Article: A semantic abstraction criterion to reduce complexity on automatically discovered declarative maps Journal: International Journal of Business Process Integration and Management IJBPIM 2022 Vol 11 No.1 pp.1 18 Abstract: A declarative approach can be employed on the definition of constraints that limit process execution possibilities. This perspective is appropriate when dealing with unstructured or flexible processes, a.k.a. knowledge-intensive processes. Declarative process mining may result in complex models due the discovery of a high quantity of constraints, producing models with excessive complexity. As abstractions are seen as an effective approach to represent readable models, this work proposes to create language-independent hierarchical declarative maps using a linguistic hierarchy of activities. The proposed approach applies natural language processing to build more abstract declarative models produced by process mining. The presented method was evaluated in a case study with real life data and support from domain experts. The findings showed that it is possible to generate meaningful groups by looking for the semantics of activity labels in order to create abstract process views with reduced complexity, starting from a low-level declarative map. Inderscience Publishers linking academia, business and industry through research

For example, a list like democracy, crocodile, microwave may become dem_cracy, cro_odile, microw_ after some forgetting. Missing segments might then be replaced by looking up similar sounding words in long-term memory (Baddeley et al., 1998; Hulme et al., 1991; Schweickert, 1993). Although this process is based on long-term phonological, as opposed to semantic, knowledge, information about word meaning might contribute to the selection of words for reconstruction (Poirier & Saint-Aubin, 1995). In an alternative conception, verbal short-term memory reflects temporary activation of the same representations that underpin the production and comprehension of language (Martin & Saffran, 1997; Patterson et al., 1994).

Typically this process occurs due to different extralinguistic reasons, such as cultural and worldview changes occurring. Information we choose to hold on to from the sensory store passes to the short-term memory store. An example of semantic memory is knowledge about the meaning of words. Other algorithms that help with understanding of words are lemmatisation and stemming. These are text normalisation techniques often used by search engines and chatbots. Stemming algorithms work by using the end or the beginning of a word (a stem of the word) to identify the common root form of the word.

Semantic change: reclamation

It is implicit rather than explicit memory and no conscious processing needs to take place. Mandler (1967) conducted and experiment in which he gave participants a pack of 52 picture cards, semantic processing definition each of which had a word printed on it. Participants were then asked to sort the cards into piles, using anything from two to seven categories, and could go by any system the wished.

The results of the preceding studies generally indicate

that lexical decision tasks are not reliable instruments to guarantee the

validity of the results. The investigation of bilingual mental lexicon has in fact rested

on the assumption that generalization from monolingual lexical studies

to the more complex domain of bilingual lexicon is not based on any a priori

justification. The predominant contribution of priming in this relation

has been to determine the possibility of dual ML in bilinguals. Bank must thus be kept in a pending tray somewhere until more data are available. Linguists thus believe the brain must have a storage pad in which sentences are assembled and disassembled, with the words represented in a way which is still neutral as to which meaning is the right one.

Which is Better: Lexical Search or Semantic Search?

More intelligent AIs raise the prospect of artificial consciousness, which has created a new field of philosophical and applied research. Google Translate may not be good enough yet for medical instructions, but NLP is widely used in healthcare. It is particularly useful in aggregating information from electronic health https://www.metadialog.com/ record systems, which is full of unstructured data. Not only is it unstructured, but because of the challenges of using sometimes clunky platforms, doctors’ case notes may be inconsistent and will naturally use lots of different keywords. NLP can help discover previously missed or improperly coded conditions.

As such, the likelihood of finding their colloquial meaning in the verbal passive, which is usually characteristic of written, formal registers, decreases significantly. Indeed, all of the colloquial transitive drifts proved unable to appear in the verbal passive (see (19b), along with semantic processing definition the drifts marked in bold in Appendix 2, §13). This is strengthened by the fact that all verbal passive drifts can appear in formal contexts (see (19c) and Appendix 2, §11). §3 provides the results of the preliminary pilot survey inspecting the distribution of Hebrew semantic drifts.

This is supported by the fact that novel drifted meanings are often perceived as metaphoric extensions of the original meaning before becoming conventionalized (e.g., Cruse & Croft 2004). Moreover, speakers are initially aware of the various meanings of a certain word, connecting between these meanings (Antilla 1989). However, once drifts “stabilize” and become substantially more frequent compared to the original meaning, they head their own lexical entry (as suggested, for instance, by Kawamoto 1993). Cases where the new meaning “takes over” the original one, which is rendered outdated and is preserved in frozen expressions or very limited contexts, cohere with separate listing (see example (1) above and the preceding discussion). However, not all drifts involving a change in argument structure were excluded.

• For eagle, this would be pretty similar to the vervet’s representation of the eagle call’s meaning.
• Italian, which is more relaxed about the overt expression of a subject, allows example (1) to be captured with a single word (2), which is also a well-formed sentence.
• Unique drifts were attested in transitives, unaccusatives, and adjectival passives, but not in verbal passives.
• Semantic analysis would help the computer learn about less literal meanings that go beyond the standard lexicon.
• Semantic analysis techniques are deployed to understand, interpret and extract meaning from human languages in a multitude of real-world scenarios.

For example, the stem of “caring” would be “car” rather than the correct base form of “care”. Lemmatisation uses the context in which the word is being used and refers back to the base form according to the dictionary. So, a lemmatisation algorithm would understand that the word “better” has “good” as its lemma.

This study will also attempt to explain why not all drifted transitives share their drifts with their verbal passive counterparts (18), while all verbal passive drifts are shared with their transitive alternants. Moreover, the frequency of non-drifted verbal passives sometimes exceeded that of uniquely-drifted predicates from other diatheses. Hence, the complete lack of unique verbal passive drifts (and idioms) is beyond what one would expect based on frequency alone. The results of the quantitative dictionary survey further showed that all diatheses giving rise to unique drifts – transitives, unaccusatives, and adjectival passives – can share drifts with their respective unaccusative and transitive root counterparts.

• If activation of an L2

  supreordinate spreads to the lower node in L1, then presentation of the

  L2 primes should facilitate translation of the L1 targets by reducing the

  response latencies of the translations.

• Semantic analysis is a key area of study within the field of linguistics that focuses on understanding the underlying meanings of human language.
• This impairment of semantic memory is very debilitating in everyday life because people struggle to understand what is said to them, to remember the names of objects and to recognise the significance of items and people they encounter.
• I can now explain the results of the quantitative survey along lines proposed by Horvath & Siloni (2009, 2019) regarding the distribution of phrasal idioms.
• By using both semantics and segmentation, businesses can gain a better understanding of their target audience and develop more effective strategies for reaching them.
• Where the attributes of a data value are not available in a

  dictionary listing, it may be assumed that a character string

  interpretable as a number should be taken to represent an item of type

  numb.

Frenck and Pynte (1987) suggested that the priming facilitation observed

may not have been the result of effortless, automatic processing but was

rather due to the conscious, strategic use of primes. They argue that in

order to truly substantiate the hypothesis of a common semantic network,

it is necessary to show that across-language priming facilitation is the

result of non-controlled, automatic processing. Keatly and Gelder (1992)

also found that cross-language (but not within-language) priming disappeared

when subjects were required to respond at a fixed fast rate. Support from word meaning might be especially critical for those with additional phonological problems who struggle to process/maintain the sounds of items. By this view, it is conceivable that a healthy phonological system is sufficient for immediate serial recall and that semantic memory does not play an essential role in normal recall.