Chasing Hypernyms in Vector Spaces with Entropy

In this paper, we introduce SLQS, a new entropy-based measure for the unsupervised identification of hypernymy and its directionality in Distributional Semantic Models (DSMs). SLQS is assessed through two tasks: (i.) identifying the hypernym in hyponym-hypernym pairs, and (ii.) discriminating hypernymy among various semantic relations. In both tasks, SLQS outperforms other state-of-the-art measures

Characterizing response types and revealing noun ambiguity in German association norms

This paper presents an analysis of semantic association norms for German nouns. In contrast to prior studies, we not only collected associations elicited by written representations of target objects but also by their pictorial representations. In a first analysis, we identified systematic differences in the type and distribution of associate responses for the two presentation forms. In a second analysis, we applied a soft cluster analysis to the collected target-response pairs. We subsequently used the clustering to predict noun ambiguity and to discriminate senses in our target nouns

Non-Compositional Term Dependence for Information Retrieval

Modelling term dependence in IR aims to identify co-occurring terms that are too heavily dependent on each other to be treated as a bag of words, and to adapt the indexing and ranking accordingly. Dependent terms are predominantly identified using lexical frequency statistics, assuming that (a) if terms co-occur often enough in some corpus, they are semantically dependent; (b) the more often they co-occur, the more semantically dependent they are. This assumption is not always correct: the frequency of co-occurring terms can be separate from the strength of their semantic dependence. E.g. "red tape" might be overall less frequent than "tape measure" in some corpus, but this does not mean that "red"+"tape" are less dependent than "tape"+"measure". This is especially the case for non-compositional phrases, i.e. phrases whose meaning cannot be composed from the individual meanings of their terms (such as the phrase "red tape" meaning bureaucracy). Motivated by this lack of distinction between the frequency and strength of term dependence in IR, we present a principled approach for handling term dependence in queries, using both lexical frequency and semantic evidence. We focus on non-compositional phrases, extending a recent unsupervised model for their detection [21] to IR. Our approach, integrated into ranking using Markov Random Fields [31], yields effectiveness gains over competitive TREC baselines, showing that there is still room for improvement in the very well-studied area of term dependence in IR

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

German particle verbs : compositionality at the syntax-semantics interface

Particle verbs represent a type of multi-word expression composed of a base verb and a particle. The meaning of the particle verb is often, but not always, derived from the meaning of the base verb, sometimes in quite complex ways. In this work, we computationally assess the levels of German particle verb compositionality by applying distributional semantic models. Furthermore, we investigate properties of German particle verbs at the syntax-semantics interface that influence their degrees of compositionality: (i) regularity in semantic particle verb derivation and (ii) transfer of syntactic subcategorization from base verbs to particle verbs. Our distributional models show that both superficial window co-occurrence models as well as theoretically well-founded syntactic models are sensitive to subcategorization frame transfer and can be used to predict degrees of particle verb compositionality, with window models performing better even though they are conceptually and computationally simpler

Exploiting ''Subjective'' Annotations

Many interesting phenomena in conversation can only be annotated as a subjective task, requiring interpretative judgements from annotators. This leads to data which is annotated with lower levels of agreement not only due to errors in the annotation, but also due to the differences in how annotators interpret conversations. This paper constitutes an attempt to find out how subjective annotations with a low level of agreement can profitably be used for machine learning purposes. We analyse the (dis)agreements between annotators for two different cases in a multimodal annotated corpus and explicitly relate the results to the way machine-learning algorithms perform on the annotated data. Finally we present two new concepts, namely `subjective entity' classifiers resp. `consensus objective' classifiers, and give recommendations for using subjective data in machine-learning applications

BabyExp: Constructing a huge multimodal resource to acquire commonsense knowledge like children do

The BabyExp project is collecting very dense audio and video recordings of the first 3 years of life of a baby. The corpus constructed in this way will be transcribed with automated techniques and made available to the research community. Moreover, techniques to extract commonsense conceptual knowledge incrementally from these multimodal data are also being explored within the project. The current paper describes BabyExp in general, and presents pilot studies on the feasability of the automated audio and video transcriptions

BabyExp: Constructing a huge multimodal resource to acquire commonsense knowledge like children do

The BabyExp project is collecting very dense audio and video recordings of the first 3 years of life of a baby. The corpus constructed in this way will be transcribed with automated techniques and made available to the research community. Moreover, techniques to extract commonsense conceptual knowledge incrementally from these multimodal data are also being explored within the project. The current paper describes BabyExp in general, and presents pilot studies on the feasibility of the automated audio and video transcriptions