RTE Knowledge Resources
Knowledge resources have shown their relevance for applied semantic inference, and are extensively used by applied inference systems, such as those developed within the Textual Entailment framework.
This page presents a list of the knowledge resources used by systems that have participated in the last RTE challenges. The first table lists the publicly available resources, the second one lists unpublished resources. Both tables are sortable by Resource name, type, author and number of users.
RTE Participants are encouraged to add information about all kind of knowledge resources used, from standard existing resources (e.g. WordNet) to knowledge collections created for specific purposes, which can be made available to the community.
Publicly available Resources
| Resource | Type | Authors | Brief description | RTE Users* | Usage info |
|---|---|---|---|---|---|
| WordNet | Lexical DB | George A. Miller (project director) - Princeton University |
Lexical database of English nouns, verbs, adjectives and adverbs | 23 | Users |
| Verbnet | Lexical DB | Martha Palmer, Karin Kipper - University of Colorado Boulder |
Lexicon for English verbs organized into classes extending Levin (1993) classes through refinement and addition of subclasses to achieve syntactic and semantic coherence among members of a class | 3 | Users |
| VerbOcean | Lexical DB | Timothy Chklovski and Patrick Pantel - Information Sciences Institute, University of Southern California |
Broad-coverage semantic network of verbs | 5 | Users |
| FrameNet | Lexical DB | Charles J. Fillmore (project director) - ICSI (International Computer Science Institute) - Berkley University |
Lexical resource for English words, based on frame semantics (valences) and supported by corpus evidence | 2 | Users |
| NomBank | Lexical DB | Adam Meyers, Ruth Reeves, Catherine Macleod, Rachel Szekely, Veronika Zielinska, Brian Young - New York University |
Lexical resource containing syntactic frames for nouns, extracted from annotated corpora | 2 | Users |
| PropBank | Lexical DB | Martha Palmer, Mitch Marcus - University of Colorado Boulder |
Lexical resource containing syntactic frames for verbs, extracted from annotated corpora | 2 | Users |
| Nomlex Plus | Lexical DB | Catherine Macleod, Ralph Grishman, Adam Meyers, Leslie Barrett and Ruth Reeves - New York University |
Dictionary of English nominalizations: it describes the allowed complements for a nominalization and relates the nominal complements to the arguments of the corresponding verb | 1 | Users |
| Wikipedia | Encyclopedia | Free encyclopedia. Used for extraction of lexical-semantic rules (from its more structured parts), named entity recognition, geographical information etc. | 3 | Users | |
| TEASE Collection | Collection of Entailment Rules | Idan Szpektor - Bar Ilan University |
Output of the TEASE algorithm | 0 | Users |
| BADC Acronym and Abbreviation List | Word List | BADC (British Atmospheric Data Centre) | Acronym and Abbreviation List | 1 | Users |
| Acronym Guide | Word List | Acronym-Guide.com | Acronym and Abbreviation Lists for English, branched in thematic directories | 1 | Users |
| Dekang Lin’s Thesaurus | Thesaurus | Dekang Lin - University of Alberta |
Thesaurus automatically constructed using a parsed corpus, based on distributional similarity scores | 1 | Users |
| Roget's Thesaurus | Thesaurus | Peter Mark Roget (Electronic version distributed by University of Chicago) | Roget's Thesaurus is a widely-used English thesaurus, created by Dr. Peter Mark Roget in 1805. The original edition had 15,000 words, and each new edition has been larger. The electronic edition (version 1.02) is made available by University of Chicago. | 1 | Users |
| Web1T 5-grams | Word list | Thorsten Brants, Alex Franz - Linguistic Data Consortium, University of Pennsylvania; Google Inc. |
Data set containing English word n-grams and their observed frequency counts. The n-gram counts were generated from approximately 1 trillion word tokens of text from publicly accessible Web pages | 1 | Users |
| GNIS - Geographic Names Information System | Gazetteer | USGS (United States Geological Survey) | Database containing the Federal and national standard toponyms for USA, associated areas and Antarctica | 1 | Users |
| Geonames | Gazetteer | Database containing eight million geographical names. It is integrating geographical data such as names of places in various languages, elevation, population and others from various sources. | 1 | Users | |
| Sekine's Paraphrase Database | Collection of paraphrases | Satoshi Sekine - Department of Computer Science, New York University |
Data-base created using Sekine's method, NOT cleaned up by human. It includes 19,975 sets of paraphrases with 191,572 phrases. | 0 | Users |
| Microsoft Research Paraphrase Corpus | Collection of paraphrases | Microsoft Research | Text file containing 5800 pairs of sentences which have been extracted from news sources on the web, along with human annotations indicating whether each pair captures a paraphrase/semantic equivalence relationship. | 0 | Users |
| Downward entailing operators | Collection of entailing operators | Cristian Danescu-Niculescu-Mizil - Department of Computer Science, Cornell University, Ithaca NY |
System output of an unsupervised algorithm recovering many Downward Entailing operators, like 'doubt'. | 0 | Users |
| New resource | Participants are encouraged to contribute | Users | |||
| New resource | Participants are encouraged to contribute | Users |
Not available Resources
The following table lists the unpublished resources used by RTE participants. Some of them have been developed by Users themselves specifically for RTE. Interested people may turn to authors to obtain further information.
| Resource | Type | Authors | Brief description | RTE Users* | Usage info |
|---|---|---|---|---|---|
| PARC Polarity Lexicon | Lexical DB | PARC - Palo Alto Research Center | Verbs classification with respect to semantic polarity | 1 | Users |
| DIRT Paraphrase Collection | Collection of paraphrases | Dekang Lin and Patrick Pantel - University of Alberta |
Output of the DIRT algorithm | 4 | Users |
| Gazetteer from TREC | Gazetteer | NIST - National Institute of Standards and Technology | Cities and other geographical names | 1 | Users |
| DFKI Geographic Ontology (to be released) |
Ontology | DFKI - German Research Center for Artificial Intelligence | Ontology containing geographic terms and two kinds of relations: the directional part-of relation, and the equal relation for synonyms and abbreviations of the same geographic area (e.g the United Kingdom, the UK, Great Britain, etc.) | 1 | Users |
| Syntactic rule base (to be released) |
Collection of Entailment Rules | Bar-Ilan University; Tel-Aviv University | A manually-composed collection of entailment rules which define parse tree transformations. The rules cover generic syntactic phenomena such as appositions, conjunctions, passive, relative clause, etc. (Bar-Haim et al., AAAI-07) | 1 | Users |
| Polarity rule base (to be released) |
Collection of Entailment Rules | Bar-Ilan University; Tel-Aviv University | A manually-composed collection of entailment rules which detect predicates whose polarity is negative (e.g. didn't dance) or unknown (e.g. plans to dance). The rules capture diverse phenomena that affect polarity, e.g. verbal negation, modal verbs, conditionals, and certain verbs that induce negative or "unknown" polarity context. The latter were taken mainly from VerbNet. Extends a resource described in (Bar-Haim et al., AAAI-07) | 1 | Users |
| Lexical-Syntactic rule base combining WordNet, NomLex-plus and Unary DIRT | Collection of Entailment Rules | Bar-Ilan University; Tel-Aviv University | Extract lexical-syntactic entailment rules for predicates (verbal and nominal), including argument mapping. The resource is based on WordNet, Nomlex-Plus and Unary DIRT (Szpektor and Dagan, Coling 08) | 1 | Users |
| Lexical reference rules extracted from Wikipedia (to be released) |
Collection of Entailment Rules | Bar-Ilan University; Tel-Aviv University | Extraction of lexical entailment rules from the text body (first sentence), and from metadata (links, redirects, parentheses) | 1 | Users |
| OPENU Collection | Collection of Entailment Rules and Patterns | Open University | Collections of rules, patterns etc. for RTE purpose, extracted from Reuter corpus parsed using Minipar. | 1 | Users |
| New resource | Participants are encouraged to contribute | Users | |||
| New resource | Participants are encouraged to contribute | Users |
[*] The number of Users (see "Usage Info" links for details) refers to participants in the last two RTE challenges.
RTE-3 data have been provided only by participants, whereas RTE-4 data have been integrated with information extracted from the related proceedings.