The Reading tab comprises a set of contextual sections in support of a first reading of a text. These include bibliographical information about the text (title, author, themes, genres, headnote, references) and a choice between a text view that excludes most paratexts and a document view that represents all of the text in the source edition.
Depending on the form, structure, and content of the text, other available contextual sections include a table of contents, a summative view of the poetic form (metre, stanza form, syllabic pattern, rhyme type and scheme), bibliographic information about the source of the text, a statement of editorial principles applied, any text-specific secondary literature, an introductory essay, other versions of the text in ECPA, related works, and a list of other works by the same author.
ECPA supports the reading process through an extensible contextual reading aid function. When hovering over any word a set of word properties is displayed, including standard spelling, lemma, part of speech, word class, and pronunciation according to the International Phonetic Alphabet (IPA). Also identical word tokens and lemmas are highlighted throughout the poem. Additionally, the selected word can be looked up in a number of external dictionaries, thesauri, gazetteers, and reference works.
ECPA makes it easy to add notes or queries to any part of the poetic text by simply clicking on the word or line in question and filling in the annotations form with your details. If the note or query applies not to a word or line, but to an entire stanza or paragraph, or to a piece of paratext, please adjust the context of the annotation settings. Please note that all contributions will be submitted to the editor in the first instance for review. Once peer reviewed, the contribution will be made publicly available under a Creative Commons BY-NC-SA License. This license protects your contributions, but at the same time lets others re-use and build upon them.
We welcome user-contributed content to the Eighteenth-Century Poetry Archive. Throughout the website, you can make contributions to augment an existing piece of information, make a correction to an obvious error (no transcription or edition is without error!), or submit your own notes, glosses, observations, suggestions, readings and interpretations. These contributions help make the resource better for everyone and we thank you for them in advance! These places of peer participation are marked with a icon and are subject to a peer-review process. If you would like to be acknowledged for your contribution, please fill in your contact details in the lower half of the form.
The Analysis tab comprises results from a number of computationally-assisted analytical processes on five core linguistic levels. These analytical layers can be studied individually (hence their containers are collapsible and sortable), when focusing on a particular aspect, for example the relation between verse line and syntax. However they should be considered as connected and interrelated when studying the poem as a whole, as each layer interacts with the others at any given point as well as over time. They require the reader to pay attention to them simultaneously, and to take into account literary as well as sociological features while doing so.
The analysis results thus represent a means of assisting the reader in the task of analysing a poem on a number of interrelated levels. We understand computer-assisted reading as enabling us to realize the full scope of phenomena on phonological, morphological, syntactic, semantic, and pragmatic levels that can be expressed and analysed algorithmically. The key task of fine-tuning the contextual data around every word in a text, i.e. assuming unique significance of every occurrence and its relations to other words, is at the heart of creating the inventory of analytical results. Any of these findings, once verified, are potentially useful, but it is through selection based on relevance and weighting in the context of an individual poem that this potential is fully realized and new insights can be won.
The verse line serves as the basic reference point for all analytical results. Visually as well as rhythmically accentuated, it provides an accessible level of granularity between smaller units such as phonemes, morphemes, and lexemes, and bigger building blocks, such as sentences, stanzas, and ultimately the text as a whole.
As any computationally-assisted analysis of poetic texts that transcends basic quantitative levels is notoriously complex, frequently ambiguous and always error-prone, a word of caution must preface any such undertaking. Despite our best efforts to present useful and interesting results and highlight potential avenues for further investigation, we are aware of the pitfalls and many potential sources of errors, which we will continue to address as ECPA evolves and matures. The main issue of the use of natural language processing (NLP) on language that is both poetic and historically distant can only be addressed through ongoing work to improve domain appropriation, the training of tools on historical corpora, and the contribution of human knowlegde in the form of textual notes, glosses, corrections and queries, interpretations, and models.
The long-term aim of this applied computational criticism is to make it possible, analytically, to "zoom" into a single phoneme, morpheme, lexeme and to seamlessly "zoom" out to the word, the line, the stanza, the whole poem, or even a cluster of poems, thus supplementing the current focus on close reading of individual texts with an analysis of the historical and cultural functions of poetic form on a larger scale. At the functional level, it should be possible to enhance the the website with new tools and let them operate on the underlying texts. Analysis thus becomes an act not separate from, but integrated with the act of reading. The integration of tools with the corpus, rather than as a separate entity, modifies the texts to "research objects".
This layer is concerned with elements traditionally associated with the musical aspects of lyric poems. These include metre, rhythm, rhetorical figures such as alliteration, assonance, and consonance, and, of course, rhyme. In the context of the long tradition of oral transmission of poetry, these patterns of repetition and composition (sonic, rhythmic, or otherwise), contribute to making poems more cohesive and memorizable. More generally, the sound schemata in this layer interact, frequently supporting, countering, or playing with the components of the other layers.
Throughout the 18th century, the dominant prosodic mode is accentual-syllabic, which is based on recurrent units (feet) comprised of any combination of stressed and unstressed syllables in an invariant sequence. This sequence, which is abstracted from the observed combination of stressed and unstressed syllables, is assigned to the poem as its metrical pattern or metre. Distinct from metre, we define realisation, narrowly, as the actual verse pattern of stressed and unstressed syllables (line rhythm) that contrasts or coincides with the metrical pattern.*
The properties highlighted for end rhymes include the rhyme label, the position in the rhyme pattern and stanza type, properties related to stress patterns and across word boundaries, as well as the type(s) of similarity with matching rhymes. Related rhymes are highlighted when hovering over the rhyme words. Some of the global properties of rhyme are summarized in the Poetic Form section in the reading-view.
The rhetorical figures currently detected in the phonological domain are alliteration, paroemion, assonance, and consonance. The components of each figure are highlighted when hovering over the constituent words. Individual patterns of each figure will be highlighted globally when hovering over the identified pattern. Freezing the line currently selected will make it easy to investigate particular sound patterns beyond the confines of the current line, thus highlighting their movement in space and time.
* As such, realisation must be confused neither with a view that considers metre as variant, nor with a rendition of the line, which can take many different forms depending on the speaker and the historical context. Of course, there is no 1:1 relation between stress and metrical prominence, and a metrical position can be filled by more than one syllable. Realisation is here merely used as a simplified way of recording stress and metrical patterns, but still allows us to highlight interesting phenomena, such as stressed syllables in metrically non-prominent positions etc.
This layer is concerned with the internal structure of words as well as word formation mechanisms. It focuses on the poem as written language, as an act of composition, selecting and arranging morphemes into words and phrases. It examines features such as word formation and choice, syllabic phenomena, composition, and word origins.
Syllables, morphemes, and words are independent units of structure, i.e. there are morphemes that are not syllables, syllables that are not morphemes, words that just consist of a single syllable or morpheme. The number of syllables per line is displayed here and any variations given.
The brief morphological table lists every word in the selected line, giving the word, number of syllables, lemma, word class, part of speech, indication of upper case, word frequency in the poem, and a KWIC list. The plan is to expand the current view to a full morphological analysis in the future.
The rhetorical figures currently detected in the morphological domain are polyptoton, epizeuxis, diacope, anaphora, epistrophe, aphaeresis, apocope, syncope, and synalepha. The components of each figure are highlighted when hovering over the constituent words. Individual patterns of each figure will be highlighted globally when hovering over the identified pattern. Freezing the line currently selected will make it easy to investigate particular morphological patterns beyond the confines of the current line.
This layer is concerned with the analysis of syntactic structures, the position and arrangement of words, interesting phenomena such as parataxis and hypotaxis, and the relationship between verse line, metrical and rhythmic structures, and syntactic units, such as phrases, clauses, and sentences.
The relationship between verse line and syntactic structure is complex and ever changing as the poetic text is played out in time and space. Metrical pattern, rhythm, rhyme, and syntactic structure need to be studied as closely connected and interrelated. The stanza form is an important indicator for further analysis and will be identified first.
A computationally facilitated analysis of the syntactic structure is presented in form of a syntactic dependency parse of the selected sentence. Dependency as a syntactic theory is based on the idea that all linguistic units are connected to each other by directed links named dependencies. A syntactic dependency parse thus connects linguistic units according to their relationships. The result is a tree diagram, to be read from left to right and top to bottom, that serves as a visual representation of syntactic structure, in which the grammatical hierarchy is graphically displayed. The verb is taken to be the structural centre of the clause structure. All other syntactic units are either directly or indirectly connected to the verb in terms of the directed links. Technically spoken, each vertex in the tree represents a lexical unit, child nodes are units that are dependent on the parent, and edges are labelled by the relationship between the words.
The major word classes, nouns, pronouns, verbs, adjectives, and adverbs, are highlighted with a different colour scheme each to make it easier to process the information and to relate it to the verse lines. Hovering over the nodes and edges highlights the node properties as well as the types of dependencies. Closer integration of the syntactic parse with the poetic text is planned for a future update. As a start, a sentencing button has been introduced that highlights syntactic units in the poetic text.
This layer is concerned with the creation of "literal" meaning on the word and sentence level. The meaning of a sentence is a function of the meaning of its component words (paradigmatic associations) and the way they are combined (syntagmatic associations). The study of the function of this selection and arrangement of words is at the centre of this layer.
For the purpose of a computationally assisted analysis of meaning, frame semantics offers an attractive model as it relativizes word meanings to a finite set of semantic frames. Semantic frames are schematic representations of the conceptual structures and patterns that provide the foundation for meaningful interaction in a given speech community. Thus, semantic frames are linked by linguistic conventions to the meanings of linguistic units (lexical items) constructing a schematic representation of a situation, object, event, or relation and providing the background structure against which words are understood. Each semantic frame identifies a set of frame elements, i.e. participants in the frame (semantic role labels), which in turn are linked to individual lexical units (words). We currently use the SEMAFOR v3.0.4 alpha [pre-trained] software to generate the frame semantic parses and the frameviz.js software to visualize them.
Just like the syntactic dependency parse, the frame semantic parse is meant to be read from left to right and top to bottom. At the top are the lexical units of the selected sentence. Below the lexical units are the names of the evoked frames, a complete list of which can be found at FrameNet. The subsequent rows indicate the frame elements for each frame. Frame element spans are indicated with blue bars. For the future, the plan is both to adapt the frames for the narrower context of eighteenth-century writing by supplementing the frame associations with historical contextual information, such as contemporary dictionaries and historical gazetteers, and to integrate the results of this analysis more closely with the other analytical layers.
NB: Meaning is notoriously difficult to establish computationally, and the analysis presented here should be considered experimental. Frames may be evoked erroneously when lexical units are not or wrongly mapped to frame elements due to changes in word meaning, a loss of meaning altogether, or a change of associations conveyed through historical distance between speaker and recipient, or insufficient domain appropriation.
The rhetorical figures currently detected in the semantic domain are similie and homophonic paronomasia. The components of each figure are highlighted when hovering over the constituent words. Individual patterns of each figure will be highlighted globally when hovering over the identified pattern. Please be aware that semantic figures of speech are complex entities and provide a considerable challenge for computationally facilitated detection.
Unlike semantics, which is concerned with the creation of meaning on the level of the word and sentence, this layer is concerned with the creation of meaning in context. The poem is considered as spoken language, i.e. as an act of internal communication between speaker and addressee in the text, and external communication between the poet and the reader and in a wider sense with society itself. This layer comprises elements such as discourse, intention, argumentative structure, internal and external contexts, themes and genre, and real-world references (e.g. named entities).
The rhetorical figures currently detected in the pragmatic domain are ecphonesis, apostrophe, and pysma. The components of each figure are highlighted when hovering over the constituent parts. Individual patterns of each figure will be highlighted globally when hovering over the identified pattern.
Among the various types of referring expressions, references to named entities are crucial to the communication process in a specific context. For the purpose of identifying these references to named entities, ECPA uses a hand-curated and domain customized gazetteer that has been constructed from the results of four NERC engines (Morphadorner, Stanford NER, MITIE, and OpenNLP). We have supplemented the gazetteer with automated PoS-based named entity recognition (classifier), and the source of the detected named entity will be identified from one of these two sources.
As an enabling technology, Named Entity Recognition and Classification is often a pre-processing step for relation extraction, knowledge base generation (taxonomies, ontologies, thesauri), question answering, semantic search, and proper noun and pronominal coreference resolution. All of these areas may offer valuable avenues of enquiry in future updates.