{ "paper_id": "W07-0200", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T04:41:43.434466Z" }, "title": "INVITED SPEAKER", "authors": [ { "first": "Chris", "middle": [], "last": "Biemann", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Eneko", "middle": [], "last": "Agirre", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Michael", "middle": [], "last": "Gamon", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Rosie", "middle": [], "last": "Jones", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Yahoo", "middle": [], "last": "Research", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Fabio", "middle": [ "Massimo" ], "last": "Zanzotto", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Andrew", "middle": [], "last": "Mccallum", "suffix": "", "affiliation": {}, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "", "pdf_parse": { "paper_id": "W07-0200", "_pdf_hash": "", "abstract": [], "body_text": [ { "text": "Recent years have shown an increased interest in bringing the field of graph theory into Natural Language Processing. In many NLP applications entities can be naturally represented as nodes in a graph and relations between them can be represented as edges. Recent research has shown that graphbased representations of linguistic units as diverse as words, sentences and documents give rise to novel and efficient solutions in a variety of NLP tasks, ranging from part of speech tagging, word sense disambiguation and parsing to information extraction, semantic role assignment, summarization and sentiment analysis.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "PREFACE", "sec_num": null }, { "text": "This volume contains papers accepted for presentation at the TextGraphs-2 2007 Workshop on Graph-Based Algorithms for Natural Language Processing. This event took place on April 26, 2007, in Rochester, NY, USA, immediately following the HLT-NAACL Human Language Technologies Conference. It was the second workshop on this topic, building on the success of the first TextGraphs workshop at HLT-NAACL 2006. The workshop aimed at bringing together researchers working on problems related to the use of graph-based algorithms for Natural Language Processing and on the theory of graph-based methods. It addressed a broad spectrum of research areas to foster exchange of ideas and help to identify principles of using the graph notions that go beyond an ad-hoc usage. Unveiling these principles will give rise to applying generic graph methods to many new problems that can be encoded in this framework.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "PREFACE", "sec_num": null }, { "text": "We issued calls for both regular and short, late-breaking papers. In total, ten regular and three short papers were accepted for presentation, considering the careful reviews of our program committee. We are indebted to all program committee members for their thoughtful, high quality and elaborate reviews, especially considering our extremely tight time frame for reviewing. The papers appearing in this volume have surely benefited from their expert feedback. This year's workshop attracted papers employing graphs in a wide range of settings. While some contributions focus on analyzing the structure of graphs induced by language data or the interaction of processes on various levels, others use graphs as a means for data representation to solve NLP tasks, sometimes involving transformations on the graph structure. i Cancho et al. find correlations in the organization of syntactic dependency networks for a wide range of languages. Co-occurrence degree distributions are examined in a comparative study of Russian and English by V. Kapustin and A. Jamsen. In the setting of spell checking, M. Choudhury et al. find that spelling error probabilities for different languages are proportional to the average weighted degree of the corresponding SpellNet. A transductive classification algorithm based on graph clustering is described by K. Ganchev and F. Pereira, and tested on various NLP tasks.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "PREFACE", "sec_num": null }, { "text": "Finally, having a prominent researcher as an invited speaker greatly contributes to the quality of the workshop. We thank Andrew McCallum for his talk and for the support that his prompt acceptance provided to the workshop.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "H. F. Witschel introduces a new graph based meta model for Information", "sec_num": null }, { "text": "Chris Biemann, Irina Matveeva, Rada Mihalcea and Dragomir Radev April 2007", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "H. F. Witschel introduces a new graph based meta model for Information", "sec_num": null } ], "back_matter": [], "bib_entries": { "BIBREF1": { "ref_id": "b1", "title": "Multi-level Association Graphs -A New Graph-Based Model for Information Retrieval Hans Friedrich Witschel", "authors": [], "year": null, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Multi-level Association Graphs -A New Graph-Based Model for Information Retrieval Hans Friedrich Witschel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9", "links": null }, "BIBREF2": { "ref_id": "b2", "title": "Extractive Automatic Summarization: Does more Linguistic Knowledge Make a Difference?", "authors": [ { "first": "Daniel", "middle": [ "S" ], "last": "Leite", "suffix": "" }, { "first": "H", "middle": [ "M" ], "last": "Lucia", "suffix": "" }, { "first": "", "middle": [], "last": "Rino", "suffix": "" }, { "first": "A", "middle": [ "S" ], "last": "Thiago", "suffix": "" }, { "first": "Maria", "middle": [], "last": "Pardo", "suffix": "" }, { "first": "V", "middle": [], "last": "Das Gra\u00e7as", "suffix": "" }, { "first": "", "middle": [ ". ." ], "last": "Nunes", "suffix": "" } ], "year": null, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Extractive Automatic Summarization: Does more Linguistic Knowledge Make a Difference? Daniel S. Leite, Lucia H. M. Rino, Thiago A. S. Pardo and Maria das Gra\u00e7as V. 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Montero and Kenji Araki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61", "links": null }, "BIBREF9": { "ref_id": "b9", "title": "Correlations in the Organization of Large-Scale Syntactic Dependency Networks Ramon Ferrer i Cancho", "authors": [ { "first": "Alexander", "middle": [], "last": "Mehler", "suffix": "" }, { "first": "Olga", "middle": [], "last": "Pustylnikov", "suffix": "" }, { "first": "Albert", "middle": [ ". . ." ], "last": "Diaz-Guilera", "suffix": "" } ], "year": null, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Correlations in the Organization of Large-Scale Syntactic Dependency Networks Ramon Ferrer i Cancho, Alexander Mehler, Olga Pustylnikov and Albert Diaz-Guilera . . . . . . . 65", "links": null }, "BIBREF10": { "ref_id": "b10", "title": "How Difficult is it to Develop a Perfect Spell-checker? A Cross-Linguistic Analysis through Complex Network Approach Monojit Choudhury", "authors": [ { "first": "Markose", "middle": [], "last": "Thomas", "suffix": "" }, { "first": "Animesh", "middle": [], "last": "Mukherjee", "suffix": "" }, { "first": "Anupam", "middle": [], "last": "Basu", "suffix": "" }, { "first": "Niloy", "middle": [], "last": "Ganguly", "suffix": "" }, { "first": ".", "middle": [ "." ], "last": "", "suffix": "" } ], "year": null, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "How Difficult is it to Develop a Perfect Spell-checker? A Cross-Linguistic Analysis through Complex Network Approach Monojit Choudhury, Markose Thomas, Animesh Mukherjee, Anupam Basu and Niloy Ganguly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81", "links": null }, "BIBREF11": { "ref_id": "b11", "title": "Vertex Degree Distribution for the Graph of Word Co-Occurrences in Russian Victor Kapustin and Anna Jamsen", "authors": [], "year": null, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Vertex Degree Distribution for the Graph of Word Co-Occurrences in Russian Victor Kapustin and Anna Jamsen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89", "links": null }, "BIBREF12": { "ref_id": "b12", "title": ":15 Learning to Transform Linguistic Graphs Valentin Jijkoun and Maarten de Rijke 15:15-15:30 Semi-supervised Algorithm for Human-Computer Dialogue Mining Calkin S. Montero and Kenji Araki 15:30-16:00 Coffee Break Session 4: Session Four 16:00-16:25 Correlations in the Organization of Large-Scale Syntactic Dependency Networks Ramon Ferrer i Cancho", "authors": [ { "first": "Daniel", "middle": [ "S" ], "last": "Leite", "suffix": "" }, { "first": "H", "middle": [ "M" ], "last": "Lucia", "suffix": "" }, { "first": "", "middle": [], "last": "Rino", "suffix": "" }, { "first": "A", "middle": [ "S" ], "last": "Thiago", "suffix": "" }, { "first": "Maria", "middle": [], "last": "Pardo", "suffix": "" }, { "first": "V", "middle": [], "last": "Das Gra\u00e7as", "suffix": "" } ], "year": 2007, "venue": "Semantic Similarity Based on Syntactic Dependency Trees Applied to Textual Entailment Daniel Micol,\u00d3scar Ferr\u00e1ndez", "volume": "8", "issue": "", "pages": "15--17", "other_ids": {}, "num": null, "urls": [], "raw_text": "Conference Program Thursday, April 26, 2007 8:45-9:00 Opening Remarks Session 1: Session One 09:00-10:00 Invited Talk by Andrew McCallum 10:00-10:25 Analysis of the Wikipedia Category Graph for NLP Applications Torsten Zesch and Iryna Gurevych 10:30-11:00 Coffee Break Session 2: Session Two 11:00-11:25 Multi-level Association Graphs -A New Graph-Based Model for Information Re- trieval Hans Friedrich Witschel 11:25-11:50 Extractive Automatic Summarization: Does more Linguistic Knowledge Make a Difference? Daniel S. Leite, Lucia H. M. Rino, Thiago A. S. Pardo and Maria das Gra\u00e7as V. Nunes 11:50-12:15 Timestamped Graphs: Evolutionary Models of Text for Multi-Document Summa- rization Ziheng Lin and Min-Yen Kan 12:15-12:30 Unigram Language Models using Diffusion Smoothing over Graphs Bruno Jedynak and Damianos Karakos 12:30-14:00 Lunch Break Thursday, April 26, 2007 (continued) Session 3: Session Three 14:00-14:25 Transductive Structured Classification through Constrained Min-Cuts Kuzman Ganchev and Fernando Pereira 14:25-14:50 Latent Semantic Grammar Induction: Context, Projectivity, and Prior Distributions Andrew M Olney 14:50-15:15 Learning to Transform Linguistic Graphs Valentin Jijkoun and Maarten de Rijke 15:15-15:30 Semi-supervised Algorithm for Human-Computer Dialogue Mining Calkin S. Montero and Kenji Araki 15:30-16:00 Coffee Break Session 4: Session Four 16:00-16:25 Correlations in the Organization of Large-Scale Syntactic Dependency Networks Ramon Ferrer i Cancho, Alexander Mehler, Olga Pustylnikov and Albert Diaz-Guilera 16:25-16:50 DLSITE-2: Semantic Similarity Based on Syntactic Dependency Trees Applied to Textual Entailment Daniel Micol,\u00d3scar Ferr\u00e1ndez, Rafael Mu\u00f1oz and Manuel Palomar 16:50-17:15 How Difficult is it to Develop a Perfect Spell-checker? A Cross-Linguistic Analysis through Complex Network Approach Monojit Choudhury, Markose Thomas, Animesh Mukherjee, Anupam Basu and Niloy Ganguly 17:15-17:30 Vertex Degree Distribution for the Graph of Word Co-Occurrences in Russian Victor Kapustin and Anna Jamsen x", "links": null } }, "ref_entries": { "FIGREF0": { "num": null, "type_str": "figure", "text": "Retrieval that subsumes many previous retrieval models and supports different forms of search. Improved unigram language models by a smoothing technique that accounts for word similarities are constructed by B. Jedynak and D. Karakos.Unsupervised grammar induction using latent semantics is the topic of A. M. Olney's research. V. Jijkoun and M. de Rijke view NLP tasks as graph transformations of labelled, directed graphs and experiment with tasks involving syntax and semantics. Syntactic dependency trees as a basis for semantic similarity are applied to textual entailment by D. Micol et al. D. Leite et al. find in their graph-based automatic summarization experiments that linguistic knowledge is necessary to improve automatic extracts. For multi-document summarization, evolving timestamped graphs are employed in the contribution of Z. Lin and M.-Y. Kan. A graph for the extraction of patterns combined with an extension of chance discovery is applied by C. S. Montero and K. Araki to human-computer dialogue mining. The small-world and scale-free property of linguistic graphs that go in hand with power-law distributions on entity and entity pair frequencies are examined in four papers: T. Zesch and I. Gurevych analyze the article and category graph of Wikipedia and measure correlation with WordNet. R. Ferrer", "uris": null } } } }