SemEval ( Sem antic Eval uation) is an ongoing series of evaluations of computational semantic analysis systems; it evolved from the Senseval word sense evaluation series. The evaluations are intended to explore the nature of meaning in language. While meaning is intuitive to humans, transferring those intuitions to computational analysis has proved elusive.
32-490: This series of evaluations is providing a mechanism to characterize in more precise terms exactly what is necessary to compute in meaning. As such, the evaluations provide an emergent mechanism to identify the problems and solutions for computations with meaning. These exercises have evolved to articulate more of the dimensions that are involved in our use of language. They began with apparently simple attempts to identify word senses computationally. They have evolved to investigate
64-492: A qualifier , such as " sensu stricto " ("in the strict sense") or " sensu lato " ("in the broad sense") are sometimes used to clarify what is meant by a text. Polysemy entails a common historic root to a word or phrase. Broad medical terms usually followed by qualifiers , such as those in relation to certain conditions or types of anatomical locations are polysemic, and older conceptual words are with few exceptions highly polysemic (and usually beyond shades of similar meaning into
96-419: A sub-distinction. A word sense corresponds either neatly to a seme (the smallest possible unit of meaning ) or a sememe (larger unit of meaning), and polysemy of a word of phrase is the property of having multiple semes or sememes and thus multiple senses. Often the senses of a word are related to each other within a semantic field . A common pattern is that one sense is broader and another narrower. This
128-417: A word. This process uses context to narrow the possible senses down to the probable ones. The context includes such things as the ideas conveyed by adjacent words and nearby phrases, the known or probable purpose and register of the conversation or document, and the orientation (time and place) implied or expressed. The disambiguation is thus context-sensitive . Advanced semantic analysis has resulted in
160-502: A workshop entitled Tagging with Lexical Semantics: Why, What, and How? in conjunction with the Conference on Applied Natural Language Processing. At the time, there was a clear recognition that manually annotated corpora had revolutionized other areas of NLP, such as part-of-speech tagging and parsing , and that corpus-driven approaches had the potential to revolutionize automatic semantic analysis as well. Kilgarriff recalled that there
192-400: Is a dedicated track for Semantic Taxonomy with a new Semantic Taxonomy Enrichment task. Word sense In linguistics , a word sense is one of the meanings of a word . For example, a dictionary may have over 50 different senses of the word " play ", each of these having a different meaning based on the context of the word's usage in a sentence , as follows: We went to see
224-449: Is often the case in technical jargon , where the target audience uses a narrower sense of a word that a general audience would tend to take in its broader sense. For example, in casual use " orthography " will often be glossed for a lay audience as " spelling ", but in linguistic usage "orthography" (comprising spelling, casing , spacing , hyphenation , and other punctuation ) is a hypernym of "spelling". Besides jargon, however,
256-665: Is the understanding of how different sentence and textual elements fit together. Tasks in this area include semantic role labeling, semantic relation analysis, and coreference resolution. Other tasks in this area look at more specialized issues of semantic analysis, such as temporal information processing, metonymy resolution, and sentiment analysis. The tasks in this area have many potential applications, such as information extraction, question answering, document summarization, machine translation, construction of thesauri and semantic networks, language modeling, paraphrasing, and recognizing textual entailment. In each of these potential applications,
288-418: Is to replicate human processing by means of computer systems. The tasks (shown below) are developed by individuals and groups to deal with identifiable issues, as they take on some concrete form. The first major area in semantic analysis is the identification of the intended meaning at the word level (taken to include idiomatic expressions). This is word-sense disambiguation (a concept that is evolving away from
320-453: The lexemes and started to evaluate systems that looked into wider areas of semantics, such as Semantic Roles (technically known as Theta roles in formal semantics), Logic Form Transformation (commonly semantics of phrases, clauses or sentences were represented in first-order logic forms ) and Senseval-3 explored performances of semantics analysis on Machine translation . As the types of different computational semantic systems grew beyond
352-506: The multilingual lexical substitution task, where no fixed sense inventory is specified, Multilingual WSD uses the BabelNet as its sense inventory. Prior to the development of BabelNet, a bilingual lexical sample WSD evaluation task was carried out in SemEval-2007 on Chinese-English bitexts. The Cross-lingual WSD task was introduced in the SemEval-2007 evaluation workshop and re-proposed in
SECTION 10
#1732848306643384-402: The play Romeo and Juliet at the theater. The coach devised a great play that put the visiting team on the defensive. The children went out to play in the park. In each sentence different collocates of "play" signal its different meanings. People and computers , as they read words, must use a process called word-sense disambiguation to reconstruct the likely intended meaning of
416-526: The *SEM conference and collocate the SemEval workshop with the *SEM conference. The organizers got very positive responses (from the task coordinators/organizers and participants) about the association with the yearly *SEM, and 8 tasks were willing to switch to 2012. Thus was born SemEval-2012 and SemEval-2013. The current plan is to switch to a yearly SemEval schedule to associate it with the *SEM conference but not every task needs to run every year. The framework of
448-572: The *SEM conference. It was also the decision that not every evaluation task will be run every year, e.g. none of the WSD tasks were included in the SemEval-2012 workshop. From the earliest days, assessing the quality of word sense disambiguation algorithms had been primarily a matter of intrinsic evaluation , and “almost no attempts had been made to evaluate embedded WSD components”. Only very recently had extrinsic evaluations begun to provide some evidence for
480-481: The SemEval coordinators gave the opportunity for task organizers to choose between a 2-year or a 3-year cycle. The SemEval community favored the 3-year cycle. Although the votes within the SemEval community favored a 3-year cycle, organizers and coordinators had settled to split the SemEval task into 2 evaluation workshops. This was triggered by the introduction of the new *SEM conference . The SemEval organizers thought it would be appropriate to associate our event with
512-495: The SemEval-2013 workshop . To facilitate the ease of integrating WSD systems into other Natural Language Processing (NLP) applications, such as Machine Translation and multilingual Information Retrieval , the cross-lingual WSD evaluation task was introduced a language-independent and knowledge-lean approach to WSD. The task is an unsupervised Word Sense Disambiguation task for English nouns by means of parallel corpora. It follows
544-676: The SemEval/Senseval evaluation workshops emulates the Message Understanding Conferences (MUCs) and other evaluation workshops ran by ARPA (Advanced Research Projects Agency, renamed the Defense Advanced Research Projects Agency (DARPA) ). Stages of SemEval/Senseval evaluation workshops Senseval-1 & Senseval-2 focused on evaluation WSD systems on major languages that were available corpus and computerized dictionary. Senseval-3 looked beyond
576-498: The contribution of the types of semantic analysis constitutes the most outstanding research issue. For example, in the word sense induction and disambiguation task, there are three separate phases: The unsupervised evaluation for WSI considered two types of evaluation V Measure (Rosenberg and Hirschberg, 2007), and paired F-Score (Artiles et al., 2009). This evaluation follows the supervised evaluation of SemEval-2007 WSI task (Agirre and Soroa, 2007) The tables below reflects
608-442: The coverage of WSD, Senseval evolved into SemEval, where more aspects of computational semantic systems were evaluated. The SemEval exercises provide a mechanism for examining issues in semantic analysis of texts. The topics of interest fall short of the logical rigor that is found in formal computational semantics, attempting to identify and characterize the kinds of issues relevant to human understanding of language. The primary goal
640-416: The development of standards for evaluation, e.g. the adoption of metrics like precision and recall . Only for the first conference (MUC-1) could the participant choose the output format for the extracted information. From the second conference the output format, by which the participants' systems would be evaluated, was prescribed. For each topic fields were given, which had to be filled with information from
672-487: The following areas of natural language processing . This list is expected to grow as the field progresses. The following table shows the areas of studies that were involved in Senseval-1 through SemEval-2014 (S refers to Senseval and SE refers to SemEval, e.g. S1 refers to Senseval-1 and SE07 refers to SemEval2007): SemEval tasks have created many types of semantic annotations, each type with various schema. In SemEval-2015,
SECTION 20
#1732848306643704-608: The interrelationships among the elements in a sentence (e.g., semantic role labeling ), relations between sentences (e.g., coreference ), and the nature of what we are saying ( semantic relations and sentiment analysis ). The purpose of the SemEval and Senseval exercises is to evaluate semantic analysis systems. " Semantic Analysis " refers to a formal analysis of meaning, and "computational" refer to approaches that in principle support effective implementation. The first three evaluations, Senseval-1 through Senseval-3, were focused on word sense disambiguation (WSD), each time growing in
736-739: The lexical-sample variant of the Classic WSD task, restricted to only 20 polysemous nouns. It is worth noting that the SemEval-2014 have only two tasks that were multilingual/crosslingual, i.e. (i) the L2 Writing Assistant task, which is a crosslingual WSD task that includes English, Spanish, German, French and Dutch and (ii) the Multilingual Semantic Textual Similarity task that evaluates systems on English and Spanish texts. The major tasks in semantic evaluation include
768-502: The notion that words have discrete senses, but rather are characterized by the ways in which they are used, i.e., their contexts). The tasks in this area include lexical sample and all-word disambiguation, multi- and cross-lingual disambiguation, and lexical substitution. Given the difficulties of identifying word senses, other tasks relevant to this topic include word-sense induction, subcategorization acquisition, and evaluation of lexical resources. The second major area in semantic analysis
800-405: The number of languages offered in the tasks and in the number of participating teams. Beginning with the fourth workshop, SemEval-2007 (SemEval-1), the nature of the tasks evolved to include semantic analysis tasks outside of word sense disambiguation. Triggered by the conception of the *SEM conference , the SemEval community had decided to hold the evaluation workshops yearly in association with
832-629: The organizers have decided to group tasks together into several tracks. These tracks are by the type of semantic annotations that the task hope to achieve. Here lists the type of semantic annotations involved in the SemEval workshops: A task and its track allocation is flexible; a task might develop into its own track, e.g. the taxonomy evaluation task in SemEval-2015 was under the Learning Semantic Relations track and in SemEval-2016, there
864-451: The pattern is common even in general vocabulary. Examples are the variation in senses of the term "wood wool" and in those of the word "bean" . This pattern entails that natural language can often lack explicitness about hyponymy and hypernymy . Much more than programming languages do, it relies on context instead of explicitness; meaning is implicit within a context. Common examples are as follows: Usage labels of " sensu " plus
896-552: The realms of being ambiguous ). Homonymy is where two separate-root words ( lexemes ) happen to have the same spelling and pronunciation . Message Understanding Conference The Message Understanding Conferences ( MUC ) for computing and computer science , were initiated and financed by DARPA (Defense Advanced Research Projects Agency) to encourage the development of new and better methods of information extraction . The character of this competition, many concurrent research teams competing against one another—required
928-471: The text. Typical fields were, for example, the cause, the agent, the time and place of an event, the consequences etc. The number of fields increased from conference to conference. At the sixth conference (MUC-6) the task of recognition of named entities and coreference was added. For named entity all phrases in the text were supposed to be marked as person, location, organization, time or quantity. The topics and text sources, which were processed, show
960-401: The value of WSD in end-user applications. Until 1990 or so, discussions of the sense disambiguation task focused mainly on illustrative examples rather than comprehensive evaluation. The early 1990s saw the beginnings of more systematic and rigorous intrinsic evaluations, including more formal experimentation on small sets of ambiguous words. In April 1997, Martha Palmer and Marc Light organized
992-474: The workshop growth from Senseval to SemEval and gives an overview of which area of computational semantics was evaluated throughout the Senseval/SemEval workshops. The Multilingual WSD task was introduced for the SemEval-2013 workshop. The task is aimed at evaluating Word Sense Disambiguation systems in a multilingual scenario using BabelNet as its sense inventory. Unlike similar task like crosslingual WSD or
SemEval - Misplaced Pages Continue
1024-499: Was "a high degree of consensus that the field needed evaluation", and several practical proposals by Resnik and Yarowsky kicked off a discussion that led to the creation of the Senseval evaluation exercises. After SemEval-2010, many participants feel that the 3-year cycle is a long wait. Many other shared tasks such as Conference on Natural Language Learning (CoNLL) and Recognizing Textual Entailments (RTE) run annually. For this reason,
#642357