary is chosen from the summary population, which is generated by a genetic algorithm. Pre-processing Module contains four operators: i) Split documents into paragraphs and sentences. … 2.3 Sentence Extraction Module … Cited by 9 Related articles BL Direct All 4 versions

[PDF] from Using topic themes for multi-document summarization S Harabagiu, F Lacatusu – ACM Transactions on Information Systems ( …, 2010 – Page 1. 13 Using Topic Themes for Multi-Document Summarization SANDA HARABAGIU and FINLEY LACATUSU University of Texas at Dallas The problem of using topic representations for multidocument summarization (MDS) has received considerable attention recently. … Cited by 2 Related articles All 3 versions

[PDF] from IXIR: A statistical information distillation system M Levit, D Hakkani-Tür, G Tur, D Gillick – Computer Speech & Language, 2009 – Elsevier … Fig. 1. IXIR distillation system. View Within Article. In this paper, we focus on the sentence extraction module. To fully understand the extent of the problem, suppose that we need to find answers to a query about prosecution of … Cited by 3 Related articles All 18 versions

Where does text mining meet knowledge management? A case study E D’Avanzo, A Elia, T Kuflik, A Lieto… – Interdisciplinary Aspects of …, 2008 – Springer … LAKE has been extended for Multidocument summarization purposes. Again it has been exploited the KE ability of the system, adding, however, a sentence extraction module able to extract a tex- tual summary of pre-defined length from a cluster of documents. … Related articles All 4 versions

[PDF] from [CITATION] Exploiting information extraction annotations for document retrieval in distillation tasks D Hakkani-Tür, G Tur, M Levit – Eighth Annual Conference of the International Speech …, 2007 Cited by 5 Related articles All 14 versions

A Context based Word Indexing Model for Document Summarization P Goyal, L Behera, T McGinnity – 2012 – … gram). These bi-grams were supposed to be contextually informative. Firstly, the bi-grams are extracted using the sentence extraction module. Sentences are extracted from these bi-grams using another sentence extraction task. …

[PDF] from [CITATION] QASR: Question answering using semantic roles for speech interface S Stenchikova, D Hakkani-Tür, G Tur – Ninth International Conference on Spoken …, 2006 Cited by 12 Related articles All 18 versions

IN-TEXT EMBEDDED ADVERTISING T Mei, XS Hua, S Li, L Yang – US Patent App. 12/334,364, 2008 – Google Patents … 17, 2010 Sheet 3 of 7 US 2010/0153219 Al 400 Composition Server 112 Memory 406 Webpage Crawling Module 408 Webpage Segmentation Module 410 v * s Sentence Extraction Module 412 Advertisement-Keyword Matching Module 414 v * < ¦> Advertisement-Sentence … All 2 versions

Answer determination for natural language questionning S Stenchikova, DH Tur – EP Patent 1,793,318, 2007 – … question comprising: a phrase extraction module configured to generate a phrase from a natural language question; a search module configured to determine at least one candidate document based upon the extracted phrase; a sentence extraction module configured to extract … Related articles Cached

[PDF] from [PDF] Using information extraction to improve cross-lingual document retrieval D Hakkani-Tür, H Ji, R Grishman – MuLTI-SOuRcE, MuLTILINguAL …, 2007 – … works optimally for all queries using them. Therefore, we use an intermediate processing stage between the IR engine and the sentence extraction module, to filter out irrelevant documents. The basic idea is as follows: Since … Cited by 6 Related articles View as HTML All 9 versions

MSBGA: a multi-document summarization system based on genetic algorithm YX He, DX Liu, DH Ji, H Yang… – Machine Learning and …, 2006 – … 2. System design MSBGA includes three modules (see figure 1): pre-processing module, statistical modules and sentence extraction module. … In sentence extraction module, the best summary is chosen from the summary population, which is generated by a genetic algorithm. … Cited by 5 Related articles

A term weighting method based on lexical chain for automatic summarization YI Song, KS Han, HC Rim – Computational Linguistics and Intelligent Text …, 2004 – Springer … We implemented two systems to be compared with ours; the system using tf as a term weight and the system using tf.idf as a term weight. All systems in our experiments have a common sentence extraction module described in section 3.2. … Cited by 25 Related articles BL Direct All 7 versions

[PDF] from [PDF] Sentence Level Event Detection and Coreference Resolution M Naughton – 2009 – Page 1. Sentence Level Event Detection and Coreference Resolution by Martina Naughton B.Sc. A Thesis submitted to the National University of Ireland, Dublin for the degree of Doctor of Philosophy in the College of Engineering, Mathematical and Physical Sciences … Cited by 1 Related articles View as HTML

[PDF] from The query answering system Prodicos L Monceaux, C Jacquin, E Desmontils – Accessing Multilingual Information …, 2006 – Springer … question analysis module; – sentence extraction module (extracts sentences which might contain the an- swer); – answer extraction module (extracts the answer according to the results pro- vided by the previous module). … 4 Sentence Extraction Module … Cited by 3 Related articles BL Direct All 11 versions

[PDF] from [PDF] National University of Singapore at the TREC-13 question answering main task HCK Li, R Sun, TSCMY Kan – 2004 – … any of the manually constructed patterns. In this way, we boost the recall of definition sentences identified by the sentence extraction module. 4.4 Redundancy Removal and Answer String Extraction As the TREC QA guideline … Cited by 44 Related articles View as HTML All 35 versions

Information processing apparatus and method with speech synthesis function M Yamada, K Kawasaki, T Fukada… – EP Patent …, 2007 – … RAM 103. A sentence extraction module 207 extracts one sentence from text. A text … RAM 103. A one-sentence holding module 209 holds the sentence extracted by the sentence extraction module 207 in the RAM 103. A speech … Cited by 1 Related articles Cached

[PDF] from [PDF] Chinese QA and CLQA: NTCIR-5 QA experiments at UNT J Chen, R Li, P Yu, H Ge, P Chin, F Li… – Proceedings of NTCIR-5 …, 2005 – … The Sentence Extraction module identifies a certain number of non-duplicate sentences (500 sentences maximum for this year) from the annotated documents as sentence candidates which may contain an answer to each test question from the retrieved documents. … Cited by 5 Related articles View as HTML All 5 versions

[PDF] from [BOOK] Automatic summarization A Nenkova – 2011 – Page 1. Automatic Summarization Page 2. Page 3. Automatic Summarization Ani Nenkova University of Pennsylvania USA Kathleen McKeown Columbia University USA Boston – Delft Page 4. … Cited by 11 Related articles All 16 versions

[PDF] from [PDF] Sentence level information patterns for novelty detection X Li – 2006 – Page 1. Sentence Level Information Patterns for Novelty Detection A Dissertation Presented by XIAOYAN LI Submitted to the Graduate School of the University of Massachusetts at Amherst in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY … Cited by 7 Related articles View as HTML Library Search All 14 versions

[PDF] from [PDF] Semantics and Question Answering: an approach to “why” questions S Stenchikova – 2006 – Page 1. 1 Semantics and Question Answering: an approach to “why” questions Svetlana Stenchikova Semantics course Stony Brook University December 18, 2006 Page 2. 2 The internet brings to our fingertips unlimited information resources. We need tools to … Related articles View as HTML All 7 versions

Answer determination for natural language questioning S Stenchikova, G Tur, DH Tur – US Patent App. 11/319,188, 2005 – Google Patents … Question I Phrase Extraction Module 101 phrase 1 Search Module 103 Candidate Documents I I Sentence Extraction Module 105 Candidate Sentences i Answer Extraction Module 107 Answer Candidates Answer Ranking Module 109 I Sorted by Confidence List of Answers … All 2 versions

[PDF] from [PDF] Multilingual summarization by integrating linguistic resources in the MLIS-MUSI project A Lenci, R Bartolini, N Calzolari, A Agua… – … and Evaluation (LREC …, 2002 – … For instance, sentences in introduction and conclusion sections are assigned a higher weight, as well as the first sentence of each section. The sentence extractor module assigns to each sentence a global weight, computed on the basis of the above parameters. … Cited by 18 Related articles View as HTML All 18 versions

Generic and query-based text summarization using lexical cohesion Y Chali – Advances in Artificial Intelligence, 2002 – Springer … Hence, the sentence extractor module proceeds next. … The sentence extractor module has two strategies: generic extraction of sum- maries, and user-focused extraction of summaries in which we assume that the user supplies a list of terms as expressions of the user’s interests. … Cited by 7 Related articles BL Direct All 8 versions

Information processing apparatus and method with speech synthesis function M Yamada, K Kawasaki, T Fukada… – US Patent App. 10/ …, 2003 – Google Patents Page 1. US 20030158735A1 (19) United States (12) Patent Application Publication (io) Pub. No.: US 2003/0158735 Al Yamada et al. (43) Pub. Date: Aug. 21,2003 (54) INFORMATION PROCESSING APPARATUS AND METHOD … All 2 versions

[PDF] from [PDF] Unsupervised Learning for Information Distillation K Kamangar – 2007 – Page 1. E S E A R C H R E P R O R T I D I A P Av. des Prés-Beudin 20 IDIAP Research Institute 1920 Martigny – Switzerland Tel: +41 27 721 77 11 Email: PO Box 592 Fax: +41 27 721 77 12 Unsupervised Learning for Information Distillation … Related articles View as HTML All 8 versions