The Analysis of False Judgement According to Being and Not-Being in Plato’s Theaetetus (188c10–189b9)

The version of the paradox of false judgement examined at Tht. 188c10–189b9 relies on the assumption that to judge falsehoods is to judge the things which are not. The presentation of the argument displays several syntactic ambiguities: at several points it allows the reader to adopt different syntactic connections between the components of sentences. For instance, when Socrates says that in a false judgement the cognizer is “he who judges the things which are not about anything whatsoever” (188d3–4), how should the clause “about anything whatsoever” be construed? In common with “he who judges” and “the things which are not” (in which case the cognizer would be “he who judges about anything whatsoever the things which are not about it”), or exclusively with “he who judges” (in which case the cognizer would be “he who judges about anything whatsoever the things which are not”)? The most plausible answer is that both construals are envisaged. Accordingly, the argument has two branches corresponding to these two alternative construals. In particular, it attempts to show that in both cases the cognizer will address what does not exist – an impossibility. The idea that a false judgement is concerned with what is not about its reference has a clear echo in the Sophist. The way in which the problem is handled in the Theaetetus provides a hint that can help to find a solution for the hotly debated issue of the interpretation of the Sophist’s account of false statement.

PATCHCOMM: Using Commonsense Knowledge to Guide Syntactic Parsers

Syntactic parsing technologies have become significantly more robust thanks to advancements in their underlying statistical and Deep Neural Network (DNN) techniques: most modern syntactic parsers can produce a syntactic parse tree for almost any sentence, including ones that may not be strictly grammatical. Despite improved robustness, such parsers still do not reflect the alternatives in parsing that are intrinsic in syntactic ambiguities. Two most notable such ambiguities are prepositional phrase (PP) attachment ambiguities and pronoun coreference ambiguities. In this paper, we discuss PatchComm, which uses commonsense knowledge to help resolve both kinds of ambiguities. To the best of our knowledge, we are the first to propose the general-purpose approach of using external commonsense knowledge bases to guide syntactic parsers. We evaluated PatchComm against the state-of-the-art (SOTA) spaCy parser on a PP attachment task and against the SOTA NeuralCoref module on a coreference task. Results show that PatchComm is successful at detecting syntactic ambiguities and using commonsense knowledge to help resolve them.

O sądach wartościujących w wykładni doktrynalnej sensu stricto w opisowym ujęciu pragmatycznym

The subject of the article is a description of reasoning that occurs in the doctrinal interpretation process, in which value judgments are used. The value judgments in doctrinal interpretation have been grouped according to the subject of the evaluation. Distinctions were made regarding the semantic ambiguities of the legal text, syntactic ambiguities of the legal text, and validation problems, filling the gap by using analogia legis.

Interaction with Context During Recurrent Neural Network Sentence Processing

Syntactic ambiguities in isolated sentences can lead to increased difficulty in incremental sentence processing, a phenomenon known as a garden-path effect. This difficulty, however, can be alleviated for humans when they are presented with supporting discourse contexts. We tested whether recurrent neural network (RNN) language models (LMs) could learn linguistic representations that are similarly influenced by discourse context. RNN LMs have been claimed to learn a variety of syntactic constructions. However, recent work has suggested that pragmatically conditioned syntactic phenomena are not acquired by RNNs. In comparing model behavior to human behavior, we show that our models can, in fact, learn pragmatic constraints that alleviate garden-path effects given the correct training and testing conditions. This suggests that some aspects of linguistically relevant pragmatic knowledge can be learned from distributional information alone.

The interaction between structure and meaning in sentence comprehension: Recurrent neural networks and reading times

Recurrent neural network (RNN) models of sentence processing have recently displayed a remarkable ability to learn aspects of structure comprehension, as evidenced by their ability to account for reading times on sentences with local syntactic ambiguities (i.e., garden-path effects). Here, we investigate if these models can also simulate the effect of semantic appropriateness of the ambiguity's readings. RNNs-based estimates of surprisal of the disambiguating verb of sentences with an NP/S-coordination ambiguity (as in `The wizard guards the king and the princess protects ...') show identical patters to human reading times on the same sentences: Surprisal is higher on ambiguous structures than on their disambiguated counterparts and this effect is weaker, but not absent, in cases of poor thematic fit between the verb and its potential object (`The teacher baked the cake and the baker made ...'). These results show that an RNN is able to simultaneously learn about structural and semantic relations between words and suggest that garden-path phenomena may be more closely related to word predictability than traditionally assumed.

The types of jokes due to syntactic ambiguity (Illustrated by the Vietnamese and English languages)

Jokes are very common in our lives - brief but exquisite and artistic. Vietnamese and English language have many jokes based on the using of ambiguous languages. The listeners/readers recognize ridiculous situations or event due to the "tools" of ambiguous language in combination with the knowledge and sensitivity of their language. Syntactic ambiguity is a kind of language ambiguity and occurs in sentences that have more than one meaning because their syntactical relationships can be distinguished in different ways. There are many kinds of syntactic ambiguities, but the paper mainly examines the kind of jokes due to the attachment and analytical ambiguity. This paper presents the characteristics of syntactic ambiguity as a "means" to make up the comedy of jokes in Vietnamese and English language; at the same time, it presents the similarities and differences between the kinds of jokes due to the syntactic ambiguity of Vietnamese and English people.

Lexico-syntactic interactions in the resolution of relative clause ambiguities in a second language (L2): The role of cognate status and L2 proficiency

There is extensive evidence showing that bilinguals activate lexical representations in a non-selective way both when words are presented in isolation and in sentence contexts. Recent research has shown the existence of cross-language activation at the syntactic level as well. However, the extent to which the lexical and syntactic levels of representation interact during second language (L2) sentence processing, and how these interactions are modulated by L2 proficiency remain unclear. In this paper, we explore how native speakers of European-Portuguese (L1) who are learning English as an L2 at different levels of proficiency (intermediate vs. advanced) resolve relative clause (RC) syntactic ambiguities in their L2. European Portuguese and English native speakers were used as controls. Participants were asked to perform a sentence completion task, with cognates and noncognates critically embedded in the complex noun phrase (NP) preceding the RC, and which contained its antecedent. Results revealed that L2 learners, like English controls, preferred to attach the RC to the last host of the complex NP, regardless of L2 proficiency. Importantly, the cognate status of the complex NP modulated the results, although, contrary to our expectation, the presence of cognates induced less L1 syntax interference compared to noncognates.

SPATIO-SEMANTIC COMPARISON OF LARGE 3D CITY MODELS IN CITYGML USING A GRAPH DATABASE

A city may have multiple CityGML documents recorded at different times or surveyed by different users. To analyse the city’s evolution over a given period of time, as well as to update or edit the city model without negating modifications made by other users, it is of utmost importance to first compare, detect and locate spatio-semantic changes between CityGML datasets. This is however difficult due to the fact that CityGML elements belong to a complex hierarchical structure containing multi-level deep associations, which can basically be considered as a graph. Moreover, CityGML allows multiple syntactic ways to define an object leading to syntactic ambiguities in the exchange format. Furthermore, CityGML is capable of including not only 3D urban objects’ graphical appearances but also their semantic properties. Since to date, no known algorithm is capable of detecting spatio-semantic changes in CityGML documents, a frequent approach is to replace the older models completely with the newer ones, which not only costs computational resources, but also loses track of collaborative and chronological changes. Thus, this research proposes an approach capable of comparing two arbitrarily large-sized CityGML documents on both semantic and geometric level. Detected deviations are then attached to their respective sources and can easily be retrieved on demand. As a result, updating a 3D city model using this approach is much more efficient as only real changes are committed. To achieve this, the research employs a graph database as the main data structure for storing and processing CityGML datasets in three major steps: mapping, matching and updating. The mapping process transforms input CityGML documents into respective graph representations. The matching process compares these graphs and attaches edit operations on the fly. Found changes can then be executed using the Web Feature Service (WFS), the standard interface for updating geographical features across the web.