In this provocative book, Barwise and Perry tackle the slippery subject of \"meaning, \" a subject that has long vexed linguists, language philosophers, and logicians.

/pdf/situations-and-attitudes-57f065zg2d.pdf

Situations and Attitudes.

This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task provided 19 training and 20 testing datasets for 8 languages and 7 domains, as well as a common evaluation procedure. From these datasets, 25 were for sentence-level and 14 for text-level ABSA; the latter was introduced for the first time as a subtask in SemEval. The task attracted 245 submissions from 29 teams.

https://hal.archives-ouvertes.fr/hal-01838537/document

SemEval-2016 task 5 : aspect based sentiment analysis

This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.

/pdf/semeval-2017-task-4-sentiment-analysis-in-twitter-5294l6x1f7.pdf

SemEval-2017 Task 4: Sentiment Analysis in Twitter

From the Publisher:
"Covering the latest issues and achievements, this well documented, precisely presented text is timely and suitable for graduate and upper undergraduate students in knowledge engineering, intelligent systems, AI, neural networks, fuzzy systems, and related areas. The author's goal is to explain the principles of neural networks and fuzzy systems and to demonstrate how they can be applied to building knowledge-based systems for problem solving. Especially useful are the comparisons between different techniques (AI rule-based methods, fuzzy methods, connectionist methods, hybrid systems) used to solve the same or similar problems."
-- Anca Ralescu, Associate Professor of Computer Science, University of Cincinnati Neural networks and fuzzy systems are different approaches to introducing human-like reasoning into expert systems. This text is the first to combine the study of these two subjects, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems. 
In a clear and accessible style, Kasabov describes rule- based and connectionist techniques and then their combinations, with fuzzy logic included, showing the application of the different techniques to a set of simple prototype problems, which makes comparisons possible. A particularly strong feature of the text is that it is filled with applications in engineering, business, and finance. AI problems that cover most of the application-oriented research in the field (pattern recognition, speech and image processing, classification, planning, optimization, prediction, control, decision making, and game simulations) are discussed and illustrated with concrete examples.
Intended both as a text for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering has chapters structured for various levels of teaching and includes original work by the author along with the classic material. 
Data sets for the examples in the book as well as an integrated software environment that can be used to solve the problems and do the exercises at the end of each chapter are available free through anonymous ftp.

Foundations of neural networks, fuzzy systems, and knowledge engineering

We describe an integrated theory of analogical access and mapping, instantiated in a computational model called LISA (Learning and Inference with Schemas and Analogies). LISA represents predicates and objects as distributed patterns of activation over units representing semantic primitives. These representations are dynamically bound into propositional structures, thereby achieving the structure-sensitivity of a symbolic system and the flexibility of a connectionist system. LISA also has a number of inherent limitations, including capacity limits and sensitivity to the manner in which a problem is represented. A key theoretical claim is that similar limitations also arise in human reasoning, suggesting that the architecture of LISA can provide computational explanations of properties of the human cognitive architecture. We report LISA's performance in simulating a wide range of empirical phenomena concerning human analogical access and mapping. The model treats both access and mapping as types of guided pattern classification, differing only in that mapping is augmented by a capacity to learn new correspondences. Extensions of the approach to account for analogical inference and schema induction are also discussed.

https://internal.psychology.illinois.edu/~jehummel/pubs/LISA1.pdf

Distributed representations of structure: A theory of analogical access and mapping.

This report summarizes the objectives and evaluation of the SemEval 2015 task on the sentiment analysis of figurative language on Twitter (Task 11). This is the first sentiment analysis task wholly dedicated to analyzing figurative language on Twitter. Specifically, three broad classes of figurative language are considered: irony, sarcasm and metaphor. Gold standard sets of 8000 training tweets and 4000 test tweets were annotated using workers on the crowdsourcing platform CrowdFlower. Participating systems were required to provide a fine-grained sentiment score on an 11-point scale (-5 to +5, including 0 for neutral intent) for each tweet, and systems were evaluated against the gold standard using both a Cosinesimilarity and a Mean-Squared-Error measure.

/pdf/semeval-2015-task-11-sentiment-analysis-of-figurative-3lv4em4fgn.pdf

SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter

Abstract This paper continues the debate about how to distinguish metaphor from metonymy, and whether this can be done. It examines some of the differences that have been alleged to exist, and augments the already existing doubt about them. The main differences addressed are the similarity/contiguity distinction and the issue of whether source-target links are part of the message in metonymy or metaphor. In particular, the paper argues that metaphorical links can always be used metonymically and regarded as contiguities, and conversely that two particular, central types of metonymic contiguity essentially involve similarity. The paper also touches briefly on how metaphor and metonymy interact with domains, frames, etc. and on the role of imaginary identification/categorization of target as/under source items. With the possible exception of this last issue, the paper suggests that no combination of the alleged differences addressed can serve cleanly to categorize source/target associations into metaphorical ones and metonymic ones. It also suggests that it can be more profitable to analyse utterances at the level of the dimensions involved in the differences than at the higher level of metaphor and metonymy as such.

/pdf/metaphor-and-metonymy-making-their-connections-more-slippery-1yrtmyx6l4.pdf

Metaphor and metonymy: Making their connections more slippery

This article reports a study on metaphor comprehension by the international students whose first language is not English, while attending undergraduate lectures at a British university. Study participants identified words or multiword items that they found difficult in extracts from four academic lectures, and they interpreted metaphors from those extracts. Among the items reported as difficult, we established the proportion of metaphorical items, plus the proportion of items composed only of words familiar to the students. We developed a measure of the extent of students’ awareness of their metaphor interpretation difficulties, plus a scheme for categorizing the most common types of metaphor misinterpretations. We found that, of the items that were difficult though composed of familiar words, ∼40 per cent involved metaphor. Further, when the students misinterpreted metaphors, they only seemed aware of having difficulty in ∼4 per cent of cases. As university lecturers use metaphors for important functions, such as explaining and evaluating, such international students may thus be missing valuable learning opportunities. Our error categorization scheme could be used in helping English learners with metaphor comprehension.

Difficulties in Metaphor Comprehension Faced by International Students whose First Language is not English

Two general information encoding techniques called relative position encoding and pattern similarity association are presented. They are claimed to be a convenient basis for the connectionist implementation of complex, short term information processing of the sort needed in common sense reasoning, semantic/pragmatic interpretation of natural language utterances, and other types of high level cognitive processing. The relationships of the techniques to other connectionist information-structuring methods, and also to methods used in computers, are discussed in detail. The rich inter-relationships of these other connectionist and computer methods are also clarified. The particular, simple forms are discussed that the relative position encoding and pattern similarity association techniques take in the author's own connectionist system, called Conposit, in order to clarify some issues and to provide evidence that the techniques are indeed useful in practice.

/pdf/encoding-techniques-for-complex-information-structures-in-1bq01cwp7i.pdf

Encoding techniques for complex information structures in connectionist systems

https://www.cs.nmsu.edu/~rth/publications/TICS.pdf

John A. Barnden

Papers

SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter

Metaphor and metonymy: Making their connections more slippery

Difficulties in Metaphor Comprehension Faced by International Students whose First Language is not English

Encoding techniques for complex information structures in connectionist systems

Semantic networks: visualizations of knowledge