scholarly journals Personality Predictive Lexical Cues and Their Correlations

2021 ◽  
Author(s):  
Xiaoli He ◽  
◽  
Gerard de Melo ◽  
Keyword(s):  
Lexical Cues

scholarly journals Acoustic-Prosodic and Lexical Cues to Deception and Trust: Deciphering How People Detect Lies

2020 ◽  
Vol 8 ◽  
pp. 199-214
Author(s):  
Xi (Leslie) Chen ◽  
Sarah Ita Levitan ◽  
Michelle Levine ◽  
Marko Mandic ◽  
Julia Hirschberg
Keyword(s):  
Deception Detection ◽  
Human Perception ◽  
Lie Detection ◽  
Challenging Problem ◽  
Cues To Deception ◽  
Lexical Cues ◽  
Insight Into ◽  
Large Corpus ◽  
Better Than

Humans rarely perform better than chance at lie detection. To better understand human perception of deception, we created a game framework, LieCatcher, to collect ratings of perceived deception using a large corpus of deceptive and truthful interviews. We analyzed the acoustic-prosodic and linguistic characteristics of language trusted and mistrusted by raters and compared these to characteristics of actual truthful and deceptive language to understand how perception aligns with reality. With this data we built classifiers to automatically distinguish trusted from mistrusted speech, achieving an F1 of 66.1%. We next evaluated whether the strategies raters said they used to discriminate between truthful and deceptive responses were in fact useful. Our results show that, although several prosodic and lexical features were consistently perceived as trustworthy, they were not reliable cues. Also, the strategies that judges reported using in deception detection were not helpful for the task. Our work sheds light on the nature of trusted language and provides insight into the challenging problem of human deception detection.

scholarly journals Children do not overcome lexical biases where adults do: the role of the referential scene in garden-path recovery

2010 ◽  
Vol 38 (1) ◽  
pp. 222-234 ◽  
Author(s):  
EVAN KIDD ◽  
ANDREW J. STEWART ◽  
LUDOVICA SERRATRICE
Keyword(s):  
Eye Tracking ◽  
Sentence Processing ◽  
Test Sentence ◽  
Visual World ◽  
Garden Path ◽  
Initial Analysis ◽  
Adults And Children ◽  
Scene Information ◽  
Lexical Cues

ABSTRACTIn this paper we report on a visual world eye-tracking experiment that investigated the differing abilities of adults and children to use referential scene information during reanalysis to overcome lexical biases during sentence processing. The results showed that adults incorporated aspects of the referential scene into their parse as soon as it became apparent that a test sentence was syntactically ambiguous, suggesting they considered the two alternative analyses in parallel. In contrast, the children appeared not to reanalyze their initial analysis, even over shorter distances than have been investigated in prior research. We argue that this reflects the children's over-reliance on bottom-up, lexical cues to interpretation. The implications for the development of parsing routines are discussed.

Targetive Cues to a Target

2018 ◽  
Author(s):  
Leonard Talmy
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Sensory Stimuli ◽  
Term Memory ◽  
Intended Target ◽  
Feature Search ◽  
Lexical Cues

Targetive cues are properties exhibited by a speaker’s intended target itself. They help guide the hearer toward singling that target out. Where the target is speech-external, the targetive cues are sensory stimuli that the target produces and that the hearer perceives. Where the target is speech-internal, the targetive cues are phonetic, syntactic, semantic, or pragmatic properties of the target that the hearer either perceives or apprehends and then holds in short-term memory. There are three types of targetive cues, and the character of the lexical cues in the speaker’s utterance determines which one is relevant. In one case, the lexical cues ascribe features to the target that are definitive for its determination. They evoke in the hearer a category that is effective for recognizing instances of the target. The hearer under-takes a feature search over her perceivable environment to find the corresponding targetive feature cues.

Using Multi-Document Summarization to Facilitate Semi-Structured Literature Retrieval

2005 ◽  
pp. 111-128
Author(s):  
Min-Yen Kan
Keyword(s):  
User Interface ◽  
Digital Library ◽  
Digital Libraries ◽  
Information Seeking ◽  
Document Summarization ◽  
Consumer Healthcare ◽  
Information Professionals ◽  
Lexical Cues ◽  
Ranked List

This chapter examines the techniques behind a user interface that computes a multi-document summary of documents retrieved by a search. As a user’s query can retrieve thousands of relevant documents, it is paramount that they be logically organized. In digital libraries, documents are traditionally represented as a ranked list of documents ordered by computed relevance and do not take into account presentation techniques used by information professionals (such as librarians) in the physical library. This chapter examines a framework used in a consumer healthcare digital library that incorporates techniques used by librarians. It brings together commonalities between documents and highlights their salient differences to target the needs of users using the browsing and searching modes of information seeking. It achieves this by discovering common and unique topics among its input from a combination of structural and lexical cues.

Adult learners’ use of lexical cues in the acquisition of L2 allophones

2018 ◽  
Vol 144 (3) ◽  
pp. 1865-1865
Author(s):  
Shannon L. Barrios ◽  
Joselyn Rodriguez
Keyword(s):  
Adult Learners ◽  
Lexical Cues

scholarly journals Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation

2001 ◽  
Vol 27 (1) ◽  
pp. 31-57 ◽  
Author(s):  
Gökhan Tür ◽  
Dilek Hakkani-Tür ◽  
Andreas Stolcke ◽  
Elizabeth Shriberg
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Automatic Segmentation ◽  
Prosodic Features ◽  
Lexical Information ◽  
Topic Segmentation ◽  
Segmentation Methods ◽  
News Corpus ◽  
Speech Recognizer ◽  
Lexical Cues

We present a probabilistic model that uses both prosodic and lexical cues for the automatic segmentation of speech into topically coherent units. We propose two methods for combining lexical and prosodic information using hidden Markov models and decision trees. Lexical information is obtained from a speech recognizer, and prosodic features are extracted automatically from speech waveforms. We evaluate our approach on the Broadcast News corpus, using the DARPA-TDT evaluation metrics. Results show that the prosodic model alone is competitive with word-based segmentation methods. Furthermore, we achieve a significant reduction in error by combining the prosodic and word-based knowledge sources.

PARSING STRATEGIES IN L1 AND L2 SENTENCE PROCESSING

2003 ◽  
Vol 25 (4) ◽  
pp. 501-528 ◽  
Author(s):  
Despina Papadopoulou ◽  
Harald Clahsen
Keyword(s):  
Sentence Processing ◽  
Relative Clause ◽  
Native Speakers ◽  
Control Group ◽  
Native Languages ◽  
Advanced Learners ◽  
L2 Learners ◽  
On Line ◽  
L1 And L2 ◽  
Lexical Cues

To contribute to a better understanding of second language (L2) sentence processing, the present study examines how L2 learners parse temporarily ambiguous sentences containing relative clauses. Results are reported from both off-line and on-line experiments with three groups of advanced learners of Greek whose native languages (L1s) were Spanish, German, or Russian as well as from corresponding experiments with a control group of adult native speakers of Greek. We found that, despite their nativelike mastery of the construction under investigation, the L2 learners showed relative-clause attachment preferences that were different from those of the native speakers. Moreover, the L2 learners did not exhibit L1-based preferences in their L2 Greek, as might be expected if they were directly influenced by L1 attachment preferences. We suggest that L2 learners integrate information relevant for parsing differently from native speakers, with the L2 learners relying more on lexical cues than the native speakers and less on purely structurally based parsing strategies.

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