DOCSLIB.ORG

The Berkeley Framenet Project

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Berkeley Framenet Project The Berkeley FrameNet Project Collin F. Baker and Charles J. Fillmore and John B. Lowe {collinb, fillmore, jblowe}@icsi.berkeley.edu International Computer Science Institute 1947 Center St. Suite 600 Berkeley, Calif., 94704 Abstract These descriptions are based on hand-tagged FrameNet is a three-year NSF-supported semantic annotations of example sentences ex- project in corpus-based computational lexicog- tracted from large text corpora and systematic raphy, now in its second year (NSF IRI-9618838, analysis of the semantic patterns they exem- "Tools for Lexicon Building"). The project's plify by lexicographers and linguists. The pri- key features are (a) a commitment to corpus mary emphasis of the project therefore is the evidence for semantic and syntactic generaliza- encoding, by humans, of semantic knowledge tions, and (b) the representation of the valences in machine-readable form. The intuition of the of its target words (mostly nouns, adjectives, lexicographers is guided by and constrained by and verbs) in which the semantic portion makes the results of corpus-based research using high- use of frame semantics. The resulting database performance software tools. will contain (a) descriptions of the semantic The semantic domains to be covered are" frames underlying the meanings of the words de- HEALTH CARE, CHANCE, PERCEPTION, COMMU- scribed, and (b) the valence representation (se- NICATION, TRANSACTION, TIME, SPACE, BODY mantic and syntactic) of several thousand words (parts and functions of the body), MOTION, LIFE and phrases, each accompanied by (c) a repre- STAGES, SOCIAL CONTEXT, EMOTION and COG- sentative collection of annotated corpus attes- NITION. tations, which jointly exemplify the observed 1.1 Scope of the Project linkings between "frame elements" and their syntactic realizations (e.g. grammatical func- The results of the project are (a) a lexical re- source, called the FrameNet database 3, and (b) tion, phrase type, and other syntactic traits). This report will present the project's goals and associated software tools. The database has workflow, and information about the computa- three major components (described in more de- tional tools that have been adapted or created tail below: • Lexicon containing entries which are com- in-house for this work. posed of: (a) some conventional dictionary-type data, mainly for the sake of human readers; (b) FOR- 1 Introduction MULAS which capture the morphosyntactic ways in The Berkeley FrameNet project 1 is producing which elements of the semantic frame can be realized frame-semantic descriptions of several thousand within the phrases or sentences built up around the word; (c) links to semantically ANNOTATED EXAM- English lexical items and backing up these de- scriptions with semantically annotated attesta- European collaborators whose participation has made tions from contemporary English corpora2. this possible are Sue Atkins, Oxford University Press, and Ulrich Held, IMS-Stuttgart. 1The project is based at the International Computer SThe database will ultimately contain at least 5,000 Science Institute (1947 Center Street, Berkeley, CA). A lexical entries together with a parallel annotated cor- fuller bibliography may be found in (Lowe et ai., 1997) pus, these in formats suitable for integration into appli- 2Our main corpus is the British National Corpus. cations which use other lexical resources such as Word- We have access to it through the courtesy of Oxford Net and COMLEX. The final design of the database will University Press; the POS-tagged and lemmatized ver- be selected in consultation with colleagues at Princeton sion we use was prepared by the Institut flit Maschinelle (WordNet), ICSI, and IMS, and with other members of Sprachverarbeitung of the University of Stuttgart). The the NLP community. 86 PLE SENTENCES which illustrate each of the poten- subframes associated with individual words in- tial realization patterns identified in the formula; 4 herit all of these while possibly adding some of and (d) links to the FRAME DATABASE and to other their own. Fig. 1 shows some of the subframes, machine-readable resources such as WordNet and as discussed below. COMLEX. • Frame Database containing descriptions of fra~ne(TRANSPORTATION) each frame's basic conceptual structure and giving frame.elements(MOVER(S), MEANS, PATH) names and descriptions for the elements which par- scene(MOVER(S) move along PATH by MEANS) ticipate in such structures. Several related entries in frame(DRiVING) this database are schematized in Fig. 1. inherit(TRANSPORTATION) • Annotated Example Sentences which are frarne.elements(DRIVER (:MOVER), VEHICLE marked up to exemplify the semantic and morpho- (:MEANS), RIDER(S) (:MOVER(S)), CARGO syntactic properties of the lexical items. (Several (=MOVER(S))) of these are schematized in Fig. 2). These sentences scenes(DRIVER starts VEHICLE, DRIVER con- provide empirical support for the lexicographic anal- trois VEHICLE, DRIVER stops VEHICLE) ysis provided in the frame database and lexicon en- frame(RIDING-i) tries. inherit(TRANSP O RTATION) These three components form a highly rela- frame.elements(RIDER(S) (=MOVER(S)), VE- tional and tightly integrated whole: elements HICLE (:MEANS)) in each may point to elements in the other scenes(RIDER enters VEHICLE, two. The database will also contain estimates VEHICLE carries RIDER along PATH, of the relative frequency of senses and comple- RIDER leaves VEHICLE ) mentation patterns calculated by matching the Figure 1: A subframe can inherit elements and senses and patterns in the hand-tagged exam- semantics from its parent ples against the entire BNC corpus. 1.2 Conceptual Model The DRIVING frame, for example, specifies a DRIVER (a principal MOVER), a VEHICLE (a par- The FrameNet work is in some ways similar ticularization of the MEANS element), and po- to efforts to describe the argument structures tentially CARGO or RIDER as secondary movers. of lexical items in terms of case-roles or theta- In this frame, the DRIVER initiates and controls roles, 5 but in FrameNet, the role names (called the movement of the VEHICLE. For most verbs frame elements or FEs) are local to particular in this frame, DRIVER or VEHICLE can be real- conceptual structures (frames); some of these ized as subjects; VEHICLE, RIDER, or CARGO can are quite general, while others are specific to a appear as direct objects; and PATH and VEHICLE small family of lexical items. can appear as oblique complements. For example, the TRANSPORTATION frame, Some combinations of frame elements, or within the domain of MOTION, provides MOVERS, MEANS of transportation, and PATHS; 6 Frame Element Groups (FEGs), for some real corpus sentences in the DRIVING frame are 4In cases of accidental gaps, clearly marked invented shown in Fig. 2. examples may be added. A RIDING_I frame has the primary mover role 5The semantic frames for individual lexical units are as RIDER, and allows as VEHICLE those driven typically "blends" of more than one basic frame; from our point of view, the so-called "linking" patterns pro- by others/ In grammatical realizations of this posed in LFG, HPSG, and Construction Grammar, op- frame, the RIDER can be the subject; the VEHI- erate on higher-level frames of action (giving agent, pa- CLE can appear as a direct object or an oblique tient, instrument), motion and location (giving theme, complement; and the PATH is generally realized location, source, goal, path), and experience (giving ex- periencer, stimulus, content), etc. In some but not all as an oblique. cases, the assignment of syntactic correlates to frame el- The FrameNet entry for each of these verbs ements could be mediated by mapping them to the roles will include a concise formula for all seman- of one of the more abstract frames. 8A detailed study of motion predicates would require work includes the separate analysis of the flame seman- a finer-grained analysis of the Path element, separating tics of directional and locational expressions. out Source and Goal, and perhaps Direction and Area, 7A separate frame RIDING_2 that applies to the En- but for a basic study of the transportation predicates glish verb r/de selects means of transportation that can such refined analysis is not necessary. In any case, our be straddled, such as bicycles, motorcycles, and horses. 87 FEG Annotated Example from BNC of the full range of use possibilities for individ- D [D Kate] drove [v home] in a ual words, documented with corpus data, the stupor. model examples for each use, and the statistical V, D A pregnant woman lost her baby af- information on relative frequency. ter she fainted as she waited for a bus and fell into the path of [v a 2 Organization and Workflow lorry] driven [~ by her uncle]. D, P And that was why [D I] drove 2.1 Overview [p eastwards along Lake Geneva]. The computational side of the FrameNet project D, R, P Now [D Van Cheele] was driving is directed at efficiently capturing human in- [R his guest] Iv back to the station]. sights into semantic structure. The majority D, V, P [D Cumming] had a fascination with most forms of transport, driving of the work involved is marking text with se- [y his Rolls] at high speed [p around mantic tags, specifying (again by hand) the the streets of London]. structure of the frames to be treated, and writ- D+R, P [D We] drive [p home along miles ing dictionary-style entries based the results of of empty freeway]. annotation and a priori descriptions. With V, P Over the next 4 days, Iv the Rolls the exception of the example sentence extrac- Royces] will drive [p down to Ply- tion component, all the software modules are mouth], following the route of the highly interactive and have substantial user in- railway. terface requirements. Most of this functionality Figure 2: Examples of Frame Element Groups is provided by WWW-based programs written and Annotated Sentences in PERL.
Load more

Recommended publications
  • Enriching an Explanatory Dictionary with Framenet and Propbank Corpus Examples
    Proceedings of eLex 2019 Enriching an Explanatory Dictionary with FrameNet and PropBank Corpus Examples Pēteris Paikens 1, Normunds Grūzītis 2, Laura Rituma 2, Gunta Nešpore 2, Viktors Lipskis 2, Lauma Pretkalniņa2, Andrejs Spektors 2 1 Latvian Information Agency LETA, Marijas street 2, Riga, Latvia 2 Institute of Mathematics and Computer Science, University of Latvia, Raina blvd. 29, Riga, Latvia E-mail: [email protected], [email protected] Abstract This paper describes ongoing work to extend an online dictionary of Latvian – Tezaurs.lv – with representative semantically annotated corpus examples according to the FrameNet and PropBank methodologies and word sense inventories. Tezaurs.lv is one of the largest open lexical resources for Latvian, combining information from more than 300 legacy dictionaries and other sources. The corpus examples are extracted from Latvian FrameNet and PropBank corpora, which are manually annotated parallel subsets of a balanced text corpus of contemporary Latvian. The proposed approach augments traditional lexicographic information with modern cross-lingually interpretable information and enables analysis of word senses from the perspective of frame semantics, which is substantially different from (complementary to) the traditional approach applied in Latvian lexicography. In cases where FrameNet and PropBank corpus evidence aligns well with the word sense split in legacy dictionaries, the frame-semantically annotated corpus examples supplement the word sense information with clarifying usage examples and commonly used semantic valence patterns. However, the annotated corpus examples often provide evidence of a different sense split, which is often more coarse-grained and, thus, may help dictionary users to cluster and comprehend a fine-grained sense split suggested by the legacy sources.
    [Show full text]
  • Developing a Large Scale Framenet for Italian: the Iframenet Experience
    Developing a large scale FrameNet for Italian: the IFrameNet experience Roberto Basili° Silvia Brambilla§ Danilo Croce° Fabio Tamburini§ ° § Dept. of Enterprise Engineering Dept. of Classic Philology and Italian Studies University of Rome Tor Vergata University of Bologna {basili,croce}@info.uniroma2.it [email protected], [email protected] ian Portuguese, German, Spanish, Japanese, Swe- Abstract dish and Korean. All these projects are based on the idea that English. This paper presents work in pro- most of the Frames are the same among languages gress for the development of IFrameNet, a and that, thanks to this, it is possible to adopt large-scale, computationally oriented, lexi- Berkeley’s Frames and FEs and their relations, cal resource based on Fillmore’s frame se- with few changes, once all the language-specific mantics for Italian. For the development of information has been cut away (Tonelli et al. 2009, IFrameNet linguistic analysis, corpus- Tonelli 2010). processing and machine learning techniques With regard to Italian, over the past ten years are combined in order to support the semi- several research projects have been carried out at automatic development and annotation of different universities and Research Centres. In par- the resource. ticular, the ILC-CNR in Pisa (e.g. Lenci et al. 2008; Johnson and Lenci 2011), FBK in Trento (e.g. Italiano. Questo articolo presenta un work Tonelli et al. 2009, Tonelli 2010) and the Universi- in progress per lo sviluppo di IFrameNet, ty of Rome, Tor Vergata (e.g. Pennacchiotti et al. una risorsa lessicale ad ampia copertura, 2008, Basili et al.
    [Show full text]
  • Integrating Wordnet and Framenet Using a Knowledge-Based Word Sense Disambiguation Algorithm
    Integrating WordNet and FrameNet using a knowledge-based Word Sense Disambiguation algorithm Egoitz Laparra and German Rigau IXA group. University of the Basque Country, Donostia {egoitz.laparra,german.rigau}@ehu.com Abstract coherent groupings of words belonging to the same frame. This paper presents a novel automatic approach to In that way we expect to extend the coverage of FrameNet partially integrate FrameNet and WordNet. In that (by including from WordNet closely related concepts), to way we expect to extend FrameNet coverage, to en- enrich WordNet with frame semantic information (by port- rich WordNet with frame semantic information and ing frame information to WordNet) and possibly to extend possibly to extend FrameNet to languages other than FrameNet to languages other than English (by exploiting English. The method uses a knowledge-based Word local wordnets aligned to the English WordNet). Sense Disambiguation algorithm for linking FrameNet WordNet1 [12] (hereinafter WN) is by far the most lexical units to WordNet synsets. Specifically, we ex- ploit a graph-based Word Sense Disambiguation algo- widely-used knowledge base. In fact, WN is being rithm that uses a large-scale knowledge-base derived used world-wide for anchoring different types of seman- from WordNet. We have developed and tested four tic knowledge including wordnets for languages other than additional versions of this algorithm showing a sub- English [4], domain knowledge [17] or ontologies like stantial improvement over previous results. SUMO [22] or the EuroWordNet Top Concept Ontology [3]. It contains manually coded information about En- glish nouns, verbs, adjectives and adverbs and is organized around the notion of a synset.
    [Show full text]
  • The Hebrew Framenet Project
    The Hebrew FrameNet Project Avi Hayoun, Michael Elhadad Dept. of Computer Science Ben-Gurion University Beer Sheva, Israel {hayounav,elhadad}@cs.bgu.ac.il Abstract We present the Hebrew FrameNet project, describe the development and annotation processes and enumerate the challenges we faced along the way. We have developed semi-automatic tools to help speed the annotation and data collection process. The resource currently covers 167 frames, 3,000 lexical units and about 500 fully annotated sentences. We have started training and testing automatic SRL tools on the seed data. Keywords: FrameNet, Hebrew, frame semantics, semantic resources 1. Introduction frames and their structures in natural language. Recent years have seen growing interest in the task 1.2. FrameNet in Other Languages of Semantic Role Labeling (SRL) of natural language The original FrameNet project has been adapted and text (sometimes called “shallow semantic parsing”). ported to multiple languages. The most active interna- The task is usually described as the act of identify- tional FrameNet teams include the Swedish FrameNet ing the semantic roles, which are the set of semantic (SweFN) covering close to 1,200 frames with 34K LUs properties and relationships defined over constituents (Ahlberg et al., 2014); the Japanese FrameNet (JFN) of a sentence, given a semantic context. with 565 frames, 8,500 LUs and 60K annotated ex- The creation of resources that document the realiza- ample sentences (Ohara, 2013); and FrameNet Brazil tion of semantic roles in natural language texts, such (FN-Br) covering 179 frames, 196 LUs and 12K anno- as FrameNet (Fillmore and Baker, 2010; Ruppenhofer tated sentences (Torrent and Ellsworth, 2013).
    [Show full text]
  • INF5830 Introduction to Semantic Role Labeling
    INF5830 Introduction to Semantic Role Labeling Andrey Kutuzov University of Oslo Language Technology Group With thanks to Lilja Øvrelid, Martha Palmer and Dan Jurafsky INF5830 Introduction to Semantic Role Labeling 1(36) Semantic Role Labeling INF5830 Introduction to Semantic Role Labeling 2(36) Introduction Contents Introduction Semantic roles in general PropBank: Proto-roles FrameNet: Frame Semantics Summary INF5830 Introduction to Semantic Role Labeling 2(36) Introduction Semantics I Study of meaning, expressed in language; I Morphemes, words, phrases, sentences; I Lexical semantics; I Sentence semantics; I (Pragmatics: how the context affects meaning). INF5830 Introduction to Semantic Role Labeling 3(36) Introduction Semantics I Linguistic knowledge: meaning I Meaningful or not: I Word { flick vs blick I Sentence { John swims vs John metaphorically every I Several meanings (WSD): I Words { fish I Sentence { John saw the man with the binoculars I Same meaning (semantic similarity): I Word { sofa vs couch I Sentence { John gave Hannah a gift vs John gave a gift to Hannah I Truth conditions: I All kings are male I Molybdenum conducts electricity I Entailment: I Alfred murdered the librarian I The librarian is dead I Participant roles: John is the `giver', Hannah is the `receiver' INF5830 Introduction to Semantic Role Labeling 4(36) Introduction Representing events I We want to understand the event described by these sentences: 1. IBM bought Spark 2. IBM acquired Spark 3. Spark was acquired by IBM 4. The owners of Spark sold it to IBM I Dependency parsing is insufficient. UDPipe will give us simple relations between verbs and arguments: 1. (buy, nsubj, IBM), (buy, obj, Spark) 2.
    [Show full text]
  • Using Frame Semantics in Natural Language Processing
    Using Frame Semantics in Natural Language Processing Apoorv Agarwal Daniel Bauer Owen Rambow Dept. of Computer Science Dept. of Computer Science CCLS Columbia University Columbia University Columbia University New York, NY New York, NY New York, NY [email protected] [email protected] [email protected] Abstract the notion of a social event (Agarwal et al., 2010), We summarize our experience using a particular kind of event which involves (at least) FrameNet in two rather different projects two people such that at least one of them is aware in natural language processing (NLP). of the other person. If only one person is aware We conclude that NLP can benefit from of the event, we call it Observation (OBS): for FrameNet in different ways, but we sketch example, someone is talking about someone else some problems that need to be overcome. in their absence. If both people are aware of the event, we call it Interaction (INR): for example, 1 Introduction one person is telling the other a story. Our claim We present two projects at Columbia in which we is that links in social networks are in fact made use FrameNet. In these projects, we do not de- up of social events: OBS social events give rise velop basic NLP tools for FrameNet, and we do to one-way links, and INR social events to two- not develop FramNets for new languages: we sim- way links. For more information, see (Agarwal ply use FrameNet or a FrameNet parser in an NLP and Rambow, 2010; Agarwal et al., 2013a; Agar- application.
    [Show full text]
  • Event-Based Knowledge Reconciliation Using Frame Embeddings And
    Knowledge-Based Systems 135 (2017) 192–203 Contents lists available at ScienceDirect Knowle dge-Base d Systems journal homepage: www.elsevier.com/locate/knosys Event-base d knowle dge reconciliation using frame emb e ddings and frame similarity ∗ Mehwish Alam a, Diego Reforgiato Recupero b,d, , Misael Mongiovi c, Aldo Gangemi a,d, Petar Ristoski e a Université Paris 13, 99 avenue JB Clément, Villetaneuse, Paris 93430, France b Università degli Studi di Cagliari, Department of Mathematics and Computer Science, Via Ospedale 72, Cagliari 09124, Italy c CNR, ISTC, Catania, Italy d CNR, ISTC, Via S. Martino della Battaglia 44, Rome, Italy e University of Mannheim, Mannheim, Germany a r t i c l e i n f o a b s t r a c t Article history: This paper proposes an evolution over MERGILO, a tool for reconciling knowledge graphs extracted from Received 6 April 2017 text, using graph alignment and word similarity. The reconciled knowledge graphs are typically used Revised 9 August 2017 for multi-document summarization, or to detect knowledge evolution across document series. The main Accepted 14 August 2017 point of improvement focuses on event reconciliation i.e., reconciling knowledge graphs generated by text Available online 16 August 2017 about two similar events described differently. In order to gather a complete semantic representation of Keywords: events, we use FRED semantic web machine reader, jointly with Framester, a linguistic linked data hub Knowledge reconciliation represented using a novel formal semantics for frames. Framester is used to enhance the extracted event Frame semantics knowledge with semantic frames.
    [Show full text]
  • Out-Of-Domain Framenet Semantic Role Labeling
    Out-of-domain FrameNet Semantic Role Labeling Silvana Hartmann§†, Ilia Kuznetsov†, Teresa Martin§†, Iryna Gurevych§† Research Training Group AIPHES Ubiquitous§ Knowledge Processing (UKP) Lab Department† of Computer Science, Technische Universitat¨ Darmstadt http://www.ukp.tu-darmstadt.de Abstract on a range of benchmark datasets. This is crucial as the demand for semantic textual analysis of large- Domain dependence of NLP systems is one scale web data keeps growing. of the major obstacles to their application in large-scale text analysis, also restrict- Based on FrameNet (Fillmore et al., 2003), ing the applicability of FrameNet semantic FrameNet SRL extracts frame-semantic structures role labeling (SRL) systems. Yet, current on the sentence level that describe a specific FrameNet SRL systems are still only eval- situation centered around a semantic predicate, uated on a single in-domain test set. For often a verb, and its participants, typically the first time, we study the domain depen- syntactic arguments or adjuncts of the predicate. frame dence of FrameNet SRL on a wide range of The predicate is assigned a label, essentially benchmark sets. We create a novel test set a word sense label, that defines the situation and semantic roles for FrameNet SRL based on user-generated determines the of the participants. web text and find that the major bottleneck The following sentence from FrameNet provides Grinding for out-of-domain FrameNet SRL is the an example of the frame and its roles: frame identification step. To address this [The mill]Grinding cause grindsGrinding [the problem, we develop a simple, yet efficient malt]P atient [to grist]Result.
    [Show full text]
  • Verbnet Guidelines
    VerbNet Annotation Guidelines 1. Why Verbs? 2. VerbNet: A Verb Class Lexical Resource 3. VerbNet Contents a. The Hierarchy b. Semantic Role Labels and Selectional Restrictions c. Syntactic Frames d. Semantic Predicates 4. Annotation Guidelines a. Does the Instance Fit the Class? b. Annotating Verbs Represented in Multiple Classes c. Things that look like verbs but aren’t i. Nouns ii. Adjectives d. Auxiliaries e. Light Verbs f. Figurative Uses of Verbs 1 Why Verbs? Computational verb lexicons are key to supporting NLP systems aimed at semantic interpretation. Verbs express the semantics of an event being described as well as the relational information among participants in that event, and project the syntactic structures that encode that information. Verbs are also highly variable, displaying a rich range of semantic and syntactic behavior. Verb classifications help NLP systems to deal with this complexity by organizing verbs into groups that share core semantic and syntactic properties. VerbNet (Kipper et al., 2008) is one such lexicon, which identifies semantic roles and syntactic patterns characteristic of the verbs in each class and makes explicit the connections between the syntactic patterns and the underlying semantic relations that can be inferred for all members of the class. Each syntactic frame in a class has a corresponding semantic representation that details the semantic relations between event participants across the course of the event. In the following sections, each component of VerbNet is identified and explained. VerbNet: A Verb Class Lexical Resource VerbNet is a lexicon of approximately 5800 English verbs, and groups verbs according to shared syntactic behaviors, thereby revealing generalizations of verb behavior.
    [Show full text]
  • Rs and IOS Press
    ECAI 2014 57 T. Schaub et al. (Eds.) © 2014 The Authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License. doi:10.3233/978-1-61499-419-0-57 Effective and Robust Natural Language Understanding for Human-R obot Interaction Emanuele Bastianelli(‡), Giuseppe Castellucci(•), Danilo Croce(†), Roberto Basili(†), Daniele Nardi() (†) DII, (‡) DICII, (•) DIE - University of Rome Tor Vergata - Rome, Italy {bastianelli,castellucci}@ing.uniroma2.it, {basili,croce}@info.uniroma2.it () DIAG - Sapienza University of Rome - Rome, Italy [email protected] Abstract. Robots are slowly becoming part of everyday life, as knowledge). Moreover, in HRI, different scenarios need to be con- they are being marketed for commercial applications (viz. telepres- sidered. In some situations, e.g. rescue robotic tasks, the precision is ence, cleaning or entertainment). Thus, the ability to interact with crucial and no command misunderstanding is allowed. non-expert users is becoming a key requirement. Even if user ut- Recently, works in the interpretation of natural language instruc- terances can be efficiently recognized and transcribed by Automatic tions for robots in controlled environments have been focused on Speech Recognition systems, several issues arise in translating them specific subsets of the human language. The interpretation process into suitable robotic actions. In this paper, we will discuss both ap- is mainly carried out through the adoption of specific grammars that proaches providing two existing Natural Language Understanding describe the human language allowed. It gives, for example, a robotic workflows for Human Robot Interaction.
    [Show full text]
  • UMCC DLSI: Textual Similarity Based on Lexical-Semantic Features
    UMCC_DLSI: Textual Similarity based on Lexical-Semantic features Alexander Chávez, Antonio Fernández Orquín, Andrés Montoyo, Rafael Muñoz Héctor Dávila, Yoan Gutiérrez, Armando DLSI, University of Alicante Carretera de Collazo, José I. Abreu San Vicente S/N Alicante, Spain. DI, University of Matanzas {montoyo,rafael}@dlsi.ua.es Autopista a Varadero km 3 ½ Matanzas, Cuba. {alexander.chavez, tony, hector.davila, yoan.gutierrez, armando.collazo, jose.abreu}@umcc.cu necessarily a student). In addition, STS differs Abstract from TE and Paraphrase in that, rather than being a binary yes/no decision, STS is a This paper describes the specifications and results of UMCC_DLSI system, which similarity-graded notion (e.g. a student is more participated in the Semantic Textual similar to a person than a dog to a person). Similarity task (STS) of SemEval-2013. Our This graded bidirectional is useful for NLP supervised system uses different types of tasks such as Machine Translation (MT), lexical and semantic features to train a Information Extraction (IE), Question Bagging classifier used to decide the correct Answering (QA), and Summarization. Several option. Related to the different features we semantic tasks could be added as modules in the can highlight the resource ISR-WN used to STS framework, “such as Word Sense extract semantic relations among words and Disambiguation and Induction, Lexical the use of different algorithms to establish Substitution, Semantic Role Labeling, Multiword semantic and lexical similarities. In order to establish which features are the most Expression detection and handling, Anaphora appropriate to improve STS results we and Co-reference resolution, Time and Date 1 participated with three runs using different resolution and Named Entity, among others” set of features.
    [Show full text]
  • Exploring the Integration of Wordnet and Framenet
    Exploring the Integration of WordNet and FrameNet Egoitz Laparra German Rigau Montse Cuadros IXA group, UPV/EHU IXA group, UPV/EHU TALP Research Center, UPC Donostia, Spain Donostia, Spain Barcelona, Spain [email protected] [email protected] [email protected] Abstract However, building large and rich enough predi- cate models for broad–coverage semantic process- This paper presents a novel automatic approach to ing takes a great deal of expensive manual effort partially integrate FrameNet and WordNet. In that involving large research groups during long peri- way we expect to extend FrameNet coverage, to ods of development. Thus, the coverage of cur- enrich WordNet with frame semantic information rently available predicate-argument resources is and possibly to extend FrameNet to languages other still unsatisfactory. For example, (Burchardt et than English. The method uses a knowledge-based al., 2005) or (Shen and Lapata, 2007) indicate the Word Sense Disambiguation algorithm for match- limited coverage of FrameNet as one of the main ing the FrameNet lexical units to WordNet synsets. problems of this resource. In fact, FrameNet1.3 Specifically, we exploit a graph-based Word Sense covers around 10,000 lexical-units while for in- Disambiguation algorithm that uses a large-scale stance, WordNet3.0 contains more than 150,000 knowledge-base derived from existing resources. words. Furthermore, the same effort should be in- We have developed and tested additional versions vested for each different language (Subirats and of this algorithm showing substantial improvements Petruck, 2003). Following the line of previous over state-of-the-art results. Finally, we show some works (Shi and Mihalcea, 2005), (Burchardt et al., examples and figures of the resulting semantic re- 2005), (Johansson and Nugues, 2007), (Pennac- source.
    [Show full text]