Search Results (8)

GPT (Generative Pre-trained Transformer) is a generative language model that demonstrates outstanding performance in the field of text generation. Generally, the attention mechanism of the transformer model behaves similarly to a copy distribution. However, due to the absence of a dedicated encoder, it is challenging to ensure that the input is retained for generation. We propose a model that emphasizes the copy mechanism in GPT. We generate masks for the input words to initialize the distribution and explicitly encourage copying through training. To demonstrate the effectiveness of our approach, we conducted experiments to restore ellipsis and anaphora in dialogue. In a single domain, we achieved 0.4319 (BLEU), 0.6408 (Rouge-L), 0.9040 (simCSE), and 0.9070 (BERTScore), while in multi-domain settings we obtained 0.4611 (BLEU), 0.6379 (Rouge-L), 0.8902 (simCSE), and 0.8999 (BERTScore). Additionally, we evaluated the operation of the copy mechanism on out-of-domain data, yielding excellent results. We anticipate that applying the copy mechanism to GPT will be useful for utilizing language models in constrained situations. Full article

Business and science are using sentiment analysis to extract and assess subjective information from the web, social media, and other sources using NLP, computational linguistics, text analysis, image processing, audio processing, and video processing. It models polarity, attitudes, and urgency from positive, negative, or neutral inputs. Unstructured data make emotion assessment difficult. Unstructured consumer data allow businesses to market, engage, and connect with consumers on social media. Text data are instantly assessed for user sentiment. Opinion mining identifies a text’s positive, negative, or neutral opinions, attitudes, views, emotions, and sentiments. Text analytics uses machine learning to evaluate “unstructured” natural language text data. These data can help firms make money and decisions. Sentiment analysis shows how individuals feel about things, services, organizations, people, events, themes, and qualities. Reviews, forums, blogs, social media, and other articles use it. DD (data-driven) methods find complicated semantic representations of texts without feature engineering. Data-driven sentiment analysis is three-tiered: document-level sentiment analysis determines polarity and sentiment, aspect-based sentiment analysis assesses document segments for emotion and polarity, and data-driven (DD) sentiment analysis recognizes word polarity and writes positive and negative neutral sentiments. Our innovative method captures sentiments from text comments. The syntactic layer encompasses various processes such as sentence-level normalisation, identification of ambiguities at paragraph boundaries, part-of-speech (POS) tagging, text chunking, and lemmatization. Pragmatics include personality recognition, sarcasm detection, metaphor comprehension, aspect extraction, and polarity detection; semantics include word sense disambiguation, concept extraction, named entity recognition, anaphora resolution, and subjectivity detection. Full article

In the age of big data, there is increasing growth of data on the Internet. It becomes frustrating for users to locate the desired data. Therefore, text summarization emerges as a solution to this problem. It summarizes and presents the users with the gist of the provided documents. However, summarizer systems face challenges, such as poor grammaticality, missing important information, and redundancy, particularly in multi-document summarization. This study involves the development of a graph-based extractive generic MDS technique, named Grapharizer (GRAPH-based summARIZER), focusing on resolving these challenges. Grapharizer addresses the grammaticality problems of the summary using lemmatization during pre-processing. Furthermore, synonym mapping, multi-word expression mapping, and anaphora and cataphora resolution, contribute positively to improving the grammaticality of the generated summary. Challenges, such as redundancy and proper coverage of all topics, are dealt with to achieve informativity and representativeness. Grapharizer is a novel approach which can also be used in combination with different machine learning models. The system was tested on DUC 2004 and Recent News Article datasets against various state-of-the-art techniques. Use of Grapharizer with machine learning increased accuracy by up to 23.05% compared with different baseline techniques on ROUGE scores. Expert evaluation of the proposed system indicated the accuracy to be more than 55%. Full article

Many so-called ‘zero tense’-marked (which we define as morphologically reduced and underspecified inflections) or untensed verb forms found in tenseless languages, have been characterized as context dependent for their temporal and aspectual interpretation, with the verb’s aspectual content (either as event structure or viewpoint properties) being given more or less prominent roles in their temporal anchoring. In this paper, we focus on a morpho-phonologically reduced inflectional verbal paradigm in Anindilyakwa (Groote Eylandt archipelago, NT, Australia), which is both temporally and aspectually underspecified, and constitutes an instance of zero tense as defined above. On the basis of a quantitative study of an annotated corpus of zero-inflected utterances, we establish that in the absence of independent overt or covert temporal information, the temporal anchoring of this ‘zero tense’ exhibits complex patterns of sensitivity to event structural parameters. Notably we establish that while dynamicity/stativity and telicity/atelicity are to some extent valuable predictors for the temporal interpretation of zero tense in Anindilyakwa, only atomicity (i.e., event punctuality) and boundedness categorically impose a past temporal anchoring—this confirms insights found in previous works, both on Anindilyakwa and on other languages, while also differing from other generalisations contained in these works. Our analysis also shows that unlike several zero tenses identified in various languages (especially in Pidgins and Creoles), Anindilyakwa zero tense-marked dynamic utterances do not correlate with a past temporal reading. Rather, we show that Anindilyakwa seems to come closest to languages possessing zero tensed-verbs (or tenseless verbs) where boundedness monotonically enforces a past temporal anchoring, such as Navajo and Mandarin Chinese. We also show that aspect-independent temporal information appears to determine the temporal anchoring of all zero tense-marked unbounded atelic utterances (both stative and dynamic) in Anindilyakwa—a fact at once conflicting with some claims made in previous works on zero tenses, while confirming results from past studies of Indigenous languages of the Americas (especially Yucatec Maya), concerning the role of temporal anaphora in the temporal interpretation of ‘tenseless’ verb forms. Full article

Post-authorship attribution is a scientific process of using stylometric features to identify the genuine writer of an online text snippet such as an email, blog, forum post, or chat log. It has useful applications in manifold domains, for instance, in a verification process to proactively detect misogynistic, misandrist, xenophobic, and abusive posts on the internet or social networks. The process assumes that texts can be characterized by sequences of words that agglutinate the functional and content lyrics of a writer. However, defining an appropriate characterization of text to capture the unique writing style of an author is a complex endeavor in the discipline of computational linguistics. Moreover, posts are typically short texts with obfuscating vocabularies that might impact the accuracy of authorship attribution. The vocabularies include idioms, onomatopoeias, homophones, phonemes, synonyms, acronyms, anaphora, and polysemy. The method of the regularized deep neural network (RDNN) is introduced in this paper to circumvent the intrinsic challenges of post-authorship attribution. It is based on a convolutional neural network, bidirectional long short-term memory encoder, and distributed highway network. The neural network was used to extract lexical stylometric features that are fed into the bidirectional encoder to extract a syntactic feature-vector representation. The feature vector was then supplied as input to the distributed high networks for regularization to minimize the network-generalization error. The regularized feature vector was ultimately passed to the bidirectional decoder to learn the writing style of an author. The feature-classification layer consists of a fully connected network and a SoftMax function to make the prediction. The RDNN method was tested against thirteen state-of-the-art methods using four benchmark experimental datasets to validate its performance. Experimental results have demonstrated the effectiveness of the method when compared to the existing state-of-the-art methods on three datasets while producing comparable results on one dataset. Full article

Anaphora resolution is a crucial task for information extraction. Syntax-based approaches are based on the syntactic structure of sentences. Knowledge-poor approaches aim at avoiding the need for further external resources or knowledge to carry out their task. This paper proposes a knowledge-poor, syntax-based approach to anaphora resolution in English texts. Our approach improves the traditional algorithm that is considered the standard baseline for comparison in the literature. Its most relevant contributions are in its ability to handle differently different kinds of anaphoras, and to disambiguate alternate associations using gender recognition of proper nouns. The former is obtained by refining the rules in the baseline algorithm, while the latter is obtained using a machine learning approach. Experimental results on a standard benchmark dataset used in the literature show that our approach can significantly improve the performance over the standard baseline algorithm used in the literature, and compares well also to the state-of-the-art algorithm that thoroughly exploits external knowledge. It is also efficient. Thus, we propose to use our algorithm as the new baseline in the literature. Full article

A liturgical phenomenology of Roman Catholic priesthood based on the experience of images of priests and people in scripture and liturgy lends itself to a renewed appropriation of Vatican II and post-conciliar approaches to priesthood. The authors interpret the relational dynamics of Christ’s own priesthood using the pericope of Christ’s anointing at Bethany (Mark 14:1–9), followed by a phenomenological examination of the dialogical introduction to the Eucharistic Prayer or anaphora in the Roman and Byzantine Eucharistic rites. The way ordained ministry is exercised in dialogical and symbolic fashions then provides the impetus for a new look at the significance of prostration in the context of Good Friday and of the Roman Catholic ordination rite. The trinitarian implications of the unified but differentiated priesthood of the Church are the theme of the final section. Full article

Although amnesic H.M. typically could not recall where or when he met someone, he could recall their topics of conversation after long interference-filled delays, suggesting impaired encoding for some categories of novel events but not others. Similarly, H.M. successfully encoded into internal representations (sentence plans) some novel linguistic structures but not others in the present language production studies. For example, on the Test of Language Competence (TLC), H.M. produced uncorrected errors when encoding a wide range of novel linguistic structures, e.g., violating reliably more gender constraints than memory-normal controls when encoding referent-noun, pronoun-antecedent, and referent-pronoun anaphora, as when he erroneously and without correction used the gender-inappropriate pronoun “her” to refer to a man. In contrast, H.M. never violated corresponding referent-gender constraints for proper names, suggesting that his mechanisms for encoding proper name gender-agreement were intact. However, H.M. produced no more dysfluencies, off-topic comments, false starts, neologisms, or word and phonological sequencing errors than controls on the TLC. Present results suggest that: (a) frontal mechanisms for retrieving and sequencing word, phrase, and phonological categories are intact in H.M., unlike in category-specific aphasia; (b) encoding mechanisms in the hippocampal region are category-specific rather than item-specific, applying to, e.g., proper names rather than words; (c) H.M.’s category-specific mechanisms for encoding referents into words, phrases, and propositions are impaired, with the exception of referent gender, person, and number for encoding proper names; and (d) H.M. overuses his intact proper name encoding mechanisms to compensate for his impaired mechanisms for encoding other functionally equivalent linguistic information. Full article