Katrin Erk
Dados Biográficos
| ID | 1666253 |
|---|---|
| NOME | Katrin Erk |
| PRENOMES | Katrin |
| SOBRENOME | Erk |
| ASSINATURA | ERK K |
| AFILIAÇÕES | The University of Texas at Austin |
| ORCID | 0000-0002-2888-6961 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 9 |
| TOTAL DE CITAÇÕES | 26 |
| TOTAL COMO AUTOR | 9 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2010 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2023 |
| ÍNDICE H | 3 |
How to Marry a Star
In this paper, we derive a notion of word meaning in context that characterizes meaning as both intensional and conceptual. We introduce a framework for specifying local as well as global constraints on word meaning in context, together with their interactions, thus modelling a wide range of lexical shifts and ambiguities observed in utterance interpretation. We represent sentence meaning as a situation description system, a probabilistic model w…
Рецензия На Книгу
Professor György Kara, a distinguished member of academia, celebrated his 80th birthday recently. His students and colleagues commemorated this occasion with papers on Altaic Studies. The work, which consists of 24 articles, was edited by Ákos Bertalan Apatóczky and Christopher P. Atwood, and guest-edited by Béla Kempf. The main topics discussed in the work are Manuscripts-Texts Analyse, Sino-Mongol Glossaries, Middle Turkic, Middle Mongolian, Oi…
The Probabilistic Turn in Semantics and Pragmatics
This article provides an overview of graded and probabilistic approaches in semantics and pragmatics. These approaches share a common set of core research goals: ( a) a concern with phenomena that are best described as graded, including a vast lexicon of words whose meanings adapt flexibly to the contexts in which they are used, as well as reasoning under uncertainty about interlocutors, their goals, and their strategies; ( b) the need to show th…
What do you know about an alligator when you know the company it keeps
Distributional models describe the meaning of a word in terms of its observed contexts. They have been very successful in computational linguistics. They have also been suggested as a model for how humans acquire (partial) knowledge about word meanings. But that raises the question of what, exactly, distributional models can learn, and the question of how distributional information would interact with everything else that an agent knows. For the …
Representing Meaning with a Combination of Logical and Distributional Models
NLP tasks differ in the semantic information they require, and at this time no single semantic representation fulfills all requirements. Logic-based representations characterize sentence structure, but do not capture the graded aspect of meaning. Distributional models give graded similarity ratings for words and phrases, but do not capture sentence structure in the same detail as logic-based approaches. It has therefore been argued that the two a…
Word Sense Clustering and Clusterability
Word sense disambiguation and the related field of automated word sense induction traditionally assume that the occurrences of a lemma can be partitioned into senses. But this seems to be a much easier task for some lemmas than others. Our work builds on recent work that proposes describing word meaning in a graded fashion rather than through a strict partition into senses; in this article we argue that not all lemmas may need the more complex gr…
Measuring Word Meaning in Context
Word sense disambiguation (WSD) is an old and important task in computational linguistics that still remains challenging, to machines as well as to human annotators. Recently there have been several proposals for representing word meaning in context that diverge from the traditional use of a single best sense for each occurrence. They represent word meaning in context through multiple paraphrases, as points in vector space, or as distributions ov…
Vector Space Models of Word Meaning and Phrase Meaning
Distributional models represent a word through the contexts in which it has been observed. They can be used to predict similarity in meaning, based on the distributional hypothesis, which states that two words that occur in similar contexts tend to have similar meanings. Distributional approaches are often implemented in vector space models. They represent a word as a point in high-dimensional space, where each dimension stands for a context item…
A Flexible, Corpus-Driven Model of Regular and Inverse Selectional Preferences
We present a vector space–based model for selectional preferences that predicts plausibility scores for argument headwords. It does not require any lexical resources (such as WordNet). It can be trained either on one corpus with syntactic annotation, or on a combination of a small semantically annotated primary corpus and a large, syntactically analyzed generalization corpus. Our model is able to predict inverse selectional preferences, that is, …
Vector Space Models of Word Meaning and Phrase Meaning
Distributional models represent a word through the contexts in which it has been observed. They can be used to predict similarity in meaning, based on the distributional hypothesis, which states that two words that occur in similar contexts tend to have similar meanings. Distributional approaches are often implemented in vector space models. They represent a word as a point in high-dimensional space, where each dimension stands for a context item…
A Flexible, Corpus-Driven Model of Regular and Inverse Selectional Preferences
We present a vector space–based model for selectional preferences that predicts plausibility scores for argument headwords. It does not require any lexical resources (such as WordNet). It can be trained either on one corpus with syntactic annotation, or on a combination of a small semantically annotated primary corpus and a large, syntactically analyzed generalization corpus. Our model is able to predict inverse selectional preferences, that is, …
Word Sense Clustering and Clusterability
Word sense disambiguation and the related field of automated word sense induction traditionally assume that the occurrences of a lemma can be partitioned into senses. But this seems to be a much easier task for some lemmas than others. Our work builds on recent work that proposes describing word meaning in a graded fashion rather than through a strict partition into senses; in this article we argue that not all lemmas may need the more complex gr…
The Probabilistic Turn in Semantics and Pragmatics
This article provides an overview of graded and probabilistic approaches in semantics and pragmatics. These approaches share a common set of core research goals: ( a) a concern with phenomena that are best described as graded, including a vast lexicon of words whose meanings adapt flexibly to the contexts in which they are used, as well as reasoning under uncertainty about interlocutors, their goals, and their strategies; ( b) the need to show th…
Measuring Word Meaning in Context
Word sense disambiguation (WSD) is an old and important task in computational linguistics that still remains challenging, to machines as well as to human annotators. Recently there have been several proposals for representing word meaning in context that diverge from the traditional use of a single best sense for each occurrence. They represent word meaning in context through multiple paraphrases, as points in vector space, or as distributions ov…
A Flexible, Corpus-Driven Model of Regular and Inverse Selectional Preferences
We present a vector space–based model for selectional preferences that predicts plausibility scores for argument headwords. It does not require any lexical resources (such as WordNet). It can be trained either on one corpus with syntactic annotation, or on a combination of a small semantically annotated primary corpus and a large, syntactically analyzed generalization corpus. Our model is able to predict inverse selectional preferences, that is, …
Measuring Word Meaning in Context
Word sense disambiguation (WSD) is an old and important task in computational linguistics that still remains challenging, to machines as well as to human annotators. Recently there have been several proposals for representing word meaning in context that diverge from the traditional use of a single best sense for each occurrence. They represent word meaning in context through multiple paraphrases, as points in vector space, or as distributions ov…
Vector Space Models of Word Meaning and Phrase Meaning
Distributional models represent a word through the contexts in which it has been observed. They can be used to predict similarity in meaning, based on the distributional hypothesis, which states that two words that occur in similar contexts tend to have similar meanings. Distributional approaches are often implemented in vector space models. They represent a word as a point in high-dimensional space, where each dimension stands for a context item…
What do you know about an alligator when you know the company it keeps
Distributional models describe the meaning of a word in terms of its observed contexts. They have been very successful in computational linguistics. They have also been suggested as a model for how humans acquire (partial) knowledge about word meanings. But that raises the question of what, exactly, distributional models can learn, and the question of how distributional information would interact with everything else that an agent knows. For the …
Representing Meaning with a Combination of Logical and Distributional Models
NLP tasks differ in the semantic information they require, and at this time no single semantic representation fulfills all requirements. Logic-based representations characterize sentence structure, but do not capture the graded aspect of meaning. Distributional models give graded similarity ratings for words and phrases, but do not capture sentence structure in the same detail as logic-based approaches. It has therefore been argued that the two a…
Word Sense Clustering and Clusterability
Word sense disambiguation and the related field of automated word sense induction traditionally assume that the occurrences of a lemma can be partitioned into senses. But this seems to be a much easier task for some lemmas than others. Our work builds on recent work that proposes describing word meaning in a graded fashion rather than through a strict partition into senses; in this article we argue that not all lemmas may need the more complex gr…
The Probabilistic Turn in Semantics and Pragmatics
This article provides an overview of graded and probabilistic approaches in semantics and pragmatics. These approaches share a common set of core research goals: ( a) a concern with phenomena that are best described as graded, including a vast lexicon of words whose meanings adapt flexibly to the contexts in which they are used, as well as reasoning under uncertainty about interlocutors, their goals, and their strategies; ( b) the need to show th…
Рецензия На Книгу
Professor György Kara, a distinguished member of academia, celebrated his 80th birthday recently. His students and colleagues commemorated this occasion with papers on Altaic Studies. The work, which consists of 24 articles, was edited by Ákos Bertalan Apatóczky and Christopher P. Atwood, and guest-edited by Béla Kempf. The main topics discussed in the work are Manuscripts-Texts Analyse, Sino-Mongol Glossaries, Middle Turkic, Middle Mongolian, Oi…
How to Marry a Star
In this paper, we derive a notion of word meaning in context that characterizes meaning as both intensional and conceptual. We introduce a framework for specifying local as well as global constraints on word meaning in context, together with their interactions, thus modelling a wide range of lexical shifts and ambiguities observed in utterance interpretation. We represent sentence meaning as a situation description system, a probabilistic model w…
Computer Science (8 obras) · Natural language processing (8 obras) · Natural Language Processing Techniques (8 obras) · Topic Modeling (8 obras) · Artificial Intelligence (7 obras) · Linguistics (6 obras) · Speech and dialogue systems (5 obras) · Probabilistic logic (4 obras) · Semantic similarity (4 obras) · Distributional semantics (3 obras)