One of the major causes of search failures in information retrieval systems is vocabulary mismatch. This paper presents a solution to the vocabulary problem through two strategies known as term suggestion (TS) and term relevance feedback (TRF). In TS, collection-specific terms are extracted from the text collection. These terms and their frequencies constitute the keyword database for suggesting terms in response to users' queries. One effect of this term suggestion is that it functions as a dynamic directory if the query is a general term that contains broad meaning. In term relevance feedback, terms extracted from the top-ranked documents retrieved from the previous query are shown to users for relevance feedback. This kind of local TRF expands users' search vocabularies and guides users in search directions closer to their goals. In our experiment, interactive TS provides very high precision rate while achieving similar recall rate as n-gram matching. Local TRF achieves improvement in both precision and recall rate in full-text News database and degrades slightly in recall rate in bibliographic database due to the very limited source of information for feedback. In terms of Rijsbergen's combined measure of recall and precision, both TS and TRF achieve better performance than n-gram matching, which implies that the greater improvement in precision rate compensates the slightly degradation in recall rate for TS and TRF. We conclude that both TS and TRF provide users with richer guidance and more predictable results than n-gram matching alone.